WorldWideScience

Sample records for pairwise probability models

  1. Statistical physics of pairwise probability models

    Directory of Open Access Journals (Sweden)

    Yasser Roudi

    2009-11-01

    Full Text Available Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of data: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying and using pairwise models. We build on our previous work on the subject and study the relation between different methods for fitting these models and evaluating their quality. In particular, using data from simulated cortical networks we study how the quality of various approximate methods for inferring the parameters in a pairwise model depends on the time bin chosen for binning the data. We also study the effect of the size of the time bin on the model quality itself, again using simulated data. We show that using finer time bins increases the quality of the pairwise model. We offer new ways of deriving the expressions reported in our previous work for assessing the quality of pairwise models.

  2. Statistical physics of pairwise probability models

    DEFF Research Database (Denmark)

    Roudi, Yasser; Aurell, Erik; Hertz, John

    2009-01-01

    (dansk abstrakt findes ikke) Statistical models for describing the probability distribution over the states of biological systems are commonly used for dimensional reduction. Among these models, pairwise models are very attractive in part because they can be fit using a reasonable amount of  data......: knowledge of the means and correlations between pairs of elements in the system is sufficient. Not surprisingly, then, using pairwise models for studying neural data has been the focus of many studies in recent years. In this paper, we describe how tools from statistical physics can be employed for studying...

  3. Inferring Pairwise Interactions from Biological Data Using Maximum-Entropy Probability Models.

    Directory of Open Access Journals (Sweden)

    Richard R Stein

    2015-07-01

    Full Text Available Maximum entropy-based inference methods have been successfully used to infer direct interactions from biological datasets such as gene expression data or sequence ensembles. Here, we review undirected pairwise maximum-entropy probability models in two categories of data types, those with continuous and categorical random variables. As a concrete example, we present recently developed inference methods from the field of protein contact prediction and show that a basic set of assumptions leads to similar solution strategies for inferring the model parameters in both variable types. These parameters reflect interactive couplings between observables, which can be used to predict global properties of the biological system. Such methods are applicable to the important problems of protein 3-D structure prediction and association of gene-gene networks, and they enable potential applications to the analysis of gene alteration patterns and to protein design.

  4. Pairwise contact energy statistical potentials can help to find probability of point mutations.

    Science.gov (United States)

    Saravanan, K M; Suvaithenamudhan, S; Parthasarathy, S; Selvaraj, S

    2017-01-01

    To adopt a particular fold, a protein requires several interactions between its amino acid residues. The energetic contribution of these residue-residue interactions can be approximated by extracting statistical potentials from known high resolution structures. Several methods based on statistical potentials extracted from unrelated proteins are found to make a better prediction of probability of point mutations. We postulate that the statistical potentials extracted from known structures of similar folds with varying sequence identity can be a powerful tool to examine probability of point mutation. By keeping this in mind, we have derived pairwise residue and atomic contact energy potentials for the different functional families that adopt the (α/β) 8 TIM-Barrel fold. We carried out computational point mutations at various conserved residue positions in yeast Triose phosphate isomerase enzyme for which experimental results are already reported. We have also performed molecular dynamics simulations on a subset of point mutants to make a comparative study. The difference in pairwise residue and atomic contact energy of wildtype and various point mutations reveals probability of mutations at a particular position. Interestingly, we found that our computational prediction agrees with the experimental studies of Silverman et al. (Proc Natl Acad Sci 2001;98:3092-3097) and perform better prediction than i Mutant and Cologne University Protein Stability Analysis Tool. The present work thus suggests deriving pairwise contact energy potentials and molecular dynamics simulations of functionally important folds could help us to predict probability of point mutations which may ultimately reduce the time and cost of mutation experiments. Proteins 2016; 85:54-64. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  5. Probing dark energy models with extreme pairwise velocities of galaxy clusters from the DEUS-FUR simulations

    Science.gov (United States)

    Bouillot, Vincent R.; Alimi, Jean-Michel; Corasaniti, Pier-Stefano; Rasera, Yann

    2015-06-01

    Observations of colliding galaxy clusters with high relative velocity probe the tail of the halo pairwise velocity distribution with the potential of providing a powerful test of cosmology. As an example it has been argued that the discovery of the Bullet Cluster challenges standard Λ cold dark matter (ΛCDM) model predictions. Halo catalogues from N-body simulations have been used to estimate the probability of Bullet-like clusters. However, due to simulation volume effects previous studies had to rely on a Gaussian extrapolation of the pairwise velocity distribution to high velocities. Here, we perform a detail analysis using the halo catalogues from the Dark Energy Universe Simulation Full Universe Runs (DEUS-FUR), which enables us to resolve the high-velocity tail of the distribution and study its dependence on the halo mass definition, redshift and cosmology. Building upon these results, we estimate the probability of Bullet-like systems in the framework of Extreme Value Statistics. We show that the tail of extreme pairwise velocities significantly deviates from that of a Gaussian, moreover it carries an imprint of the underlying cosmology. We find the Bullet Cluster probability to be two orders of magnitude larger than previous estimates, thus easing the tension with the ΛCDM model. Finally, the comparison of the inferred probabilities for the different DEUS-FUR cosmologies suggests that observations of extreme interacting clusters can provide constraints on dark energy models complementary to standard cosmological tests.

  6. A configuration space of homologous proteins conserving mutual information and allowing a phylogeny inference based on pair-wise Z-score probabilities.

    Science.gov (United States)

    Bastien, Olivier; Ortet, Philippe; Roy, Sylvaine; Maréchal, Eric

    2005-03-10

    Popular methods to reconstruct molecular phylogenies are based on multiple sequence alignments, in which addition or removal of data may change the resulting tree topology. We have sought a representation of homologous proteins that would conserve the information of pair-wise sequence alignments, respect probabilistic properties of Z-scores (Monte Carlo methods applied to pair-wise comparisons) and be the basis for a novel method of consistent and stable phylogenetic reconstruction. We have built up a spatial representation of protein sequences using concepts from particle physics (configuration space) and respecting a frame of constraints deduced from pair-wise alignment score properties in information theory. The obtained configuration space of homologous proteins (CSHP) allows the representation of real and shuffled sequences, and thereupon an expression of the TULIP theorem for Z-score probabilities. Based on the CSHP, we propose a phylogeny reconstruction using Z-scores. Deduced trees, called TULIP trees, are consistent with multiple-alignment based trees. Furthermore, the TULIP tree reconstruction method provides a solution for some previously reported incongruent results, such as the apicomplexan enolase phylogeny. The CSHP is a unified model that conserves mutual information between proteins in the way physical models conserve energy. Applications include the reconstruction of evolutionary consistent and robust trees, the topology of which is based on a spatial representation that is not reordered after addition or removal of sequences. The CSHP and its assigned phylogenetic topology, provide a powerful and easily updated representation for massive pair-wise genome comparisons based on Z-score computations.

  7. A configuration space of homologous proteins conserving mutual information and allowing a phylogeny inference based on pair-wise Z-score probabilities

    Directory of Open Access Journals (Sweden)

    Maréchal Eric

    2005-03-01

    Full Text Available Abstract Background Popular methods to reconstruct molecular phylogenies are based on multiple sequence alignments, in which addition or removal of data may change the resulting tree topology. We have sought a representation of homologous proteins that would conserve the information of pair-wise sequence alignments, respect probabilistic properties of Z-scores (Monte Carlo methods applied to pair-wise comparisons and be the basis for a novel method of consistent and stable phylogenetic reconstruction. Results We have built up a spatial representation of protein sequences using concepts from particle physics (configuration space and respecting a frame of constraints deduced from pair-wise alignment score properties in information theory. The obtained configuration space of homologous proteins (CSHP allows the representation of real and shuffled sequences, and thereupon an expression of the TULIP theorem for Z-score probabilities. Based on the CSHP, we propose a phylogeny reconstruction using Z-scores. Deduced trees, called TULIP trees, are consistent with multiple-alignment based trees. Furthermore, the TULIP tree reconstruction method provides a solution for some previously reported incongruent results, such as the apicomplexan enolase phylogeny. Conclusion The CSHP is a unified model that conserves mutual information between proteins in the way physical models conserve energy. Applications include the reconstruction of evolutionary consistent and robust trees, the topology of which is based on a spatial representation that is not reordered after addition or removal of sequences. The CSHP and its assigned phylogenetic topology, provide a powerful and easily updated representation for massive pair-wise genome comparisons based on Z-score computations.

  8. BETASCAN: probable beta-amyloids identified by pairwise probabilistic analysis.

    Directory of Open Access Journals (Sweden)

    Allen W Bryan

    2009-03-01

    Full Text Available Amyloids and prion proteins are clinically and biologically important beta-structures, whose supersecondary structures are difficult to determine by standard experimental or computational means. In addition, significant conformational heterogeneity is known or suspected to exist in many amyloid fibrils. Recent work has indicated the utility of pairwise probabilistic statistics in beta-structure prediction. We develop here a new strategy for beta-structure prediction, emphasizing the determination of beta-strands and pairs of beta-strands as fundamental units of beta-structure. Our program, BETASCAN, calculates likelihood scores for potential beta-strands and strand-pairs based on correlations observed in parallel beta-sheets. The program then determines the strands and pairs with the greatest local likelihood for all of the sequence's potential beta-structures. BETASCAN suggests multiple alternate folding patterns and assigns relative a priori probabilities based solely on amino acid sequence, probability tables, and pre-chosen parameters. The algorithm compares favorably with the results of previous algorithms (BETAPRO, PASTA, SALSA, TANGO, and Zyggregator in beta-structure prediction and amyloid propensity prediction. Accurate prediction is demonstrated for experimentally determined amyloid beta-structures, for a set of known beta-aggregates, and for the parallel beta-strands of beta-helices, amyloid-like globular proteins. BETASCAN is able both to detect beta-strands with higher sensitivity and to detect the edges of beta-strands in a richly beta-like sequence. For two proteins (Abeta and Het-s, there exist multiple sets of experimental data implying contradictory structures; BETASCAN is able to detect each competing structure as a potential structure variant. The ability to correlate multiple alternate beta-structures to experiment opens the possibility of computational investigation of prion strains and structural heterogeneity of amyloid

  9. A predictive model of music preference using pairwise comparisons

    DEFF Research Database (Denmark)

    Jensen, Bjørn Sand; Gallego, Javier Saez; Larsen, Jan

    2012-01-01

    Music recommendation is an important aspect of many streaming services and multi-media systems, however, it is typically based on so-called collaborative filtering methods. In this paper we consider the recommendation task from a personal viewpoint and examine to which degree music preference can...... be elicited and predicted using simple and robust queries such as pairwise comparisons. We propose to model - and in turn predict - the pairwise music preference using a very flexible model based on Gaussian Process priors for which we describe the required inference. We further propose a specific covariance...

  10. Unjamming in models with analytic pairwise potentials

    Science.gov (United States)

    Kooij, Stefan; Lerner, Edan

    2017-06-01

    Canonical models for studying the unjamming scenario in systems of soft repulsive particles assume pairwise potentials with a sharp cutoff in the interaction range. The sharp cutoff renders the potential nonanalytic but makes it possible to describe many properties of the solid in terms of the coordination number z , which has an unambiguous definition in these cases. Pairwise potentials without a sharp cutoff in the interaction range have not been studied in this context, but should in fact be considered to understand the relevance of the unjamming phenomenology in systems where such a cutoff is not present. In this work we explore two systems with such interactions: an inverse power law and an exponentially decaying pairwise potential, with the control parameters being the exponent (of the inverse power law) for the former and the number density for the latter. Both systems are shown to exhibit the characteristic features of the unjamming transition, among which are the vanishing of the shear-to-bulk modulus ratio and the emergence of an excess of low-frequency vibrational modes. We establish a relation between the pressure-to-bulk modulus ratio and the distance to unjamming in each of our model systems. This allows us to predict the dependence of other key observables on the distance to unjamming. Our results provide the means for a quantitative estimation of the proximity of generic glass-forming models to the unjamming transition in the absence of a clear-cut definition of the coordination number and highlight the general irrelevance of nonaffine contributions to the bulk modulus.

  11. Unjamming in models with analytic pairwise potentials

    NARCIS (Netherlands)

    Kooij, S.; Lerner, E.

    Canonical models for studying the unjamming scenario in systems of soft repulsive particles assume pairwise potentials with a sharp cutoff in the interaction range. The sharp cutoff renders the potential nonanalytic but makes it possible to describe many properties of the solid in terms of the

  12. Solution to urn models of pairwise interaction with application to social, physical, and biological sciences

    Science.gov (United States)

    Pickering, William; Lim, Chjan

    2017-07-01

    We investigate a family of urn models that correspond to one-dimensional random walks with quadratic transition probabilities that have highly diverse applications. Well-known instances of these two-urn models are the Ehrenfest model of molecular diffusion, the voter model of social influence, and the Moran model of population genetics. We also provide a generating function method for diagonalizing the corresponding transition matrix that is valid if and only if the underlying mean density satisfies a linear differential equation and express the eigenvector components as terms of ordinary hypergeometric functions. The nature of the models lead to a natural extension to interaction between agents in a general network topology. We analyze the dynamics on uncorrelated heterogeneous degree sequence networks and relate the convergence times to the moments of the degree sequences for various pairwise interaction mechanisms.

  13. Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures.

    Science.gov (United States)

    Filippi, Sarah; Holmes, Chris C; Nieto-Barajas, Luis E

    2016-11-16

    In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a "null model" of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets.

  14. Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models.

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    Rostami, Vahid; Porta Mana, PierGianLuca; Grün, Sonja; Helias, Moritz

    2017-10-01

    Pairwise maximum-entropy models have been used in neuroscience to predict the activity of neuronal populations, given only the time-averaged correlations of the neuron activities. This paper provides evidence that the pairwise model, applied to experimental recordings, would produce a bimodal distribution for the population-averaged activity, and for some population sizes the second mode would peak at high activities, that experimentally would be equivalent to 90% of the neuron population active within time-windows of few milliseconds. Several problems are connected with this bimodality: 1. The presence of the high-activity mode is unrealistic in view of observed neuronal activity and on neurobiological grounds. 2. Boltzmann learning becomes non-ergodic, hence the pairwise maximum-entropy distribution cannot be found: in fact, Boltzmann learning would produce an incorrect distribution; similarly, common variants of mean-field approximations also produce an incorrect distribution. 3. The Glauber dynamics associated with the model is unrealistically bistable and cannot be used to generate realistic surrogate data. This bimodality problem is first demonstrated for an experimental dataset from 159 neurons in the motor cortex of macaque monkey. Evidence is then provided that this problem affects typical neural recordings of population sizes of a couple of hundreds or more neurons. The cause of the bimodality problem is identified as the inability of standard maximum-entropy distributions with a uniform reference measure to model neuronal inhibition. To eliminate this problem a modified maximum-entropy model is presented, which reflects a basic effect of inhibition in the form of a simple but non-uniform reference measure. This model does not lead to unrealistic bimodalities, can be found with Boltzmann learning, and has an associated Glauber dynamics which incorporates a minimal asymmetric inhibition.

  15. Metabolic network prediction through pairwise rational kernels.

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    Roche-Lima, Abiel; Domaratzki, Michael; Fristensky, Brian

    2014-09-26

    Metabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations. We create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy

  16. Multiclass Posterior Probability Twin SVM for Motor Imagery EEG Classification.

    Science.gov (United States)

    She, Qingshan; Ma, Yuliang; Meng, Ming; Luo, Zhizeng

    2015-01-01

    Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt's estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively.

  17. Pseudo inputs for pairwise learning with Gaussian processes

    DEFF Research Database (Denmark)

    Nielsen, Jens Brehm; Jensen, Bjørn Sand; Larsen, Jan

    2012-01-01

    We consider learning and prediction of pairwise comparisons between instances. The problem is motivated from a perceptual view point, where pairwise comparisons serve as an effective and extensively used paradigm. A state-of-the-art method for modeling pairwise data in high dimensional domains...... is based on a classical pairwise probit likelihood imposed with a Gaussian process prior. While extremely flexible, this non-parametric method struggles with an inconvenient O(n3) scaling in terms of the n input instances which limits the method only to smaller problems. To overcome this, we derive...... to other similar approximations that have been applied in standard Gaussian process regression and classification problems such as FI(T)C and PI(T)C....

  18. Statistical pairwise interaction model of stock market

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    Bury, Thomas

    2013-03-01

    Financial markets are a classical example of complex systems as they are compound by many interacting stocks. As such, we can obtain a surprisingly good description of their structure by making the rough simplification of binary daily returns. Spin glass models have been applied and gave some valuable results but at the price of restrictive assumptions on the market dynamics or they are agent-based models with rules designed in order to recover some empirical behaviors. Here we show that the pairwise model is actually a statistically consistent model with the observed first and second moments of the stocks orientation without making such restrictive assumptions. This is done with an approach only based on empirical data of price returns. Our data analysis of six major indices suggests that the actual interaction structure may be thought as an Ising model on a complex network with interaction strengths scaling as the inverse of the system size. This has potentially important implications since many properties of such a model are already known and some techniques of the spin glass theory can be straightforwardly applied. Typical behaviors, as multiple equilibria or metastable states, different characteristic time scales, spatial patterns, order-disorder, could find an explanation in this picture.

  19. Pairwise Choice Markov Chains

    OpenAIRE

    Ragain, Stephen; Ugander, Johan

    2016-01-01

    As datasets capturing human choices grow in richness and scale---particularly in online domains---there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom. In this work we introduce the Pairwise Choice Markov Chain (PCMC) model of discrete choice, an inferentially tractable model that does not assume any of the above axioms while still satisfying the foundational axiom of uniform expansio...

  20. Modeling Expressed Emotions in Music using Pairwise Comparisons

    DEFF Research Database (Denmark)

    Madsen, Jens; Nielsen, Jens Brehm; Jensen, Bjørn Sand

    2012-01-01

    We introduce a two-alternative forced-choice experimental paradigm to quantify expressed emotions in music using the two wellknown arousal and valence (AV) dimensions. In order to produce AV scores from the pairwise comparisons and to visualize the locations of excerpts in the AV space, we...

  1. Pairwise conjoint analysis of activity engagement choice

    NARCIS (Netherlands)

    Wang, Donggen; Oppewal, H.; Timmermans, H.J.P.

    2000-01-01

    Information overload is a well-known problem of conjoint choice models when respondents have to evaluate a large number of attributes and/or attribute levels. In this paper we develop an alternative conjoint modelling approach, called pairwise conjoint analysis. It differs from conventional conjoint

  2. A configuration space of homologous proteins conserving mutual information and allowing a phylogeny inference based on pair-wise Z-score probabilities

    OpenAIRE

    Maréchal Eric; Ortet Philippe; Roy Sylvaine; Bastien Olivier

    2005-01-01

    Abstract Background Popular methods to reconstruct molecular phylogenies are based on multiple sequence alignments, in which addition or removal of data may change the resulting tree topology. We have sought a representation of homologous proteins that would conserve the information of pair-wise sequence alignments, respect probabilistic properties of Z-scores (Monte Carlo methods applied to pair-wise comparisons) and be the basis for a novel method of consistent and stable phylogenetic recon...

  3. Supplier Evaluation Process by Pairwise Comparisons

    Directory of Open Access Journals (Sweden)

    Arkadiusz Kawa

    2015-01-01

    Full Text Available We propose to assess suppliers by using consistency-driven pairwise comparisons for tangible and intangible criteria. The tangible criteria are simpler to compare (e.g., the price of a service is lower than that of another service with identical characteristics. Intangible criteria are more difficult to assess. The proposed model combines assessments of both types of criteria. The main contribution of this paper is the presentation of an extension framework for the selection of suppliers in a procurement process. The final weights are computed from relative pairwise comparisons. For the needs of the paper, surveys were conducted among Polish managers dealing with cooperation with suppliers in their enterprises. The Polish practice and restricted bidding are discussed, too.

  4. Preference Learning and Ranking by Pairwise Comparison

    Science.gov (United States)

    Fürnkranz, Johannes; Hüllermeier, Eyke

    This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.

  5. Nonparametric predictive pairwise comparison with competing risks

    International Nuclear Information System (INIS)

    Coolen-Maturi, Tahani

    2014-01-01

    In reliability, failure data often correspond to competing risks, where several failure modes can cause a unit to fail. This paper presents nonparametric predictive inference (NPI) for pairwise comparison with competing risks data, assuming that the failure modes are independent. These failure modes could be the same or different among the two groups, and these can be both observed and unobserved failure modes. NPI is a statistical approach based on few assumptions, with inferences strongly based on data and with uncertainty quantified via lower and upper probabilities. The focus is on the lower and upper probabilities for the event that the lifetime of a future unit from one group, say Y, is greater than the lifetime of a future unit from the second group, say X. The paper also shows how the two groups can be compared based on particular failure mode(s), and the comparison of the two groups when some of the competing risks are combined is discussed

  6. Fast and accurate estimation of the covariance between pairwise maximum likelihood distances

    Directory of Open Access Journals (Sweden)

    Manuel Gil

    2014-09-01

    Full Text Available Pairwise evolutionary distances are a model-based summary statistic for a set of molecular sequences. They represent the leaf-to-leaf path lengths of the underlying phylogenetic tree. Estimates of pairwise distances with overlapping paths covary because of shared mutation events. It is desirable to take these covariance structure into account to increase precision in any process that compares or combines distances. This paper introduces a fast estimator for the covariance of two pairwise maximum likelihood distances, estimated under general Markov models. The estimator is based on a conjecture (going back to Nei & Jin, 1989 which links the covariance to path lengths. It is proven here under a simple symmetric substitution model. A simulation shows that the estimator outperforms previously published ones in terms of the mean squared error.

  7. Fast and accurate estimation of the covariance between pairwise maximum likelihood distances.

    Science.gov (United States)

    Gil, Manuel

    2014-01-01

    Pairwise evolutionary distances are a model-based summary statistic for a set of molecular sequences. They represent the leaf-to-leaf path lengths of the underlying phylogenetic tree. Estimates of pairwise distances with overlapping paths covary because of shared mutation events. It is desirable to take these covariance structure into account to increase precision in any process that compares or combines distances. This paper introduces a fast estimator for the covariance of two pairwise maximum likelihood distances, estimated under general Markov models. The estimator is based on a conjecture (going back to Nei & Jin, 1989) which links the covariance to path lengths. It is proven here under a simple symmetric substitution model. A simulation shows that the estimator outperforms previously published ones in terms of the mean squared error.

  8. Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

    Science.gov (United States)

    Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

    2018-01-01

    Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes. PMID:29453930

  9. Automatic Camera Calibration Using Multiple Sets of Pairwise Correspondences.

    Science.gov (United States)

    Vasconcelos, Francisco; Barreto, Joao P; Boyer, Edmond

    2018-04-01

    We propose a new method to add an uncalibrated node into a network of calibrated cameras using only pairwise point correspondences. While previous methods perform this task using triple correspondences, these are often difficult to establish when there is limited overlap between different views. In such challenging cases we must rely on pairwise correspondences and our solution becomes more advantageous. Our method includes an 11-point minimal solution for the intrinsic and extrinsic calibration of a camera from pairwise correspondences with other two calibrated cameras, and a new inlier selection framework that extends the traditional RANSAC family of algorithms to sampling across multiple datasets. Our method is validated on different application scenarios where a lack of triple correspondences might occur: addition of a new node to a camera network; calibration and motion estimation of a moving camera inside a camera network; and addition of views with limited overlap to a Structure-from-Motion model.

  10. Selecting numerical scales for pairwise comparisons

    International Nuclear Information System (INIS)

    Elliott, Michael A.

    2010-01-01

    It is often desirable in decision analysis problems to elicit from an individual the rankings of a population of attributes according to the individual's preference and to understand the degree to which each attribute is preferred to the others. A common method for obtaining this information involves the use of pairwise comparisons, which allows an analyst to convert subjective expressions of preference between two attributes into numerical values indicating preferences across the entire population of attributes. Key to the use of pairwise comparisons is the underlying numerical scale that is used to convert subjective linguistic expressions of preference into numerical values. This scale represents the psychological manner in which individuals perceive increments of preference among abstract attributes and it has important implications about the distribution and consistency of an individual's preferences. Three popular scale types, the traditional integer scales, balanced scales and power scales are examined. Results of a study of 64 individuals responding to a hypothetical decision problem show that none of these scales can accurately capture the preferences of all individuals. A study of three individuals working on an actual engineering decision problem involving the design of a decay heat removal system for a nuclear fission reactor show that the choice of scale can affect the preferred decision. It is concluded that applications of pairwise comparisons would benefit from permitting participants to choose the scale that best models their own particular way of thinking about the relative preference of attributes.

  11. Pareto optimal pairwise sequence alignment.

    Science.gov (United States)

    DeRonne, Kevin W; Karypis, George

    2013-01-01

    Sequence alignment using evolutionary profiles is a commonly employed tool when investigating a protein. Many profile-profile scoring functions have been developed for use in such alignments, but there has not yet been a comprehensive study of Pareto optimal pairwise alignments for combining multiple such functions. We show that the problem of generating Pareto optimal pairwise alignments has an optimal substructure property, and develop an efficient algorithm for generating Pareto optimal frontiers of pairwise alignments. All possible sets of two, three, and four profile scoring functions are used from a pool of 11 functions and applied to 588 pairs of proteins in the ce_ref data set. The performance of the best objective combinations on ce_ref is also evaluated on an independent set of 913 protein pairs extracted from the BAliBASE RV11 data set. Our dynamic-programming-based heuristic approach produces approximated Pareto optimal frontiers of pairwise alignments that contain comparable alignments to those on the exact frontier, but on average in less than 1/58th the time in the case of four objectives. Our results show that the Pareto frontiers contain alignments whose quality is better than the alignments obtained by single objectives. However, the task of identifying a single high-quality alignment among those in the Pareto frontier remains challenging.

  12. Elastic K-means using posterior probability.

    Science.gov (United States)

    Zheng, Aihua; Jiang, Bo; Li, Yan; Zhang, Xuehan; Ding, Chris

    2017-01-01

    The widely used K-means clustering is a hard clustering algorithm. Here we propose a Elastic K-means clustering model (EKM) using posterior probability with soft capability where each data point can belong to multiple clusters fractionally and show the benefit of proposed Elastic K-means. Furthermore, in many applications, besides vector attributes information, pairwise relations (graph information) are also available. Thus we integrate EKM with Normalized Cut graph clustering into a single clustering formulation. Finally, we provide several useful matrix inequalities which are useful for matrix formulations of learning models. Based on these results, we prove the correctness and the convergence of EKM algorithms. Experimental results on six benchmark datasets demonstrate the effectiveness of proposed EKM and its integrated model.

  13. Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA.

    Science.gov (United States)

    Kelly, Brendan J; Gross, Robert; Bittinger, Kyle; Sherrill-Mix, Scott; Lewis, James D; Collman, Ronald G; Bushman, Frederic D; Li, Hongzhe

    2015-08-01

    The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence-absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-group distance and exposure/intervention effect size must be accurately modeled to estimate statistical power for a microbiome study that will be analyzed with pairwise distances and PERMANOVA. We present a framework for PERMANOVA power estimation tailored to marker-gene microbiome studies that will be analyzed by pairwise distances, which includes: (i) a novel method for distance matrix simulation that permits modeling of within-group pairwise distances according to pre-specified population parameters; (ii) a method to incorporate effects of different sizes within the simulated distance matrix; (iii) a simulation-based method for estimating PERMANOVA power from simulated distance matrices; and (iv) an R statistical software package that implements the above. Matrices of pairwise distances can be efficiently simulated to satisfy the triangle inequality and incorporate group-level effects, which are quantified by the adjusted coefficient of determination, omega-squared (ω2). From simulated distance matrices, available PERMANOVA power or necessary sample size can be estimated for a planned microbiome study. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  14. Model uncertainty: Probabilities for models?

    International Nuclear Information System (INIS)

    Winkler, R.L.

    1994-01-01

    Like any other type of uncertainty, model uncertainty should be treated in terms of probabilities. The question is how to do this. The most commonly-used approach has a drawback related to the interpretation of the probabilities assigned to the models. If we step back and look at the big picture, asking what the appropriate focus of the model uncertainty question should be in the context of risk and decision analysis, we see that a different probabilistic approach makes more sense, although it raise some implementation questions. Current work that is underway to address these questions looks very promising

  15. SVM-dependent pairwise HMM: an application to protein pairwise alignments.

    Science.gov (United States)

    Orlando, Gabriele; Raimondi, Daniele; Khan, Taushif; Lenaerts, Tom; Vranken, Wim F

    2017-12-15

    Methods able to provide reliable protein alignments are crucial for many bioinformatics applications. In the last years many different algorithms have been developed and various kinds of information, from sequence conservation to secondary structure, have been used to improve the alignment performances. This is especially relevant for proteins with highly divergent sequences. However, recent works suggest that different features may have different importance in diverse protein classes and it would be an advantage to have more customizable approaches, capable to deal with different alignment definitions. Here we present Rigapollo, a highly flexible pairwise alignment method based on a pairwise HMM-SVM that can use any type of information to build alignments. Rigapollo lets the user decide the optimal features to align their protein class of interest. It outperforms current state of the art methods on two well-known benchmark datasets when aligning highly divergent sequences. A Python implementation of the algorithm is available at http://ibsquare.be/rigapollo. wim.vranken@vub.be. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

  16. Market Competitiveness Evaluation of Mechanical Equipment with a Pairwise Comparisons Hierarchical Model.

    Science.gov (United States)

    Hou, Fujun

    2016-01-01

    This paper provides a description of how market competitiveness evaluations concerning mechanical equipment can be made in the context of multi-criteria decision environments. It is assumed that, when we are evaluating the market competitiveness, there are limited number of candidates with some required qualifications, and the alternatives will be pairwise compared on a ratio scale. The qualifications are depicted as criteria in hierarchical structure. A hierarchical decision model called PCbHDM was used in this study based on an analysis of its desirable traits. Illustration and comparison shows that the PCbHDM provides a convenient and effective tool for evaluating the market competitiveness of mechanical equipment. The researchers and practitioners might use findings of this paper in application of PCbHDM.

  17. Probabilistic Cloning of Three Real States with Optimal Success Probabilities

    Science.gov (United States)

    Rui, Pin-shu

    2017-06-01

    We investigate the probabilistic quantum cloning (PQC) of three real states with average probability distribution. To get the analytic forms of the optimal success probabilities we assume that the three states have only two pairwise inner products. Based on the optimal success probabilities, we derive the explicit form of 1 →2 PQC for cloning three real states. The unitary operation needed in the PQC process is worked out too. The optimal success probabilities are also generalized to the M→ N PQC case.

  18. Determinants of sovereign debt yield spreads under EMU: Pairwise approach

    NARCIS (Netherlands)

    Fazlioglu, S.

    2013-01-01

    This study aims at providing an empirical analysis of long-term determinants of sovereign debt yield spreads under European EMU (Economic and Monetary Union) through pairwise approach within panel framework. Panel gravity models are increasingly used in the cross-market correlation literature while

  19. Pairwise Constraint-Guided Sparse Learning for Feature Selection.

    Science.gov (United States)

    Liu, Mingxia; Zhang, Daoqiang

    2016-01-01

    Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much attention in recent studies, most of which use class labels as supervised information. Besides class labels, there are other types of supervised information, e.g., pairwise constraints that specify whether a pair of data samples belong to the same class (must-link constraint) or different classes (cannot-link constraint). However, most of existing L1-norm-based sparse learning methods do not take advantage of the pairwise constraints that provide us weak and more general supervised information. For addressing that problem, we propose a pairwise constraint-guided sparse (CGS) learning method for feature selection, where the must-link and the cannot-link constraints are used as discriminative regularization terms that directly concentrate on the local discriminative structure of data. Furthermore, we develop two variants of CGS, including: 1) semi-supervised CGS that utilizes labeled data, pairwise constraints, and unlabeled data and 2) ensemble CGS that uses the ensemble of pairwise constraint sets. We conduct a series of experiments on a number of data sets from University of California-Irvine machine learning repository, a gene expression data set, two real-world neuroimaging-based classification tasks, and two large-scale attribute classification tasks. Experimental results demonstrate the efficacy of our proposed methods, compared with several established feature selection methods.

  20. Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels.

    Science.gov (United States)

    Fu, Yanwei; Hospedales, Timothy M; Xiang, Tao; Xiong, Jiechao; Gong, Shaogang; Wang, Yizhou; Yao, Yuan

    2016-03-01

    The problem of estimating subjective visual properties from image and video has attracted increasing interest. A subjective visual property is useful either on its own (e.g. image and video interestingness) or as an intermediate representation for visual recognition (e.g. a relative attribute). Due to its ambiguous nature, annotating the value of a subjective visual property for learning a prediction model is challenging. To make the annotation more reliable, recent studies employ crowdsourcing tools to collect pairwise comparison labels. However, using crowdsourced data also introduces outliers. Existing methods rely on majority voting to prune the annotation outliers/errors. They thus require a large amount of pairwise labels to be collected. More importantly as a local outlier detection method, majority voting is ineffective in identifying outliers that can cause global ranking inconsistencies. In this paper, we propose a more principled way to identify annotation outliers by formulating the subjective visual property prediction task as a unified robust learning to rank problem, tackling both the outlier detection and learning to rank jointly. This differs from existing methods in that (1) the proposed method integrates local pairwise comparison labels together to minimise a cost that corresponds to global inconsistency of ranking order, and (2) the outlier detection and learning to rank problems are solved jointly. This not only leads to better detection of annotation outliers but also enables learning with extremely sparse annotations.

  1. Improving the modelling of redshift-space distortions - I. A bivariate Gaussian description for the galaxy pairwise velocity distributions

    Science.gov (United States)

    Bianchi, Davide; Chiesa, Matteo; Guzzo, Luigi

    2015-01-01

    As a step towards a more accurate modelling of redshift-space distortions (RSD) in galaxy surveys, we develop a general description of the probability distribution function of galaxy pairwise velocities within the framework of the so-called streaming model. For a given galaxy separation r, such function can be described as a superposition of virtually infinite local distributions. We characterize these in terms of their moments and then consider the specific case in which they are Gaussian functions, each with its own mean μ and dispersion σ. Based on physical considerations, we make the further crucial assumption that these two parameters are in turn distributed according to a bivariate Gaussian, with its own mean and covariance matrix. Tests using numerical simulations explicitly show that with this compact description one can correctly model redshift-space distortions on all scales, fully capturing the overall linear and non-linear dynamics of the galaxy flow at different separations. In particular, we naturally obtain Gaussian/exponential, skewed/unskewed distribution functions, depending on separation as observed in simulations and data. Also, the recently proposed single-Gaussian description of RSD is included in this model as a limiting case, when the bivariate Gaussian is collapsed to a two-dimensional Dirac delta function. We also show how this description naturally allows for the Taylor expansion of 1 + ξS(s) around 1 + ξR(r), which leads to the Kaiser linear formula when truncated to second order, explicating its connection with the moments of the velocity distribution functions. More work is needed, but these results indicate a very promising path to make definitive progress in our programme to improve RSD estimators.

  2. A scalable pairwise class interaction framework for multidimensional classification

    DEFF Research Database (Denmark)

    Arias, Jacinto; Gámez, Jose A.; Nielsen, Thomas Dyhre

    2016-01-01

    We present a general framework for multidimensional classification that cap- tures the pairwise interactions between class variables. The pairwise class inter- actions are encoded using a collection of base classifiers (Phase 1), for which the class predictions are combined in a Markov random fie...

  3. Dynamics of pairwise motions in the Cosmic Web

    Science.gov (United States)

    Hellwing, Wojciech A.

    2016-10-01

    We present results of analysis of the dark matter (DM) pairwise velocity statistics in different Cosmic Web environments. We use the DM velocity and density field from the Millennium 2 simulation together with the NEXUS+ algorithm to segment the simulation volume into voxels uniquely identifying one of the four possible environments: nodes, filaments, walls or cosmic voids. We show that the PDFs of the mean infall velocities v 12 as well as its spatial dependence together with the perpendicular and parallel velocity dispersions bear a significant signal of the large-scale structure environment in which DM particle pairs are embedded. The pairwise flows are notably colder and have smaller mean magnitude in wall and voids, when compared to much denser environments of filaments and nodes. We discuss on our results, indicating that they are consistent with a simple theoretical predictions for pairwise motions as induced by gravitational instability mechanism. Our results indicate that the Cosmic Web elements are coherent dynamical entities rather than just temporal geometrical associations. In addition it should be possible to observationally test various Cosmic Web finding algorithms by segmenting available peculiar velocity data and studying resulting pairwise velocity statistics.

  4. Memory-efficient dynamic programming backtrace and pairwise local sequence alignment.

    Science.gov (United States)

    Newberg, Lee A

    2008-08-15

    A backtrace through a dynamic programming algorithm's intermediate results in search of an optimal path, or to sample paths according to an implied probability distribution, or as the second stage of a forward-backward algorithm, is a task of fundamental importance in computational biology. When there is insufficient space to store all intermediate results in high-speed memory (e.g. cache) existing approaches store selected stages of the computation, and recompute missing values from these checkpoints on an as-needed basis. Here we present an optimal checkpointing strategy, and demonstrate its utility with pairwise local sequence alignment of sequences of length 10,000. Sample C++-code for optimal backtrace is available in the Supplementary Materials. Supplementary data is available at Bioinformatics online.

  5. Theory of pairwise lesion interaction

    International Nuclear Information System (INIS)

    Harder, Dietrich; Virsik-Peuckert, Patricia; Bartels, Ernst

    1992-01-01

    A comparison between repair time constants measured both at the molecular and cellular levels has shown that the DNA double strand break is the molecular change of key importance in the causation of cellular effects such as chromosome aberrations and cell inactivation. Cell fusion experiments provided the evidence that it needs the pairwise interaction between two double strand breaks - or more exactly between the two ''repair sites'' arising from them in the course of enzymatic repair - to provide the faulty chromatin crosslink which leads to cytogenetic and cytolethal effects. These modern experiments have confirmed the classical assumption of pairwise lesion interaction (PLI) on which the models of Lea and Neary were based. It seems worthwhile to continue and complete the mathematical treatment of their proposed mechanism in order to show in quantitative terms that the well-known fractionation, protraction and linear energy transfer (LET) irradiation effects are consequences of or can at least be partly attributed to PLI. Arithmetic treatment of PLI - a second order reaction - has also the advantage of providing a prerequisite for further investigations into the stages of development of misrepair products such as chromatin crosslinks. It has been possible to formulate a completely arithmetic theory of PLI by consequently applying three biophysically permitted approximations - pure first order lesion repair kinetics, dose-independent repair time constants and low yield of the ionization/lesion conversion. The mathematical approach will be summarized here, including several formulae not elaborated at the time of previous publications. We will also study an application which sheds light on the chain of events involved in PLI. (author)

  6. Calculating the Probability of Returning a Loan with Binary Probability Models

    Directory of Open Access Journals (Sweden)

    Julian Vasilev

    2014-12-01

    Full Text Available The purpose of this article is to give a new approach in calculating the probability of returning a loan. A lot of factors affect the value of the probability. In this article by using statistical and econometric models some influencing factors are proved. The main approach is concerned with applying probit and logit models in loan management institutions. A new aspect of the credit risk analysis is given. Calculating the probability of returning a loan is a difficult task. We assume that specific data fields concerning the contract (month of signing, year of signing, given sum and data fields concerning the borrower of the loan (month of birth, year of birth (age, gender, region, where he/she lives may be independent variables in a binary logistics model with a dependent variable “the probability of returning a loan”. It is proved that the month of signing a contract, the year of signing a contract, the gender and the age of the loan owner do not affect the probability of returning a loan. It is proved that the probability of returning a loan depends on the sum of contract, the remoteness of the loan owner and the month of birth. The probability of returning a loan increases with the increase of the given sum, decreases with the proximity of the customer, increases for people born in the beginning of the year and decreases for people born at the end of the year.

  7. Modelling the probability of building fires

    Directory of Open Access Journals (Sweden)

    Vojtěch Barták

    2014-12-01

    Full Text Available Systematic spatial risk analysis plays a crucial role in preventing emergencies.In the Czech Republic, risk mapping is currently based on the risk accumulationprinciple, area vulnerability, and preparedness levels of Integrated Rescue Systemcomponents. Expert estimates are used to determine risk levels for individualhazard types, while statistical modelling based on data from actual incidents andtheir possible causes is not used. Our model study, conducted in cooperation withthe Fire Rescue Service of the Czech Republic as a model within the Liberec andHradec Králové regions, presents an analytical procedure leading to the creation ofbuilding fire probability maps based on recent incidents in the studied areas andon building parameters. In order to estimate the probability of building fires, aprediction model based on logistic regression was used. Probability of fire calculatedby means of model parameters and attributes of specific buildings can subsequentlybe visualized in probability maps.

  8. A discrete model of Ostwald ripening based on multiple pairwise interactions

    Science.gov (United States)

    Di Nunzio, Paolo Emilio

    2018-06-01

    A discrete multi-particle model of Ostwald ripening based on direct pairwise interactions is developed for particles with incoherent interfaces as an alternative to the classical LSW mean field theory. The rate of matter exchange depends on the average surface-to-surface interparticle distance, a characteristic feature of the system which naturally incorporates the effect of volume fraction of second phase. The multi-particle diffusion is described through the definition of an interaction volume containing all the particles involved in the exchange of solute. At small volume fractions this is proportional to the size of the central particle, at higher volume fractions it gradually reduces as a consequence of diffusion screening described on a geometrical basis. The topological noise present in real systems is also included. For volume fractions below about 0.1 the model predicts broad and right-skewed stationary size distributions resembling a lognormal function. Above this value, a transition to sharper, more symmetrical but still right-skewed shapes occurs. An excellent agreement with experiments is obtained for 3D particle size distributions of solid-solid and solid-liquid systems with volume fraction 0.07, 0.30, 0.52 and 0.74. The kinetic constant of the model depends on the cube root of volume fraction up to about 0.1, then increases rapidly with an upward concavity. It is in good agreement with the available literature data on solid-liquid mixtures in the volume fraction range from 0.20 to about 0.75.

  9. Correlations and Non-Linear Probability Models

    DEFF Research Database (Denmark)

    Breen, Richard; Holm, Anders; Karlson, Kristian Bernt

    2014-01-01

    the dependent variable of the latent variable model and its predictor variables. We show how this correlation can be derived from the parameters of non-linear probability models, develop tests for the statistical significance of the derived correlation, and illustrate its usefulness in two applications. Under......Although the parameters of logit and probit and other non-linear probability models are often explained and interpreted in relation to the regression coefficients of an underlying linear latent variable model, we argue that they may also be usefully interpreted in terms of the correlations between...... certain circumstances, which we explain, the derived correlation provides a way of overcoming the problems inherent in cross-sample comparisons of the parameters of non-linear probability models....

  10. Doctoral Program Selection Using Pairwise Comparisons.

    Science.gov (United States)

    Tadisina, Suresh K.; Bhasin, Vijay

    1989-01-01

    The application of a pairwise comparison methodology (Saaty's Analytic Hierarchy Process) to the doctoral program selection process is illustrated. A hierarchy for structuring and facilitating the doctoral program selection decision is described. (Author/MLW)

  11. High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models.

    Science.gov (United States)

    Monaco, James P; Tomaszewski, John E; Feldman, Michael D; Hagemann, Ian; Moradi, Mehdi; Mousavi, Parvin; Boag, Alexander; Davidson, Chris; Abolmaesumi, Purang; Madabhushi, Anant

    2010-08-01

    In this paper we present a high-throughput system for detecting regions of carcinoma of the prostate (CaP) in HSs from radical prostatectomies (RPs) using probabilistic pairwise Markov models (PPMMs), a novel type of Markov random field (MRF). At diagnostic resolution a digitized HS can contain 80Kx70K pixels - far too many for current automated Gleason grading algorithms to process. However, grading can be separated into two distinct steps: (1) detecting cancerous regions and (2) then grading these regions. The detection step does not require diagnostic resolution and can be performed much more quickly. Thus, we introduce a CaP detection system capable of analyzing an entire digitized whole-mount HS (2x1.75cm(2)) in under three minutes (on a desktop computer) while achieving a CaP detection sensitivity and specificity of 0.87 and 0.90, respectively. We obtain this high-throughput by tailoring the system to analyze the HSs at low resolution (8microm per pixel). This motivates the following algorithm: (Step 1) glands are segmented, (Step 2) the segmented glands are classified as malignant or benign, and (Step 3) the malignant glands are consolidated into continuous regions. The classification of individual glands leverages two features: gland size and the tendency for proximate glands to share the same class. The latter feature describes a spatial dependency which we model using a Markov prior. Typically, Markov priors are expressed as the product of potential functions. Unfortunately, potential functions are mathematical abstractions, and constructing priors through their selection becomes an ad hoc procedure, resulting in simplistic models such as the Potts. Addressing this problem, we introduce PPMMs which formulate priors in terms of probability density functions, allowing the creation of more sophisticated models. To demonstrate the efficacy of our CaP detection system and assess the advantages of using a PPMM prior instead of the Potts, we alternately

  12. SFESA: a web server for pairwise alignment refinement by secondary structure shifts.

    Science.gov (United States)

    Tong, Jing; Pei, Jimin; Grishin, Nick V

    2015-09-03

    Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate. We developed the SFESA web server to refine pairwise protein sequence alignments. Compared to the previous version of SFESA, which required a set of 3D coordinates for a protein, the new server will search a sequence database for the closest homolog with an available 3D structure to be used as a template. For each alignment block defined by secondary structure elements in the template, SFESA evaluates alignment variants generated by local shifts and selects the best-scoring alignment variant. A scoring function that combines the sequence score of profile-profile comparison and the structure score of template-derived contact energy is used for evaluation of alignments. PROMALS pairwise alignments refined by SFESA are more accurate than those produced by current advanced alignment methods such as HHpred and CNFpred. In addition, SFESA also improves alignments generated by other software. SFESA is a web-based tool for alignment refinement, designed for researchers to compute, refine, and evaluate pairwise alignments with a combined sequence and structure scoring of alignment blocks. To our knowledge, the SFESA web server is the only tool that refines alignments by evaluating local shifts of secondary structure elements. The SFESA web server is available at http://prodata.swmed.edu/sfesa.

  13. The transition probabilities of the reciprocity model

    NARCIS (Netherlands)

    Snijders, T.A.B.

    1999-01-01

    The reciprocity model is a continuous-time Markov chain model used for modeling longitudinal network data. A new explicit expression is derived for its transition probability matrix. This expression can be checked relatively easily. Some properties of the transition probabilities are given, as well

  14. Comparing linear probability model coefficients across groups

    DEFF Research Database (Denmark)

    Holm, Anders; Ejrnæs, Mette; Karlson, Kristian Bernt

    2015-01-01

    of the following three components: outcome truncation, scale parameters and distributional shape of the predictor variable. These results point to limitations in using linear probability model coefficients for group comparisons. We also provide Monte Carlo simulations and real examples to illustrate......This article offers a formal identification analysis of the problem in comparing coefficients from linear probability models between groups. We show that differences in coefficients from these models can result not only from genuine differences in effects, but also from differences in one or more...... these limitations, and we suggest a restricted approach to using linear probability model coefficients in group comparisons....

  15. Pairwise structure alignment specifically tuned for surface pockets and interaction interfaces

    KAUST Repository

    Cui, Xuefeng

    2015-09-09

    (PROSTA) family of pairwise structure alignment methods [1, 2] that address the fragmentation issue of pockets and interfaces, and automatically align interfaces between any types of biological complexes. Our PROSTA structure alignment methods have two critical advantages comparing to existing structure alignment methods. First, our methods are completely sequence order independent, which is critical to the success of pairwise pocket and interface structure alignments. This is achieved by introducing contact groups that are not limited to backbone fragments, and by employing a maximum weighted bipartite matching solver from the beginning of the alignment process. In addition, our methods incorporate similarities of sequentially and structurally remote residues that potentially model the topology of the global structure. Comparing to existing methods that focus on local structure or whole sequence similarities, topological similarities are more reliable to find near-optimal structure alignments in the initial alignment state. As a result, a significant number of similar pockets and interfaces are newly discovered, and literatures also support that similar functions are shared between biological complexes in our cases studies. The PROSTA web-server and source codes are publicly available at "http://www.cbrc.kaust.edu.sa/prosta/".

  16. A probability space for quantum models

    Science.gov (United States)

    Lemmens, L. F.

    2017-06-01

    A probability space contains a set of outcomes, a collection of events formed by subsets of the set of outcomes and probabilities defined for all events. A reformulation in terms of propositions allows to use the maximum entropy method to assign the probabilities taking some constraints into account. The construction of a probability space for quantum models is determined by the choice of propositions, choosing the constraints and making the probability assignment by the maximum entropy method. This approach shows, how typical quantum distributions such as Maxwell-Boltzmann, Fermi-Dirac and Bose-Einstein are partly related with well-known classical distributions. The relation between the conditional probability density, given some averages as constraints and the appropriate ensemble is elucidated.

  17. The Role of Middlemen inEfficient and Strongly Pairwise Stable Networks

    NARCIS (Netherlands)

    Gilles, R.P.; Chakrabarti, S.; Sarangi, S.; Badasyan, N.

    2004-01-01

    We examine the strong pairwise stability concept in network formation theory under collective network benefits.Strong pairwise stability considers a pair of players to add a link through mutual consent while permitting them to unilaterally delete any subset of links under their control.We examine

  18. Dependency models and probability of joint events

    International Nuclear Information System (INIS)

    Oerjasaeter, O.

    1982-08-01

    Probabilistic dependencies between components/systems are discussed with reference to a broad classification of potential failure mechanisms. Further, a generalized time-dependency model, based on conditional probabilities for estimation of the probability of joint events and event sequences is described. The applicability of this model is clarified/demonstrated by various examples. It is concluded that the described model of dependency is a useful tool for solving a variety of practical problems concerning the probability of joint events and event sequences where common cause and time-dependent failure mechanisms are involved. (Auth.)

  19. Predicting community composition from pairwise interactions

    Science.gov (United States)

    Friedman, Jonathan; Higgins, Logan; Gore, Jeff

    The ability to predict the structure of complex, multispecies communities is crucial for understanding the impact of species extinction and invasion on natural communities, as well as for engineering novel, synthetic communities. Communities are often modeled using phenomenological models, such as the classical generalized Lotka-Volterra (gLV) model. While a lot of our intuition comes from such models, their predictive power has rarely been tested experimentally. To directly assess the predictive power of this approach, we constructed synthetic communities comprised of up to 8 soil bacteria. We measured the outcome of competition between all species pairs, and used these measurements to predict the composition of communities composed of more than 2 species. The pairwise competitions resulted in a diverse set of outcomes, including coexistence, exclusion, and bistability, and displayed evidence for both interference and facilitation. Most pair outcomes could be captured by the gLV framework, and the composition of multispecies communities could be predicted for communities composed solely of such pairs. Our results demonstrate the predictive ability and utility of simple phenomenology, which enables accurate predictions in the absence of mechanistic details.

  20. Model uncertainty and probability

    International Nuclear Information System (INIS)

    Parry, G.W.

    1994-01-01

    This paper discusses the issue of model uncertainty. The use of probability as a measure of an analyst's uncertainty as well as a means of describing random processes has caused some confusion, even though the two uses are representing different types of uncertainty with respect to modeling a system. The importance of maintaining the distinction between the two types is illustrated with a simple example

  1. On calculating the probability of a set of orthologous sequences

    Directory of Open Access Journals (Sweden)

    Junfeng Liu

    2009-02-01

    Full Text Available Junfeng Liu1,2, Liang Chen3, Hongyu Zhao4, Dirk F Moore1,2, Yong Lin1,2, Weichung Joe Shih1,21Biometrics Division, The Cancer, Institute of New Jersey, New Brunswick, NJ, USA; 2Department of Biostatistics, School of Public Health, University of Medicine and Dentistry of New Jersey, Piscataway, NJ, USA; 3Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA; 4Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT, USAAbstract: Probabilistic DNA sequence models have been intensively applied to genome research. Within the evolutionary biology framework, this article investigates the feasibility for rigorously estimating the probability of a set of orthologous DNA sequences which evolve from a common progenitor. We propose Monte Carlo integration algorithms to sample the unknown ancestral and/or root sequences a posteriori conditional on a reference sequence and apply pairwise Needleman–Wunsch alignment between the sampled and nonreference species sequences to estimate the probability. We test our algorithms on both simulated and real sequences and compare calculated probabilities from Monte Carlo integration to those induced by single multiple alignment.Keywords: evolution, Jukes–Cantor model, Monte Carlo integration, Needleman–Wunsch alignment, orthologous

  2. A water market simulator considering pair-wise trades between agents

    Science.gov (United States)

    Huskova, I.; Erfani, T.; Harou, J. J.

    2012-04-01

    In many basins in England no further water abstraction licences are available. Trading water between water rights holders has been recognized as a potentially effective and economically efficient strategy to mitigate increasing scarcity. A screening tool that could assess the potential for trade through realistic simulation of individual water rights holders would help assess the solution's potential contribution to local water management. We propose an optimisation-driven water market simulator that predicts pair-wise trade in a catchment and represents its interaction with natural hydrology and engineered infrastructure. A model is used to emulate licence-holders' willingness to engage in short-term trade transactions. In their simplest form agents are represented using an economic benefit function. The working hypothesis is that trading behaviour can be partially predicted based on differences in marginal values of water over space and time and estimates of transaction costs on pair-wise trades. We discuss the further possibility of embedding rules, norms and preferences of the different water user sectors to more realistically represent the behaviours, motives and constraints of individual licence holders. The potential benefits and limitations of such a social simulation (agent-based) approach is contrasted with our simulator where agents are driven by economic optimization. A case study based on the Dove River Basin (UK) demonstrates model inputs and outputs. The ability of the model to suggest impacts of water rights policy reforms on trading is discussed.

  3. Revisiting the classification of curtoviruses based on genome-wide pairwise identity

    KAUST Repository

    Varsani, Arvind; Martin, Darren Patrick; Navas-Castillo, Jesú s; Moriones, Enrique; Herná ndez-Zepeda, Cecilia; Idris, Ali; Murilo Zerbini, F.; Brown, Judith K.

    2014-01-01

    Members of the genus Curtovirus (family Geminiviridae) are important pathogens of many wild and cultivated plant species. Until recently, relatively few full curtovirus genomes have been characterised. However, with the 19 full genome sequences now available in public databases, we revisit the proposed curtovirus species and strain classification criteria. Using pairwise identities coupled with phylogenetic evidence, revised species and strain demarcation guidelines have been instituted. Specifically, we have established 77% genome-wide pairwise identity as a species demarcation threshold and 94% genome-wide pairwise identity as a strain demarcation threshold. Hence, whereas curtovirus sequences with >77% genome-wide pairwise identity would be classified as belonging to the same species, those sharing >94% identity would be classified as belonging to the same strain. We provide step-by-step guidelines to facilitate the classification of newly discovered curtovirus full genome sequences and a set of defined criteria for naming new species and strains. The revision yields three curtovirus species: Beet curly top virus (BCTV), Spinach severe surly top virus (SpSCTV) and Horseradish curly top virus (HrCTV). © 2014 Springer-Verlag Wien.

  4. Revisiting the classification of curtoviruses based on genome-wide pairwise identity

    KAUST Repository

    Varsani, Arvind

    2014-01-25

    Members of the genus Curtovirus (family Geminiviridae) are important pathogens of many wild and cultivated plant species. Until recently, relatively few full curtovirus genomes have been characterised. However, with the 19 full genome sequences now available in public databases, we revisit the proposed curtovirus species and strain classification criteria. Using pairwise identities coupled with phylogenetic evidence, revised species and strain demarcation guidelines have been instituted. Specifically, we have established 77% genome-wide pairwise identity as a species demarcation threshold and 94% genome-wide pairwise identity as a strain demarcation threshold. Hence, whereas curtovirus sequences with >77% genome-wide pairwise identity would be classified as belonging to the same species, those sharing >94% identity would be classified as belonging to the same strain. We provide step-by-step guidelines to facilitate the classification of newly discovered curtovirus full genome sequences and a set of defined criteria for naming new species and strains. The revision yields three curtovirus species: Beet curly top virus (BCTV), Spinach severe surly top virus (SpSCTV) and Horseradish curly top virus (HrCTV). © 2014 Springer-Verlag Wien.

  5. Classification between normal and tumor tissues based on the pair-wise gene expression ratio

    International Nuclear Information System (INIS)

    Yap, YeeLeng; Zhang, XueWu; Ling, MT; Wang, XiangHong; Wong, YC; Danchin, Antoine

    2004-01-01

    Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset – from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested

  6. Convergence of Transition Probability Matrix in CLVMarkov Models

    Science.gov (United States)

    Permana, D.; Pasaribu, U. S.; Indratno, S. W.; Suprayogi, S.

    2018-04-01

    A transition probability matrix is an arrangement of transition probability from one states to another in a Markov chain model (MCM). One of interesting study on the MCM is its behavior for a long time in the future. The behavior is derived from one property of transition probabilty matrix for n steps. This term is called the convergence of the n-step transition matrix for n move to infinity. Mathematically, the convergence of the transition probability matrix is finding the limit of the transition matrix which is powered by n where n moves to infinity. The convergence form of the transition probability matrix is very interesting as it will bring the matrix to its stationary form. This form is useful for predicting the probability of transitions between states in the future. The method usually used to find the convergence of transition probability matrix is through the process of limiting the distribution. In this paper, the convergence of the transition probability matrix is searched using a simple concept of linear algebra that is by diagonalizing the matrix.This method has a higher level of complexity because it has to perform the process of diagonalization in its matrix. But this way has the advantage of obtaining a common form of power n of the transition probability matrix. This form is useful to see transition matrix before stationary. For example cases are taken from CLV model using MCM called Model of CLV-Markov. There are several models taken by its transition probability matrix to find its convergence form. The result is that the convergence of the matrix of transition probability through diagonalization has similarity with convergence with commonly used distribution of probability limiting method.

  7. Comparing coefficients of nested nonlinear probability models

    DEFF Research Database (Denmark)

    Kohler, Ulrich; Karlson, Kristian Bernt; Holm, Anders

    2011-01-01

    In a series of recent articles, Karlson, Holm and Breen have developed a method for comparing the estimated coeffcients of two nested nonlinear probability models. This article describes this method and the user-written program khb that implements the method. The KHB-method is a general decomposi......In a series of recent articles, Karlson, Holm and Breen have developed a method for comparing the estimated coeffcients of two nested nonlinear probability models. This article describes this method and the user-written program khb that implements the method. The KHB-method is a general...... decomposition method that is unaffected by the rescaling or attenuation bias that arise in cross-model comparisons in nonlinear models. It recovers the degree to which a control variable, Z, mediates or explains the relationship between X and a latent outcome variable, Y*, underlying the nonlinear probability...

  8. Hierarchical ordering with partial pairwise hierarchical relationships on the macaque brain data sets.

    Directory of Open Access Journals (Sweden)

    Woosang Lim

    Full Text Available Hierarchical organizations of information processing in the brain networks have been known to exist and widely studied. To find proper hierarchical structures in the macaque brain, the traditional methods need the entire pairwise hierarchical relationships between cortical areas. In this paper, we present a new method that discovers hierarchical structures of macaque brain networks by using partial information of pairwise hierarchical relationships. Our method uses a graph-based manifold learning to exploit inherent relationship, and computes pseudo distances of hierarchical levels for every pair of cortical areas. Then, we compute hierarchy levels of all cortical areas by minimizing the sum of squared hierarchical distance errors with the hierarchical information of few cortical areas. We evaluate our method on the macaque brain data sets whose true hierarchical levels are known as the FV91 model. The experimental results show that hierarchy levels computed by our method are similar to the FV91 model, and its errors are much smaller than the errors of hierarchical clustering approaches.

  9. Predicting Cumulative Incidence Probability: Marginal and Cause-Specific Modelling

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    2005-01-01

    cumulative incidence probability; cause-specific hazards; subdistribution hazard; binomial modelling......cumulative incidence probability; cause-specific hazards; subdistribution hazard; binomial modelling...

  10. PAIRWISE BLENDING OF HIGH LEVEL WASTE

    International Nuclear Information System (INIS)

    CERTA, P.J.

    2006-01-01

    The primary objective of this study is to demonstrate a mission scenario that uses pairwise and incidental blending of high level waste (HLW) to reduce the total mass of HLW glass. Secondary objectives include understanding how recent refinements to the tank waste inventory and solubility assumptions affect the mass of HLW glass and how logistical constraints may affect the efficacy of HLW blending

  11. A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

    Science.gov (United States)

    Stock, Michiel; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard; Waegeman, Willem

    2018-06-12

    Many machine learning problems can be formulated as predicting labels for a pair of objects. Problems of that kind are often referred to as pairwise learning, dyadic prediction, or network inference problems. During the past decade, kernel methods have played a dominant role in pairwise learning. They still obtain a state-of-the-art predictive performance, but a theoretical analysis of their behavior has been underexplored in the machine learning literature. In this work we review and unify kernel-based algorithms that are commonly used in different pairwise learning settings, ranging from matrix filtering to zero-shot learning. To this end, we focus on closed-form efficient instantiations of Kronecker kernel ridge regression. We show that independent task kernel ridge regression, two-step kernel ridge regression, and a linear matrix filter arise naturally as a special case of Kronecker kernel ridge regression, implying that all these methods implicitly minimize a squared loss. In addition, we analyze universality, consistency, and spectral filtering properties. Our theoretical results provide valuable insights into assessing the advantages and limitations of existing pairwise learning methods.

  12. Uncertainty the soul of modeling, probability & statistics

    CERN Document Server

    Briggs, William

    2016-01-01

    This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance". The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models. Its jargon-free approach asserts that standard methods, suc...

  13. Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game

    Science.gov (United States)

    Zai, Dawei; Li, Jonathan; Guo, Yulan; Cheng, Ming; Huang, Pengdi; Cao, Xiaofei; Wang, Cheng

    2017-12-01

    It is challenging to automatically register TLS point clouds with noise, outliers and varying overlap. In this paper, we propose a new method for pairwise registration of TLS point clouds. We first generate covariance matrix descriptors with an adaptive neighborhood size from point clouds to find candidate correspondences, we then construct a non-cooperative game to isolate mutual compatible correspondences, which are considered as true positives. The method was tested on three models acquired by two different TLS systems. Experimental results demonstrate that our proposed adaptive covariance (ACOV) descriptor is invariant to rigid transformation and robust to noise and varying resolutions. The average registration errors achieved on three models are 0.46 cm, 0.32 cm and 1.73 cm, respectively. The computational times cost on these models are about 288 s, 184 s and 903 s, respectively. Besides, our registration framework using ACOV descriptors and a game theoretic method is superior to the state-of-the-art methods in terms of both registration error and computational time. The experiment on a large outdoor scene further demonstrates the feasibility and effectiveness of our proposed pairwise registration framework.

  14. Pairwise Comparison and Distance Measure of Hesitant Fuzzy Linguistic Term Sets

    Directory of Open Access Journals (Sweden)

    Han-Chen Huang

    2014-01-01

    Full Text Available A hesitant fuzzy linguistic term set (HFLTS, allowing experts using several possible linguistic terms to assess a qualitative linguistic variable, is very useful to express people’s hesitancy in practical decision-making problems. Up to now, a little research has been done on the comparison and distance measure of HFLTSs. In this paper, we present a comparison method for HFLTSs based on pairwise comparisons of each linguistic term in the two HFLTSs. Then, a distance measure method based on the pairwise comparison matrix of HFLTSs is proposed, and we prove that this distance is equal to the distance of the average values of HFLTSs, which makes the distance measure much more simple. Finally, the pairwise comparison and distance measure methods are utilized to develop two multicriteria decision-making approaches under hesitant fuzzy linguistic environments. The results analysis shows that our methods in this paper are more reasonable.

  15. Evaluation of advanced multiplex short tandem repeat systems in pairwise kinship analysis.

    Science.gov (United States)

    Tamura, Tomonori; Osawa, Motoki; Ochiai, Eriko; Suzuki, Takanori; Nakamura, Takashi

    2015-09-01

    The AmpFLSTR Identifiler Kit, comprising 15 autosomal short tandem repeat (STR) loci, is commonly employed in forensic practice for calculating match probabilities and parentage testing. The conventional system exhibits insufficient estimation for kinship analysis such as sibship testing because of shortness of examined loci. This study evaluated the power of the PowerPlex Fusion System, GlobalFiler Kit, and PowerPlex 21 System, which comprise more than 20 autosomal STR loci, to estimate pairwise blood relatedness (i.e., parent-child, full siblings, second-degree relatives, and first cousins). The genotypes of all 24 STR loci in 10,000 putative pedigrees were constructed by simulation. The likelihood ratio for each locus was calculated from joint probabilities for relatives and non-relatives. The combined likelihood ratio was calculated according to the product rule. The addition of STR loci improved separation between relatives and non-relatives. However, these systems were less effectively extended to the inference for first cousins. In conclusion, these advanced systems will be useful in forensic personal identification, especially in the evaluation of full siblings and second-degree relatives. Moreover, the additional loci may give rise to two major issues of more frequent mutational events and several pairs of linked loci on the same chromosome. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  16. SDT: a virus classification tool based on pairwise sequence alignment and identity calculation.

    Directory of Open Access Journals (Sweden)

    Brejnev Muhizi Muhire

    Full Text Available The perpetually increasing rate at which viral full-genome sequences are being determined is creating a pressing demand for computational tools that will aid the objective classification of these genome sequences. Taxonomic classification approaches that are based on pairwise genetic identity measures are potentially highly automatable and are progressively gaining favour with the International Committee on Taxonomy of Viruses (ICTV. There are, however, various issues with the calculation of such measures that could potentially undermine the accuracy and consistency with which they can be applied to virus classification. Firstly, pairwise sequence identities computed based on multiple sequence alignments rather than on multiple independent pairwise alignments can lead to the deflation of identity scores with increasing dataset sizes. Also, when gap-characters need to be introduced during sequence alignments to account for insertions and deletions, methodological variations in the way that these characters are introduced and handled during pairwise genetic identity calculations can cause high degrees of inconsistency in the way that different methods classify the same sets of sequences. Here we present Sequence Demarcation Tool (SDT, a free user-friendly computer program that aims to provide a robust and highly reproducible means of objectively using pairwise genetic identity calculations to classify any set of nucleotide or amino acid sequences. SDT can produce publication quality pairwise identity plots and colour-coded distance matrices to further aid the classification of sequences according to ICTV approved taxonomic demarcation criteria. Besides a graphical interface version of the program for Windows computers, command-line versions of the program are available for a variety of different operating systems (including a parallel version for cluster computing platforms.

  17. A quantum probability model of causal reasoning

    Directory of Open Access Journals (Sweden)

    Jennifer S Trueblood

    2012-05-01

    Full Text Available People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause with diagnostic judgments (i.e., the conditional probability of a cause given an effect. The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment.

  18. Pairwise Trajectory Management (PTM): Concept Overview

    Science.gov (United States)

    Jones, Kenneth M.; Graff, Thomas J.; Chartrand, Ryan C.; Carreno, Victor; Kibler, Jennifer L.

    2017-01-01

    Pairwise Trajectory Management (PTM) is an Interval Management (IM) concept that utilizes airborne and ground-based capabilities to enable the implementation of airborne pairwise spacing capabilities in oceanic regions. The goal of PTM is to use airborne surveillance and tools to manage an "at or greater than" inter-aircraft spacing. Due to the precision of Automatic Dependent Surveillance-Broadcast (ADS-B) information and the use of airborne spacing guidance, the PTM minimum spacing distance will be less than distances a controller can support with current automation systems that support oceanic operations. Ground tools assist the controller in evaluating the traffic picture and determining appropriate PTM clearances to be issued. Avionics systems provide guidance information that allows the flight crew to conform to the PTM clearance issued by the controller. The combination of a reduced minimum distance and airborne spacing management will increase the capacity and efficiency of aircraft operations at a given altitude or volume of airspace. This paper provides an overview of the proposed application, description of a few key scenarios, high level discussion of expected air and ground equipment and procedure changes, overview of a potential flight crew human-machine interface that would support PTM operations and some initial PTM benefits results.

  19. Illustrating Probability through Roulette: A Spreadsheet Simulation Model

    Directory of Open Access Journals (Sweden)

    Kala Chand Seal

    2005-11-01

    Full Text Available Teaching probability can be challenging because the mathematical formulas often are too abstract and complex for the students to fully grasp the underlying meaning and effect of the concepts. Games can provide a way to address this issue. For example, the game of roulette can be an exciting application for teaching probability concepts. In this paper, we implement a model of roulette in a spreadsheet that can simulate outcomes of various betting strategies. The simulations can be analyzed to gain better insights into the corresponding probability structures. We use the model to simulate a particular betting strategy known as the bet-doubling, or Martingale, strategy. This strategy is quite popular and is often erroneously perceived as a winning strategy even though the probability analysis shows that such a perception is incorrect. The simulation allows us to present the true implications of such a strategy for a player with a limited betting budget and relate the results to the underlying theoretical probability structure. The overall validation of the model, its use for teaching, including its application to analyze other types of betting strategies are discussed.

  20. Maximum parsimony, substitution model, and probability phylogenetic trees.

    Science.gov (United States)

    Weng, J F; Thomas, D A; Mareels, I

    2011-01-01

    The problem of inferring phylogenies (phylogenetic trees) is one of the main problems in computational biology. There are three main methods for inferring phylogenies-Maximum Parsimony (MP), Distance Matrix (DM) and Maximum Likelihood (ML), of which the MP method is the most well-studied and popular method. In the MP method the optimization criterion is the number of substitutions of the nucleotides computed by the differences in the investigated nucleotide sequences. However, the MP method is often criticized as it only counts the substitutions observable at the current time and all the unobservable substitutions that really occur in the evolutionary history are omitted. In order to take into account the unobservable substitutions, some substitution models have been established and they are now widely used in the DM and ML methods but these substitution models cannot be used within the classical MP method. Recently the authors proposed a probability representation model for phylogenetic trees and the reconstructed trees in this model are called probability phylogenetic trees. One of the advantages of the probability representation model is that it can include a substitution model to infer phylogenetic trees based on the MP principle. In this paper we explain how to use a substitution model in the reconstruction of probability phylogenetic trees and show the advantage of this approach with examples.

  1. Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability.

    Science.gov (United States)

    Lihe Zhang; Jianwu Ai; Bowen Jiang; Huchuan Lu; Xiukui Li

    2018-02-01

    In this paper, we propose a bottom-up saliency model based on absorbing Markov chain (AMC). First, a sparsely connected graph is constructed to capture the local context information of each node. All image boundary nodes and other nodes are, respectively, treated as the absorbing nodes and transient nodes in the absorbing Markov chain. Then, the expected number of times from each transient node to all other transient nodes can be used to represent the saliency value of this node. The absorbed time depends on the weights on the path and their spatial coordinates, which are completely encoded in the transition probability matrix. Considering the importance of this matrix, we adopt different hierarchies of deep features extracted from fully convolutional networks and learn a transition probability matrix, which is called learnt transition probability matrix. Although the performance is significantly promoted, salient objects are not uniformly highlighted very well. To solve this problem, an angular embedding technique is investigated to refine the saliency results. Based on pairwise local orderings, which are produced by the saliency maps of AMC and boundary maps, we rearrange the global orderings (saliency value) of all nodes. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art methods on six publicly available benchmark data sets.

  2. On the structure of the quantum-mechanical probability models

    International Nuclear Information System (INIS)

    Cufaro-Petroni, N.

    1992-01-01

    In this paper the role of the mathematical probability models in the classical and quantum physics in shortly analyzed. In particular the formal structure of the quantum probability spaces (QPS) is contrasted with the usual Kolmogorovian models of probability by putting in evidence the connections between this structure and the fundamental principles of the quantum mechanics. The fact that there is no unique Kolmogorovian model reproducing a QPS is recognized as one of the main reasons of the paradoxical behaviors pointed out in the quantum theory from its early days. 8 refs

  3. Pair-Wise Trajectory Management-Oceanic (PTM-O) . [Concept of Operations—Version 3.9

    Science.gov (United States)

    Jones, Kenneth M.

    2014-01-01

    This document describes the Pair-wise Trajectory Management-Oceanic (PTM-O) Concept of Operations (ConOps). Pair-wise Trajectory Management (PTM) is a concept that includes airborne and ground-based capabilities designed to enable and to benefit from, airborne pair-wise distance-monitoring capability. PTM includes the capabilities needed for the controller to issue a PTM clearance that resolves a conflict for a specific pair of aircraft. PTM avionics include the capabilities needed for the flight crew to manage their trajectory relative to specific designated aircraft. Pair-wise Trajectory Management PTM-Oceanic (PTM-O) is a regional specific application of the PTM concept. PTM is sponsored by the National Aeronautics and Space Administration (NASA) Concept and Technology Development Project (part of NASA's Airspace Systems Program). The goal of PTM is to use enhanced and distributed communications and surveillance along with airborne tools to permit reduced separation standards for given aircraft pairs, thereby increasing the capacity and efficiency of aircraft operations at a given altitude or volume of airspace.

  4. A decomposition of pairwise continuity via ideals

    Directory of Open Access Journals (Sweden)

    Mahes Wari

    2016-02-01

    Full Text Available In this paper, we introduce and study the notions of (i, j - regular - ℐ -closed sets, (i, j - Aℐ -sets, (i, j - ℐ -locally closed sets, p- Aℐ -continuous functions and p- ℐ -LC-continuous functions in ideal bitopological spaces and investigate some of their properties. Also, a new decomposition of pairwise continuity is obtained using these sets.

  5. Multiple model cardinalized probability hypothesis density filter

    Science.gov (United States)

    Georgescu, Ramona; Willett, Peter

    2011-09-01

    The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood of (an unlabeled) target state and the posterior probability mass function of the number of targets. Extensions of the PHD filter to the multiple model (MM) framework have been published and were implemented either with a Sequential Monte Carlo or a Gaussian Mixture approach. In this work, we introduce the multiple model version of the more elaborate CPHD filter. We present the derivation of the prediction and update steps of the MMCPHD particularized for the case of two target motion models and proceed to show that in the case of a single model, the new MMCPHD equations reduce to the original CPHD equations.

  6. Modeling experiments using quantum and Kolmogorov probability

    International Nuclear Information System (INIS)

    Hess, Karl

    2008-01-01

    Criteria are presented that permit a straightforward partition of experiments into sets that can be modeled using both quantum probability and the classical probability framework of Kolmogorov. These new criteria concentrate on the operational aspects of the experiments and lead beyond the commonly appreciated partition by relating experiments to commuting and non-commuting quantum operators as well as non-entangled and entangled wavefunctions. In other words the space of experiments that can be understood using classical probability is larger than usually assumed. This knowledge provides advantages for areas such as nanoscience and engineering or quantum computation.

  7. Models for probability and statistical inference theory and applications

    CERN Document Server

    Stapleton, James H

    2007-01-01

    This concise, yet thorough, book is enhanced with simulations and graphs to build the intuition of readersModels for Probability and Statistical Inference was written over a five-year period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.Ideal as a textbook for a two-semester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses mo...

  8. Protein distance constraints predicted by neural networks and probability density functions

    DEFF Research Database (Denmark)

    Lund, Ole; Frimand, Kenneth; Gorodkin, Jan

    1997-01-01

    We predict interatomic C-α distances by two independent data driven methods. The first method uses statistically derived probability distributions of the pairwise distance between two amino acids, whilst the latter method consists of a neural network prediction approach equipped with windows taki...... method based on the predicted distances is presented. A homepage with software, predictions and data related to this paper is available at http://www.cbs.dtu.dk/services/CPHmodels/...

  9. Perceptron learning of pairwise contact energies for proteins incorporating the amino acid environment

    Science.gov (United States)

    Heo, Muyoung; Kim, Suhkmann; Moon, Eun-Joung; Cheon, Mookyung; Chung, Kwanghoon; Chang, Iksoo

    2005-07-01

    Although a coarse-grained description of proteins is a simple and convenient way to attack the protein folding problem, the construction of a global pairwise energy function which can simultaneously recognize the native folds of many proteins has resulted in partial success. We have sought the possibility of a systematic improvement of this pairwise-contact energy function as we extended the parameter space of amino acids, incorporating local environments of amino acids, beyond a 20×20 matrix. We have studied the pairwise contact energy functions of 20×20 , 60×60 , and 180×180 matrices depending on the extent of parameter space, and compared their effect on the learnability of energy parameters in the context of a gapless threading, bearing in mind that a 20×20 pairwise contact matrix has been shown to be too simple to recognize the native folds of many proteins. In this paper, we show that the construction of a global pairwise energy function was achieved using 1006 training proteins of a homology of less than 30%, which include all representatives of different protein classes. After parametrizing the local environments of the amino acids into nine categories depending on three secondary structures and three kinds of hydrophobicity (desolvation), the 16290 pairwise contact energies (scores) of the amino acids could be determined by perceptron learning and protein threading. These could simultaneously recognize all the native folds of the 1006 training proteins. When these energy parameters were tested on the 382 test proteins of a homology of less than 90%, 370 (96.9%) proteins could recognize their native folds. We set up a simple thermodynamic framework in the conformational space of decoys to calculate the unfolded fraction and the specific heat of real proteins. The different thermodynamic stabilities of E.coli ribonuclease H (RNase H) and its mutants were well described in our calculation, agreeing with the experiment.

  10. Some simple applications of probability models to birth intervals

    International Nuclear Information System (INIS)

    Shrestha, G.

    1987-07-01

    An attempt has been made in this paper to apply some simple probability models to birth intervals under the assumption of constant fecundability and varying fecundability among women. The parameters of the probability models are estimated by using the method of moments and the method of maximum likelihood. (author). 9 refs, 2 tabs

  11. Modeling the probability distribution of peak discharge for infiltrating hillslopes

    Science.gov (United States)

    Baiamonte, Giorgio; Singh, Vijay P.

    2017-07-01

    Hillslope response plays a fundamental role in the prediction of peak discharge at the basin outlet. The peak discharge for the critical duration of rainfall and its probability distribution are needed for designing urban infrastructure facilities. This study derives the probability distribution, denoted as GABS model, by coupling three models: (1) the Green-Ampt model for computing infiltration, (2) the kinematic wave model for computing discharge hydrograph from the hillslope, and (3) the intensity-duration-frequency (IDF) model for computing design rainfall intensity. The Hortonian mechanism for runoff generation is employed for computing the surface runoff hydrograph. Since the antecedent soil moisture condition (ASMC) significantly affects the rate of infiltration, its effect on the probability distribution of peak discharge is investigated. Application to a watershed in Sicily, Italy, shows that with the increase of probability, the expected effect of ASMC to increase the maximum discharge diminishes. Only for low values of probability, the critical duration of rainfall is influenced by ASMC, whereas its effect on the peak discharge seems to be less for any probability. For a set of parameters, the derived probability distribution of peak discharge seems to be fitted by the gamma distribution well. Finally, an application to a small watershed, with the aim to test the possibility to arrange in advance the rational runoff coefficient tables to be used for the rational method, and a comparison between peak discharges obtained by the GABS model with those measured in an experimental flume for a loamy-sand soil were carried out.

  12. Modeling Spatial Dependence of Rainfall Extremes Across Multiple Durations

    Science.gov (United States)

    Le, Phuong Dong; Leonard, Michael; Westra, Seth

    2018-03-01

    Determining the probability of a flood event in a catchment given that another flood has occurred in a nearby catchment is useful in the design of infrastructure such as road networks that have multiple river crossings. These conditional flood probabilities can be estimated by calculating conditional probabilities of extreme rainfall and then transforming rainfall to runoff through a hydrologic model. Each catchment's hydrological response times are unlikely to be the same, so in order to estimate these conditional probabilities one must consider the dependence of extreme rainfall both across space and across critical storm durations. To represent these types of dependence, this study proposes a new approach for combining extreme rainfall across different durations within a spatial extreme value model using max-stable process theory. This is achieved in a stepwise manner. The first step defines a set of common parameters for the marginal distributions across multiple durations. The parameters are then spatially interpolated to develop a spatial field. Storm-level dependence is represented through the max-stable process for rainfall extremes across different durations. The dependence model shows a reasonable fit between the observed pairwise extremal coefficients and the theoretical pairwise extremal coefficient function across all durations. The study demonstrates how the approach can be applied to develop conditional maps of the return period and return level across different durations.

  13. Estimating the concordance probability in a survival analysis with a discrete number of risk groups.

    Science.gov (United States)

    Heller, Glenn; Mo, Qianxing

    2016-04-01

    A clinical risk classification system is an important component of a treatment decision algorithm. A measure used to assess the strength of a risk classification system is discrimination, and when the outcome is survival time, the most commonly applied global measure of discrimination is the concordance probability. The concordance probability represents the pairwise probability of lower patient risk given longer survival time. The c-index and the concordance probability estimate have been used to estimate the concordance probability when patient-specific risk scores are continuous. In the current paper, the concordance probability estimate and an inverse probability censoring weighted c-index are modified to account for discrete risk scores. Simulations are generated to assess the finite sample properties of the concordance probability estimate and the weighted c-index. An application of these measures of discriminatory power to a metastatic prostate cancer risk classification system is examined.

  14. Power and sample-size estimation for microbiome studies using pairwise distances and PERMANOVA

    OpenAIRE

    Kelly, Brendan J.; Gross, Robert; Bittinger, Kyle; Sherrill-Mix, Scott; Lewis, James D.; Collman, Ronald G.; Bushman, Frederic D.; Li, Hongzhe

    2015-01-01

    Motivation: The variation in community composition between microbiome samples, termed beta diversity, can be measured by pairwise distance based on either presence–absence or quantitative species abundance data. PERMANOVA, a permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances, partitions within-group and between-group distances to permit assessment of the effect of an exposure or intervention (grouping factor) upon the sampled microbiome. Within-...

  15. Pairwise Trajectory Management (PTM): Concept Description and Documentation

    Science.gov (United States)

    Jones, Kenneth M.; Graff, Thomas J.; Carreno, Victor; Chartrand, Ryan C.; Kibler, Jennifer L.

    2018-01-01

    Pairwise Trajectory Management (PTM) is an Interval Management (IM) concept that utilizes airborne and ground-based capabilities to enable the implementation of airborne pairwise spacing capabilities in oceanic regions. The goal of PTM is to use airborne surveillance and tools to manage an "at or greater than" inter-aircraft spacing. Due to the accuracy of Automatic Dependent Surveillance-Broadcast (ADS-B) information and the use of airborne spacing guidance, the minimum PTM spacing distance will be less than distances a controller can support with current automation systems that support oceanic operations. Ground tools assist the controller in evaluating the traffic picture and determining appropriate PTM clearances to be issued. Avionics systems provide guidance information that allows the flight crew to conform to the PTM clearance issued by the controller. The combination of a reduced minimum distance and airborne spacing management will increase the capacity and efficiency of aircraft operations at a given altitude or volume of airspace. This document provides an overview of the proposed application, a description of several key scenarios, a high level discussion of expected air and ground equipment and procedure changes, a description of a NASA human-machine interface (HMI) prototype for the flight crew that would support PTM operations, and initial benefits analysis results. Additionally, included as appendices, are the following documents: the PTM Operational Services and Environment Definition (OSED) document and a companion "Future Considerations for the Pairwise Trajectory Management (PTM) Concept: Potential Future Updates for the PTM OSED" paper, a detailed description of the PTM algorithm and PTM Limit Mach rules, initial PTM safety requirements and safety assessment documents, a detailed description of the design, development, and initial evaluations of the proposed flight crew HMI, an overview of the methodology and results of PTM pilot training

  16. Revision of Begomovirus taxonomy based on pairwise sequence comparisons

    KAUST Repository

    Brown, Judith K.; Zerbini, F. Murilo; Navas-Castillo, Jesú s; Moriones, Enrique; Ramos-Sobrinho, Roberto; Silva, José C. F.; Fiallo-Olivé , Elvira; Briddon, Rob W.; Herná ndez-Zepeda, Cecilia; Idris, Ali; Malathi, V. G.; Martin, Darren P.; Rivera-Bustamante, Rafael; Ueda, Shigenori; Varsani, Arvind

    2015-01-01

    Viruses of the genus Begomovirus (family Geminiviridae) are emergent pathogens of crops throughout the tropical and subtropical regions of the world. By virtue of having a small DNA genome that is easily cloned, and due to the recent innovations in cloning and low-cost sequencing, there has been a dramatic increase in the number of available begomovirus genome sequences. Even so, most of the available sequences have been obtained from cultivated plants and are likely a small and phylogenetically unrepresentative sample of begomovirus diversity, a factor constraining taxonomic decisions such as the establishment of operationally useful species demarcation criteria. In addition, problems in assigning new viruses to established species have highlighted shortcomings in the previously recommended mechanism of species demarcation. Based on the analysis of 3,123 full-length begomovirus genome (or DNA-A component) sequences available in public databases as of December 2012, a set of revised guidelines for the classification and nomenclature of begomoviruses are proposed. The guidelines primarily consider a) genus-level biological characteristics and b) results obtained using a standardized classification tool, Sequence Demarcation Tool, which performs pairwise sequence alignments and identity calculations. These guidelines are consistent with the recently published recommendations for the genera Mastrevirus and Curtovirus of the family Geminiviridae. Genome-wide pairwise identities of 91 % and 94 % are proposed as the demarcation threshold for begomoviruses belonging to different species and strains, respectively. Procedures and guidelines are outlined for resolving conflicts that may arise when assigning species and strains to categories wherever the pairwise identity falls on or very near the demarcation threshold value.

  17. Revision of Begomovirus taxonomy based on pairwise sequence comparisons

    KAUST Repository

    Brown, Judith K.

    2015-04-18

    Viruses of the genus Begomovirus (family Geminiviridae) are emergent pathogens of crops throughout the tropical and subtropical regions of the world. By virtue of having a small DNA genome that is easily cloned, and due to the recent innovations in cloning and low-cost sequencing, there has been a dramatic increase in the number of available begomovirus genome sequences. Even so, most of the available sequences have been obtained from cultivated plants and are likely a small and phylogenetically unrepresentative sample of begomovirus diversity, a factor constraining taxonomic decisions such as the establishment of operationally useful species demarcation criteria. In addition, problems in assigning new viruses to established species have highlighted shortcomings in the previously recommended mechanism of species demarcation. Based on the analysis of 3,123 full-length begomovirus genome (or DNA-A component) sequences available in public databases as of December 2012, a set of revised guidelines for the classification and nomenclature of begomoviruses are proposed. The guidelines primarily consider a) genus-level biological characteristics and b) results obtained using a standardized classification tool, Sequence Demarcation Tool, which performs pairwise sequence alignments and identity calculations. These guidelines are consistent with the recently published recommendations for the genera Mastrevirus and Curtovirus of the family Geminiviridae. Genome-wide pairwise identities of 91 % and 94 % are proposed as the demarcation threshold for begomoviruses belonging to different species and strains, respectively. Procedures and guidelines are outlined for resolving conflicts that may arise when assigning species and strains to categories wherever the pairwise identity falls on or very near the demarcation threshold value.

  18. Probability Modeling and Thinking: What Can We Learn from Practice?

    Science.gov (United States)

    Pfannkuch, Maxine; Budgett, Stephanie; Fewster, Rachel; Fitch, Marie; Pattenwise, Simeon; Wild, Chris; Ziedins, Ilze

    2016-01-01

    Because new learning technologies are enabling students to build and explore probability models, we believe that there is a need to determine the big enduring ideas that underpin probabilistic thinking and modeling. By uncovering the elements of the thinking modes of expert users of probability models we aim to provide a base for the setting of…

  19. Evaluation of nuclear power plant component failure probability and core damage probability using simplified PSA model

    International Nuclear Information System (INIS)

    Shimada, Yoshio

    2000-01-01

    It is anticipated that the change of frequency of surveillance tests, preventive maintenance or parts replacement of safety related components may cause the change of component failure probability and result in the change of core damage probability. It is also anticipated that the change is different depending on the initiating event frequency or the component types. This study assessed the change of core damage probability using simplified PSA model capable of calculating core damage probability in a short time period, which is developed by the US NRC to process accident sequence precursors, when various component's failure probability is changed between 0 and 1, or Japanese or American initiating event frequency data are used. As a result of the analysis, (1) It was clarified that frequency of surveillance test, preventive maintenance or parts replacement of motor driven pumps (high pressure injection pumps, residual heat removal pumps, auxiliary feedwater pumps) should be carefully changed, since the core damage probability's change is large, when the base failure probability changes toward increasing direction. (2) Core damage probability change is insensitive to surveillance test frequency change, since the core damage probability change is small, when motor operated valves and turbine driven auxiliary feed water pump failure probability changes around one figure. (3) Core damage probability change is small, when Japanese failure probability data are applied to emergency diesel generator, even if failure probability changes one figure from the base value. On the other hand, when American failure probability data is applied, core damage probability increase is large, even if failure probability changes toward increasing direction. Therefore, when Japanese failure probability data is applied, core damage probability change is insensitive to surveillance tests frequency change etc. (author)

  20. Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

    Science.gov (United States)

    Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

    2018-02-19

    Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

  1. On Probability Leakage

    OpenAIRE

    Briggs, William M.

    2012-01-01

    The probability leakage of model M with respect to evidence E is defined. Probability leakage is a kind of model error. It occurs when M implies that events $y$, which are impossible given E, have positive probability. Leakage does not imply model falsification. Models with probability leakage cannot be calibrated empirically. Regression models, which are ubiquitous in statistical practice, often evince probability leakage.

  2. Applied probability models with optimization applications

    CERN Document Server

    Ross, Sheldon M

    1992-01-01

    Concise advanced-level introduction to stochastic processes that frequently arise in applied probability. Largely self-contained text covers Poisson process, renewal theory, Markov chains, inventory theory, Brownian motion and continuous time optimization models, much more. Problems and references at chapter ends. ""Excellent introduction."" - Journal of the American Statistical Association. Bibliography. 1970 edition.

  3. Camera-Model Identification Using Markovian Transition Probability Matrix

    Science.gov (United States)

    Xu, Guanshuo; Gao, Shang; Shi, Yun Qing; Hu, Ruimin; Su, Wei

    Detecting the (brands and) models of digital cameras from given digital images has become a popular research topic in the field of digital forensics. As most of images are JPEG compressed before they are output from cameras, we propose to use an effective image statistical model to characterize the difference JPEG 2-D arrays of Y and Cb components from the JPEG images taken by various camera models. Specifically, the transition probability matrices derived from four different directional Markov processes applied to the image difference JPEG 2-D arrays are used to identify statistical difference caused by image formation pipelines inside different camera models. All elements of the transition probability matrices, after a thresholding technique, are directly used as features for classification purpose. Multi-class support vector machines (SVM) are used as the classification tool. The effectiveness of our proposed statistical model is demonstrated by large-scale experimental results.

  4. Probability theory for 3-layer remote sensing radiative transfer model: univariate case.

    Science.gov (United States)

    Ben-David, Avishai; Davidson, Charles E

    2012-04-23

    A probability model for a 3-layer radiative transfer model (foreground layer, cloud layer, background layer, and an external source at the end of line of sight) has been developed. The 3-layer model is fundamentally important as the primary physical model in passive infrared remote sensing. The probability model is described by the Johnson family of distributions that are used as a fit for theoretically computed moments of the radiative transfer model. From the Johnson family we use the SU distribution that can address a wide range of skewness and kurtosis values (in addition to addressing the first two moments, mean and variance). In the limit, SU can also describe lognormal and normal distributions. With the probability model one can evaluate the potential for detecting a target (vapor cloud layer), the probability of observing thermal contrast, and evaluate performance (receiver operating characteristics curves) in clutter-noise limited scenarios. This is (to our knowledge) the first probability model for the 3-layer remote sensing geometry that treats all parameters as random variables and includes higher-order statistics. © 2012 Optical Society of America

  5. Sampling, Probability Models and Statistical Reasoning Statistical

    Indian Academy of Sciences (India)

    Home; Journals; Resonance – Journal of Science Education; Volume 1; Issue 5. Sampling, Probability Models and Statistical Reasoning Statistical Inference. Mohan Delampady V R Padmawar. General Article Volume 1 Issue 5 May 1996 pp 49-58 ...

  6. A probability model for the failure of pressure containing parts

    International Nuclear Information System (INIS)

    Thomas, H.M.

    1978-01-01

    The model provides a method of estimating the order of magnitude of the leakage failure probability of pressure containing parts. It is a fatigue based model which makes use of the statistics available for both specimens and vessels. Some novel concepts are introduced but essentially the model simply quantifies the obvious i.e. that failure probability increases with increases in stress levels, number of cycles, volume of material and volume of weld metal. A further model based on fracture mechanics estimates the catastrophic fraction of leakage failures. (author)

  7. Gap probability - Measurements and models of a pecan orchard

    Science.gov (United States)

    Strahler, Alan H.; Li, Xiaowen; Moody, Aaron; Liu, YI

    1992-01-01

    Measurements and models are compared for gap probability in a pecan orchard. Measurements are based on panoramic photographs of 50* by 135 view angle made under the canopy looking upwards at regular positions along transects between orchard trees. The gap probability model is driven by geometric parameters at two levels-crown and leaf. Crown level parameters include the shape of the crown envelope and spacing of crowns; leaf level parameters include leaf size and shape, leaf area index, and leaf angle, all as functions of canopy position.

  8. Developing a probability-based model of aquifer vulnerability in an agricultural region

    Science.gov (United States)

    Chen, Shih-Kai; Jang, Cheng-Shin; Peng, Yi-Huei

    2013-04-01

    SummaryHydrogeological settings of aquifers strongly influence the regional groundwater movement and pollution processes. Establishing a map of aquifer vulnerability is considerably critical for planning a scheme of groundwater quality protection. This study developed a novel probability-based DRASTIC model of aquifer vulnerability in the Choushui River alluvial fan, Taiwan, using indicator kriging and to determine various risk categories of contamination potentials based on estimated vulnerability indexes. Categories and ratings of six parameters in the probability-based DRASTIC model were probabilistically characterized according to the parameter classification methods of selecting a maximum estimation probability and calculating an expected value. Moreover, the probability-based estimation and assessment gave us an excellent insight into propagating the uncertainty of parameters due to limited observation data. To examine the prediction capacity of pollutants for the developed probability-based DRASTIC model, medium, high, and very high risk categories of contamination potentials were compared with observed nitrate-N exceeding 0.5 mg/L indicating the anthropogenic groundwater pollution. The analyzed results reveal that the developed probability-based DRASTIC model is capable of predicting high nitrate-N groundwater pollution and characterizing the parameter uncertainty via the probability estimation processes.

  9. The Probability Model of Expectation Disconfirmation Process

    Directory of Open Access Journals (Sweden)

    Hui-Hsin HUANG

    2015-06-01

    Full Text Available This paper proposes a probability model to explore the dynamic process of customer’s satisfaction. Bases on expectation disconfirmation theory, the satisfaction is constructed with customer’s expectation before buying behavior and the perceived performance after purchase. The experiment method is designed to measure expectation disconfirmation effects and we also use the collection data to estimate the overall satisfaction and model calibration. The results show good fitness between the model and the real data. This model has application for business marketing areas in order to manage relationship satisfaction.

  10. Improving pairwise comparison of protein sequences with domain co-occurrence

    Science.gov (United States)

    Gascuel, Olivier

    2018-01-01

    Comparing and aligning protein sequences is an essential task in bioinformatics. More specifically, local alignment tools like BLAST are widely used for identifying conserved protein sub-sequences, which likely correspond to protein domains or functional motifs. However, to limit the number of false positives, these tools are used with stringent sequence-similarity thresholds and hence can miss several hits, especially for species that are phylogenetically distant from reference organisms. A solution to this problem is then to integrate additional contextual information to the procedure. Here, we propose to use domain co-occurrence to increase the sensitivity of pairwise sequence comparisons. Domain co-occurrence is a strong feature of proteins, since most protein domains tend to appear with a limited number of other domains on the same protein. We propose a method to take this information into account in a typical BLAST analysis and to construct new domain families on the basis of these results. We used Plasmodium falciparum as a case study to evaluate our method. The experimental findings showed an increase of 14% of the number of significant BLAST hits and an increase of 25% of the proteome area that can be covered with a domain. Our method identified 2240 new domains for which, in most cases, no model of the Pfam database could be linked. Moreover, our study of the quality of the new domains in terms of alignment and physicochemical properties show that they are close to that of standard Pfam domains. Source code of the proposed approach and supplementary data are available at: https://gite.lirmm.fr/menichelli/pairwise-comparison-with-cooccurrence PMID:29293498

  11. Sampling, Probability Models and Statistical Reasoning -RE ...

    Indian Academy of Sciences (India)

    random sampling allows data to be modelled with the help of probability ... g based on different trials to get an estimate of the experimental error. ... research interests lie in the .... if e is indeed the true value of the proportion of defectives in the.

  12. Pairwise harmonics for shape analysis

    KAUST Repository

    Zheng, Youyi

    2013-07-01

    This paper introduces a simple yet effective shape analysis mechanism for geometry processing. Unlike traditional shape analysis techniques which compute descriptors per surface point up to certain neighborhoods, we introduce a shape analysis framework in which the descriptors are based on pairs of surface points. Such a pairwise analysis approach leads to a new class of shape descriptors that are more global, discriminative, and can effectively capture the variations in the underlying geometry. Specifically, we introduce new shape descriptors based on the isocurves of harmonic functions whose global maximum and minimum occur at the point pair. We show that these shape descriptors can infer shape structures and consistently lead to simpler and more efficient algorithms than the state-of-the-art methods for three applications: intrinsic reflectional symmetry axis computation, matching shape extremities, and simultaneous surface segmentation and skeletonization. © 2012 IEEE.

  13. Knock probability estimation through an in-cylinder temperature model with exogenous noise

    Science.gov (United States)

    Bares, P.; Selmanaj, D.; Guardiola, C.; Onder, C.

    2018-01-01

    This paper presents a new knock model which combines a deterministic knock model based on the in-cylinder temperature and an exogenous noise disturbing this temperature. The autoignition of the end-gas is modelled by an Arrhenius-like function and the knock probability is estimated by propagating a virtual error probability distribution. Results show that the random nature of knock can be explained by uncertainties at the in-cylinder temperature estimation. The model only has one parameter for calibration and thus can be easily adapted online. In order to reduce the measurement uncertainties associated with the air mass flow sensor, the trapped mass is derived from the in-cylinder pressure resonance, which improves the knock probability estimation and reduces the number of sensors needed for the model. A four stroke SI engine was used for model validation. By varying the intake temperature, the engine speed, the injected fuel mass, and the spark advance, specific tests were conducted, which furnished data with various knock intensities and probabilities. The new model is able to predict the knock probability within a sufficient range at various operating conditions. The trapped mass obtained by the acoustical model was compared in steady conditions by using a fuel balance and a lambda sensor and differences below 1 % were found.

  14. The Probability Heuristics Model of Syllogistic Reasoning.

    Science.gov (United States)

    Chater, Nick; Oaksford, Mike

    1999-01-01

    Proposes a probability heuristic model for syllogistic reasoning and confirms the rationality of this heuristic by an analysis of the probabilistic validity of syllogistic reasoning that treats logical inference as a limiting case of probabilistic inference. Meta-analysis and two experiments involving 40 adult participants and using generalized…

  15. Detection of the pairwise kinematic Sunyaev-Zel'dovich effect with BOSS DR11 and the Atacama Cosmology Telescope

    Energy Technology Data Exchange (ETDEWEB)

    Bernardis, F. De; Vavagiakis, E.M.; Niemack, M.D.; Gallardo, P.A. [Department of Physics, Cornell University, Ithaca, NY 14853 (United States); Aiola, S. [Department of Physics and Astronomy, University of Pittsburgh, and Pittsburgh Particle Physics, Astrophysics, and Cosmology Center, 3941 O' Hara Street, Pittsburgh, PA 15260 (United States); Battaglia, N. [Department of Astrophysical Sciences, Peyton Hall, Princeton University, Princeton, NJ 08544 (United States); Beall, J.; Becker, D.T.; Cho, H.; Fox, A. [National Institute of Standards and Technology, Boulder, CO 80305 (United States); Bond, J.R. [CITA, University of Toronto, 60 St. George St., Toronto, ON M5S 3H8 (Canada); Calabrese, E.; Dunkley, J. [Sub-Department of Astrophysics, University of Oxford, Keble Road, Oxford, OX1 3RH (United Kingdom); Coughlin, K.; Datta, R. [Department of Physics, University of Michigan Ann Arbor, MI 48109 (United States); Devlin, M. [Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104 (United States); Dunner, R. [Instituto de Astrofísica and Centro de Astro-Ingeniería, Facultad de Física, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna 4860, 7820436 Macul, Santiago (Chile); Ferraro, S. [Miller Institute for Basic Research in Science, University of California, Berkeley, CA 94720 (United States); Halpern, M. [University of British Columbia, Department of Physics and Astronomy, 6224 Agricultural Road, Vancouver BC V6T 1Z1 (Canada); Hand, N., E-mail: fdeberna@gmail.com [Astronomy Department, University of California, Berkeley, CA 94720 (United States); and others

    2017-03-01

    We present a new measurement of the kinematic Sunyaev-Zel'dovich effect using data from the Atacama Cosmology Telescope (ACT) and the Baryon Oscillation Spectroscopic Survey (BOSS). Using 600 square degrees of overlapping sky area, we evaluate the mean pairwise baryon momentum associated with the positions of 50,000 bright galaxies in the BOSS DR11 Large Scale Structure catalog. A non-zero signal arises from the large-scale motions of halos containing the sample galaxies. The data fits an analytical signal model well, with the optical depth to microwave photon scattering as a free parameter determining the overall signal amplitude. We estimate the covariance matrix of the mean pairwise momentum as a function of galaxy separation, using microwave sky simulations, jackknife evaluation, and bootstrap estimates. The most conservative simulation-based errors give signal-to-noise estimates between 3.6 and 4.1 for varying galaxy luminosity cuts. We discuss how the other error determinations can lead to higher signal-to-noise values, and consider the impact of several possible systematic errors. Estimates of the optical depth from the average thermal Sunyaev-Zel'dovich signal at the sample galaxy positions are broadly consistent with those obtained from the mean pairwise momentum signal.

  16. A gradient approximation for calculating Debye temperatures from pairwise interatomic potentials

    International Nuclear Information System (INIS)

    Jackson, D.P.

    1975-09-01

    A simple gradient approximation is given for calculating the effective Debye temperature of a cubic crystal from central pairwise interatomic potentials. For examples of the Morse potential applied to cubic metals the results are in generally good agreement with experiment. (author)

  17. Probability and stochastic modeling

    CERN Document Server

    Rotar, Vladimir I

    2012-01-01

    Basic NotionsSample Space and EventsProbabilitiesCounting TechniquesIndependence and Conditional ProbabilityIndependenceConditioningThe Borel-Cantelli TheoremDiscrete Random VariablesRandom Variables and VectorsExpected ValueVariance and Other Moments. Inequalities for DeviationsSome Basic DistributionsConvergence of Random Variables. The Law of Large NumbersConditional ExpectationGenerating Functions. Branching Processes. Random Walk RevisitedBranching Processes Generating Functions Branching Processes Revisited More on Random WalkMarkov ChainsDefinitions and Examples. Probability Distributions of Markov ChainsThe First Step Analysis. Passage TimesVariables Defined on a Markov ChainErgodicity and Stationary DistributionsA Classification of States and ErgodicityContinuous Random VariablesContinuous DistributionsSome Basic Distributions Continuous Multivariate Distributions Sums of Independent Random Variables Conditional Distributions and ExpectationsDistributions in the General Case. SimulationDistribution F...

  18. NASA Lewis Launch Collision Probability Model Developed and Analyzed

    Science.gov (United States)

    Bollenbacher, Gary; Guptill, James D

    1999-01-01

    There are nearly 10,000 tracked objects orbiting the earth. These objects encompass manned objects, active and decommissioned satellites, spent rocket bodies, and debris. They range from a few centimeters across to the size of the MIR space station. Anytime a new satellite is launched, the launch vehicle with its payload attached passes through an area of space in which these objects orbit. Although the population density of these objects is low, there always is a small but finite probability of collision between the launch vehicle and one or more of these space objects. Even though the probability of collision is very low, for some payloads even this small risk is unacceptable. To mitigate the small risk of collision associated with launching at an arbitrary time within the daily launch window, NASA performs a prelaunch mission assurance Collision Avoidance Analysis (or COLA). For the COLA of the Cassini spacecraft, the NASA Lewis Research Center conducted an in-house development and analysis of a model for launch collision probability. The model allows a minimum clearance criteria to be used with the COLA analysis to ensure an acceptably low probability of collision. If, for any given liftoff time, the nominal launch vehicle trajectory would pass a space object with less than the minimum required clearance, launch would not be attempted at that time. The model assumes that the nominal positions of the orbiting objects and of the launch vehicle can be predicted as a function of time, and therefore, that any tracked object that comes within close proximity of the launch vehicle can be identified. For any such pair, these nominal positions can be used to calculate a nominal miss distance. The actual miss distances may differ substantially from the nominal miss distance, due, in part, to the statistical uncertainty of the knowledge of the objects positions. The model further assumes that these position uncertainties can be described with position covariance matrices

  19. Extension of Pairwise Broadcast Clock Synchronization for Multicluster Sensor Networks

    Directory of Open Access Journals (Sweden)

    Bruce W. Suter

    2008-01-01

    Full Text Available Time synchronization is crucial for wireless sensor networks (WSNs in performing a number of fundamental operations such as data coordination, power management, security, and localization. The Pairwise Broadcast Synchronization (PBS protocol was recently proposed to minimize the number of timing messages required for global network synchronization, which enables the design of highly energy-efficient WSNs. However, PBS requires all nodes in the network to lie within the communication ranges of two leader nodes, a condition which might not be available in some applications. This paper proposes an extension of PBS to the more general class of sensor networks. Based on the hierarchical structure of the network, an energy-efficient pair selection algorithm is proposed to select the best pairwise synchronization sequence to reduce the overall energy consumption. It is shown that in a multicluster networking environment, PBS requires a far less number of timing messages than other well-known synchronization protocols and incurs no loss in synchronization accuracy. Moreover, the proposed scheme presents significant energy savings for densely deployed WSNs.

  20. Probability model for analyzing fire management alternatives: theory and structure

    Science.gov (United States)

    Frederick W. Bratten

    1982-01-01

    A theoretical probability model has been developed for analyzing program alternatives in fire management. It includes submodels or modules for predicting probabilities of fire behavior, fire occurrence, fire suppression, effects of fire on land resources, and financial effects of fire. Generalized "fire management situations" are used to represent actual fire...

  1. A Relative-Localization Algorithm Using Incomplete Pairwise Distance Measurements for Underwater Applications

    Directory of Open Access Journals (Sweden)

    Kae Y. Foo

    2010-01-01

    Full Text Available The task of localizing underwater assets involves the relative localization of each unit using only pairwise distance measurements, usually obtained from time-of-arrival or time-delay-of-arrival measurements. In the fluctuating underwater environment, a complete set of pair-wise distance measurements can often be difficult to acquire, thus hindering a straightforward closed-form solution in deriving the assets' relative coordinates. An iterative multidimensional scaling approach is presented based upon a weighted-majorization algorithm that tolerates missing or inaccurate distance measurements. Substantial modifications are proposed to optimize the algorithm, while the effects of refractive propagation paths are considered. A parametric study of the algorithm based upon simulation results is shown. An acoustic field-trial was then carried out, presenting field measurements to highlight the practical implementation of this algorithm.

  2. Fixation probability in a two-locus intersexual selection model.

    Science.gov (United States)

    Durand, Guillermo; Lessard, Sabin

    2016-06-01

    We study a two-locus model of intersexual selection in a finite haploid population reproducing according to a discrete-time Moran model with a trait locus expressed in males and a preference locus expressed in females. We show that the probability of ultimate fixation of a single mutant allele for a male ornament introduced at random at the trait locus given any initial frequency state at the preference locus is increased by weak intersexual selection and recombination, weak or strong. Moreover, this probability exceeds the initial frequency of the mutant allele even in the case of a costly male ornament if intersexual selection is not too weak. On the other hand, the probability of ultimate fixation of a single mutant allele for a female preference towards a male ornament introduced at random at the preference locus is increased by weak intersexual selection and weak recombination if the female preference is not costly, and is strong enough in the case of a costly male ornament. The analysis relies on an extension of the ancestral recombination-selection graph for samples of haplotypes to take into account events of intersexual selection, while the symbolic calculation of the fixation probabilities is made possible in a reasonable time by an optimizing algorithm. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. Statistical validation of normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; van t Veld, Aart; Langendijk, Johannes A.; Schilstra, Cornelis

    2012-01-01

    PURPOSE: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: A penalized regression method, LASSO (least absolute shrinkage

  4. Measuring pair-wise molecular interactions in a complex mixture

    Science.gov (United States)

    Chakraborty, Krishnendu; Varma, Manoj M.; Venkatapathi, Murugesan

    2016-03-01

    Complex biological samples such as serum contain thousands of proteins and other molecules spanning up to 13 orders of magnitude in concentration. Present measurement techniques do not permit the analysis of all pair-wise interactions between the components of such a complex mixture to a given target molecule. In this work we explore the use of nanoparticle tags which encode the identity of the molecule to obtain the statistical distribution of pair-wise interactions using their Localized Surface Plasmon Resonance (LSPR) signals. The nanoparticle tags are chosen such that the binding between two molecules conjugated to the respective nanoparticle tags can be recognized by the coupling of their LSPR signals. This numerical simulation is done by DDA to investigate this approach using a reduced system consisting of three nanoparticles (a gold ellipsoid with aspect ratio 2.5 and short axis 16 nm, and two silver ellipsoids with aspect ratios 3 and 2 and short axes 8 nm and 10 nm respectively) and the set of all possible dimers formed between them. Incident light was circularly polarized and all possible particle and dimer orientations were considered. We observed that minimum peak separation between two spectra is 5 nm while maximum is 184nm.

  5. Multilevel summation methods for efficient evaluation of long-range pairwise interactions in atomistic and coarse-grained molecular simulation.

    Energy Technology Data Exchange (ETDEWEB)

    Bond, Stephen D.

    2014-01-01

    The availability of efficient algorithms for long-range pairwise interactions is central to the success of numerous applications, ranging in scale from atomic-level modeling of materials to astrophysics. This report focuses on the implementation and analysis of the multilevel summation method for approximating long-range pairwise interactions. The computational cost of the multilevel summation method is proportional to the number of particles, N, which is an improvement over FFTbased methods whos cost is asymptotically proportional to N logN. In addition to approximating electrostatic forces, the multilevel summation method can be use to efficiently approximate convolutions with long-range kernels. As an application, we apply the multilevel summation method to a discretized integral equation formulation of the regularized generalized Poisson equation. Numerical results are presented using an implementation of the multilevel summation method in the LAMMPS software package. Preliminary results show that the computational cost of the method scales as expected, but there is still a need for further optimization.

  6. Estimation and asymptotic theory for transition probabilities in Markov Renewal Multi–state models

    NARCIS (Netherlands)

    Spitoni, C.; Verduijn, M.; Putter, H.

    2012-01-01

    In this paper we discuss estimation of transition probabilities for semi–Markov multi–state models. Non–parametric and semi–parametric estimators of the transition probabilities for a large class of models (forward going models) are proposed. Large sample theory is derived using the functional

  7. Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study

    Science.gov (United States)

    Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias

    2018-01-01

    Abstract Objective To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) (“living” network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Design Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Data sources Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Eligibility criteria for study selection Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (Pmeta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. Results 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing

  8. Using multinomial and imprecise probability for non-parametric modelling of rainfall in Manizales (Colombia

    Directory of Open Access Journals (Sweden)

    Ibsen Chivatá Cárdenas

    2008-05-01

    Full Text Available This article presents a rainfall model constructed by applying non-parametric modelling and imprecise probabilities; these tools were used because there was not enough homogeneous information in the study area. The area’s hydro-logical information regarding rainfall was scarce and existing hydrological time series were not uniform. A distributed extended rainfall model was constructed from so-called probability boxes (p-boxes, multinomial probability distribu-tion and confidence intervals (a friendly algorithm was constructed for non-parametric modelling by combining the last two tools. This model confirmed the high level of uncertainty involved in local rainfall modelling. Uncertainty en-compassed the whole range (domain of probability values thereby showing the severe limitations on information, leading to the conclusion that a detailed estimation of probability would lead to significant error. Nevertheless, rele-vant information was extracted; it was estimated that maximum daily rainfall threshold (70 mm would be surpassed at least once every three years and the magnitude of uncertainty affecting hydrological parameter estimation. This paper’s conclusions may be of interest to non-parametric modellers and decisions-makers as such modelling and imprecise probability represents an alternative for hydrological variable assessment and maybe an obligatory proce-dure in the future. Its potential lies in treating scarce information and represents a robust modelling strategy for non-seasonal stochastic modelling conditions

  9. Image ranking in video sequences using pairwise image comparisons and temporal smoothing

    CSIR Research Space (South Africa)

    Burke, Michael

    2016-12-01

    Full Text Available The ability to predict the importance of an image is highly desirable in computer vision. This work introduces an image ranking scheme suitable for use in video or image sequences. Pairwise image comparisons are used to determine image ‘interest...

  10. A stochastic-bayesian model for the fracture probability of PWR pressure vessels

    Energy Technology Data Exchange (ETDEWEB)

    Francisco, Alexandre S.; Duran, Jorge Alberto R., E-mail: afrancisco@metal.eeimvr.uff.br, E-mail: duran@metal.eeimvr.uff.br [Universidade Federal Fluminense (UFF), Volta Redonda, RJ (Brazil). Dept. de Engenharia Mecanica

    2013-07-01

    Fracture probability of pressure vessels containing cracks can be obtained by methodologies of easy understanding, which require a deterministic treatment, complemented by statistical methods. However, more accurate results are required, methodologies need to be better formulated. This paper presents a new methodology to address this problem. First, a more rigorous methodology is obtained by means of the relationship of probability distributions that model crack incidence and nondestructive inspection efficiency using the Bayes' theorem. The result is an updated crack incidence distribution. Further, the accuracy of the methodology is improved by using a stochastic model for the crack growth. The stochastic model incorporates the statistical variability of the crack growth process, combining the stochastic theory with experimental data. Stochastic differential equations are derived by the randomization of empirical equations. From the solution of this equation, a distribution function related to the crack growth is derived. The fracture probability using both probability distribution functions is in agreement with theory, and presents realistic value for pressure vessels. (author)

  11. A stochastic-bayesian model for the fracture probability of PWR pressure vessels

    International Nuclear Information System (INIS)

    Francisco, Alexandre S.; Duran, Jorge Alberto R.

    2013-01-01

    Fracture probability of pressure vessels containing cracks can be obtained by methodologies of easy understanding, which require a deterministic treatment, complemented by statistical methods. However, more accurate results are required, methodologies need to be better formulated. This paper presents a new methodology to address this problem. First, a more rigorous methodology is obtained by means of the relationship of probability distributions that model crack incidence and nondestructive inspection efficiency using the Bayes' theorem. The result is an updated crack incidence distribution. Further, the accuracy of the methodology is improved by using a stochastic model for the crack growth. The stochastic model incorporates the statistical variability of the crack growth process, combining the stochastic theory with experimental data. Stochastic differential equations are derived by the randomization of empirical equations. From the solution of this equation, a distribution function related to the crack growth is derived. The fracture probability using both probability distribution functions is in agreement with theory, and presents realistic value for pressure vessels. (author)

  12. Scaling Qualitative Probability

    OpenAIRE

    Burgin, Mark

    2017-01-01

    There are different approaches to qualitative probability, which includes subjective probability. We developed a representation of qualitative probability based on relational systems, which allows modeling uncertainty by probability structures and is more coherent than existing approaches. This setting makes it possible proving that any comparative probability is induced by some probability structure (Theorem 2.1), that classical probability is a probability structure (Theorem 2.2) and that i...

  13. Dynamics of pairwise entanglement between two Tavis-Cummings atoms

    International Nuclear Information System (INIS)

    Guo Jinliang; Song Heshan

    2008-01-01

    We investigate the time evolution of pairwise entanglement between two Tavis-Cummings atoms for various entangled initial states, including pure and mixed states. We find that the phenomenon of entanglement sudden death behaviors is distinct in the evolution of entanglement for different initial states. What deserves mentioning here is that the initial portion of the excited state in the initial state is responsible for the sudden death of entanglement, and the degree of this effect also depends on the initial states

  14. Probabilistic Inference: Task Dependency and Individual Differences of Probability Weighting Revealed by Hierarchical Bayesian Modeling.

    Science.gov (United States)

    Boos, Moritz; Seer, Caroline; Lange, Florian; Kopp, Bruno

    2016-01-01

    Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modeling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities) by two (likelihoods) design. Five computational models of cognitive processes were compared with the observed behavior. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted) S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model's success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modeling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modeling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision.

  15. Statistical models based on conditional probability distributions

    International Nuclear Information System (INIS)

    Narayanan, R.S.

    1991-10-01

    We present a formulation of statistical mechanics models based on conditional probability distribution rather than a Hamiltonian. We show that it is possible to realize critical phenomena through this procedure. Closely linked with this formulation is a Monte Carlo algorithm, in which a configuration generated is guaranteed to be statistically independent from any other configuration for all values of the parameters, in particular near the critical point. (orig.)

  16. Visualization of pairwise and multilocus linkage disequilibrium structure using latent forests.

    Directory of Open Access Journals (Sweden)

    Raphaël Mourad

    Full Text Available Linkage disequilibrium study represents a major issue in statistical genetics as it plays a fundamental role in gene mapping and helps us to learn more about human history. The linkage disequilibrium complex structure makes its exploratory data analysis essential yet challenging. Visualization methods, such as the triangular heat map implemented in Haploview, provide simple and useful tools to help understand complex genetic patterns, but remain insufficient to fully describe them. Probabilistic graphical models have been widely recognized as a powerful formalism allowing a concise and accurate modeling of dependences between variables. In this paper, we propose a method for short-range, long-range and chromosome-wide linkage disequilibrium visualization using forests of hierarchical latent class models. Thanks to its hierarchical nature, our method is shown to provide a compact view of both pairwise and multilocus linkage disequilibrium spatial structures for the geneticist. Besides, a multilocus linkage disequilibrium measure has been designed to evaluate linkage disequilibrium in hierarchy clusters. To learn the proposed model, a new scalable algorithm is presented. It constrains the dependence scope, relying on physical positions, and is able to deal with more than one hundred thousand single nucleotide polymorphisms. The proposed algorithm is fast and does not require phase genotypic data.

  17. Simultaneous-Fault Diagnosis of Gas Turbine Generator Systems Using a Pairwise-Coupled Probabilistic Classifier

    Directory of Open Access Journals (Sweden)

    Zhixin Yang

    2013-01-01

    Full Text Available A reliable fault diagnostic system for gas turbine generator system (GTGS, which is complicated and inherent with many types of component faults, is essential to avoid the interruption of electricity supply. However, the GTGS diagnosis faces challenges in terms of the existence of simultaneous-fault diagnosis and high cost in acquiring the exponentially increased simultaneous-fault vibration signals for constructing the diagnostic system. This research proposes a new diagnostic framework combining feature extraction, pairwise-coupled probabilistic classifier, and decision threshold optimization. The feature extraction module adopts wavelet packet transform and time-domain statistical features to extract vibration signal features. Kernel principal component analysis is then applied to further reduce the redundant features. The features of single faults in a simultaneous-fault pattern are extracted and then detected using a probabilistic classifier, namely, pairwise-coupled relevance vector machine, which is trained with single-fault patterns only. Therefore, the training dataset of simultaneous-fault patterns is unnecessary. To optimize the decision threshold, this research proposes to use grid search method which can ensure a global solution as compared with traditional computational intelligence techniques. Experimental results show that the proposed framework performs well for both single-fault and simultaneous-fault diagnosis and is superior to the frameworks without feature extraction and pairwise coupling.

  18. Interpretations of probability

    CERN Document Server

    Khrennikov, Andrei

    2009-01-01

    This is the first fundamental book devoted to non-Kolmogorov probability models. It provides a mathematical theory of negative probabilities, with numerous applications to quantum physics, information theory, complexity, biology and psychology. The book also presents an interesting model of cognitive information reality with flows of information probabilities, describing the process of thinking, social, and psychological phenomena.

  19. Ruin probability of the renewal model with risky investment and large claims

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    The ruin probability of the renewal risk model with investment strategy for a capital market index is investigated in this paper.For claim sizes with common distribution of extended regular variation,we study the asymptotic behaviour of the ruin probability.As a corollary,we establish a simple asymptotic formula for the ruin probability for the case of Pareto-like claims.

  20. Review of Literature for Model Assisted Probability of Detection

    Energy Technology Data Exchange (ETDEWEB)

    Meyer, Ryan M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Crawford, Susan L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lareau, John P. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Anderson, Michael T. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2014-09-30

    This is a draft technical letter report for NRC client documenting a literature review of model assisted probability of detection (MAPOD) for potential application to nuclear power plant components for improvement of field NDE performance estimations.

  1. AllerHunter: a SVM-pairwise system for assessment of allergenicity and allergic cross-reactivity in proteins.

    Directory of Open Access Journals (Sweden)

    Hon Cheng Muh

    Full Text Available Allergy is a major health problem in industrialized countries. The number of transgenic food crops is growing rapidly creating the need for allergenicity assessment before they are introduced into human food chain. While existing bioinformatic methods have achieved good accuracies for highly conserved sequences, the discrimination of allergens and non-allergens from allergen-like non-allergen sequences remains difficult. We describe AllerHunter, a web-based computational system for the assessment of potential allergenicity and allergic cross-reactivity in proteins. It combines an iterative pairwise sequence similarity encoding scheme with SVM as the discriminating engine. The pairwise vectorization framework allows the system to model essential features in allergens that are involved in cross-reactivity, but not limited to distinct sets of physicochemical properties. The system was rigorously trained and tested using 1,356 known allergen and 13,449 putative non-allergen sequences. Extensive testing was performed for validation of the prediction models. The system is effective for distinguishing allergens and non-allergens from allergen-like non-allergen sequences. Testing results showed that AllerHunter, with a sensitivity of 83.4% and specificity of 96.4% (accuracy = 95.3%, area under the receiver operating characteristic curve AROC = 0.928+/-0.004 and Matthew's correlation coefficient MCC = 0.738, performs significantly better than a number of existing methods using an independent dataset of 1443 protein sequences. AllerHunter is available at (http://tiger.dbs.nus.edu.sg/AllerHunter.

  2. Bayesian probability analysis: a prospective demonstration of its clinical utility in diagnosing coronary disease

    International Nuclear Information System (INIS)

    Detrano, R.; Yiannikas, J.; Salcedo, E.E.; Rincon, G.; Go, R.T.; Williams, G.; Leatherman, J.

    1984-01-01

    One hundred fifty-four patients referred for coronary arteriography were prospectively studied with stress electrocardiography, stress thallium scintigraphy, cine fluoroscopy (for coronary calcifications), and coronary angiography. Pretest probabilities of coronary disease were determined based on age, sex, and type of chest pain. These and pooled literature values for the conditional probabilities of test results based on disease state were used in Bayes theorem to calculate posttest probabilities of disease. The results of the three noninvasive tests were compared for statistical independence, a necessary condition for their simultaneous use in Bayes theorem. The test results were found to demonstrate pairwise independence in patients with and those without disease. Some dependencies that were observed between the test results and the clinical variables of age and sex were not sufficient to invalidate application of the theorem. Sixty-eight of the study patients had at least one major coronary artery obstruction of greater than 50%. When these patients were divided into low-, intermediate-, and high-probability subgroups according to their pretest probabilities, noninvasive test results analyzed by Bayesian probability analysis appropriately advanced 17 of them by at least one probability subgroup while only seven were moved backward. Of the 76 patients without disease, 34 were appropriately moved into a lower probability subgroup while 10 were incorrectly moved up. We conclude that posttest probabilities calculated from Bayes theorem more accurately classified patients with and without disease than did pretest probabilities, thus demonstrating the utility of the theorem in this application

  3. GapMis: a tool for pairwise sequence alignment with a single gap.

    Science.gov (United States)

    Flouri, Tomás; Frousios, Kimon; Iliopoulos, Costas S; Park, Kunsoo; Pissis, Solon P; Tischler, German

    2013-08-01

    Pairwise sequence alignment has received a new motivation due to the advent of recent patents in next-generation sequencing technologies, particularly so for the application of re-sequencing---the assembly of a genome directed by a reference sequence. After the fast alignment between a factor of the reference sequence and a high-quality fragment of a short read by a short-read alignment programme, an important problem is to find the alignment between a relatively short succeeding factor of the reference sequence and the remaining low-quality part of the read allowing a number of mismatches and the insertion of a single gap in the alignment. We present GapMis, a tool for pairwise sequence alignment with a single gap. It is based on a simple algorithm, which computes a different version of the traditional dynamic programming matrix. The presented experimental results demonstrate that GapMis is more suitable and efficient than most popular tools for this task.

  4. Interpretation of the results of statistical measurements. [search for basic probability model

    Science.gov (United States)

    Olshevskiy, V. V.

    1973-01-01

    For random processes, the calculated probability characteristic, and the measured statistical estimate are used in a quality functional, which defines the difference between the two functions. Based on the assumption that the statistical measurement procedure is organized so that the parameters for a selected model are optimized, it is shown that the interpretation of experimental research is a search for a basic probability model.

  5. Locating one pairwise interaction: Three recursive constructions

    Directory of Open Access Journals (Sweden)

    Charles J. Colbourn

    2016-09-01

    Full Text Available In a complex component-based system, choices (levels for components (factors may interact tocause faults in the system behaviour. When faults may be caused by interactions among few factorsat specific levels, covering arrays provide a combinatorial test suite for discovering the presence offaults. While well studied, covering arrays do not enable one to determine the specific levels of factorscausing the faults; locating arrays ensure that the results from test suite execution suffice to determinethe precise levels and factors causing faults, when the number of such causes is small. Constructionsfor locating arrays are at present limited to heuristic computational methods and quite specific directconstructions. In this paper three recursive constructions are developed for locating arrays to locateone pairwise interaction causing a fault.

  6. Statistical validation of normal tissue complication probability models.

    Science.gov (United States)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Van't Veld, Aart A; Langendijk, Johannes A; Schilstra, Cornelis

    2012-09-01

    To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use. Copyright © 2012 Elsevier Inc. All rights reserved.

  7. Statistical Validation of Normal Tissue Complication Probability Models

    Energy Technology Data Exchange (ETDEWEB)

    Xu Chengjian, E-mail: c.j.xu@umcg.nl [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schaaf, Arjen van der; Veld, Aart A. van' t; Langendijk, Johannes A. [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Schilstra, Cornelis [Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, Groningen (Netherlands); Radiotherapy Institute Friesland, Leeuwarden (Netherlands)

    2012-09-01

    Purpose: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. Methods and Materials: A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. Results: Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. Conclusion: Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use.

  8. Probabilistic inference: Task dependency and individual differences of probability weighting revealed by hierarchical Bayesian modelling

    Directory of Open Access Journals (Sweden)

    Moritz eBoos

    2016-05-01

    Full Text Available Cognitive determinants of probabilistic inference were examined using hierarchical Bayesian modelling techniques. A classic urn-ball paradigm served as experimental strategy, involving a factorial two (prior probabilities by two (likelihoods design. Five computational models of cognitive processes were compared with the observed behaviour. Parameter-free Bayesian posterior probabilities and parameter-free base rate neglect provided inadequate models of probabilistic inference. The introduction of distorted subjective probabilities yielded more robust and generalizable results. A general class of (inverted S-shaped probability weighting functions had been proposed; however, the possibility of large differences in probability distortions not only across experimental conditions, but also across individuals, seems critical for the model’s success. It also seems advantageous to consider individual differences in parameters of probability weighting as being sampled from weakly informative prior distributions of individual parameter values. Thus, the results from hierarchical Bayesian modelling converge with previous results in revealing that probability weighting parameters show considerable task dependency and individual differences. Methodologically, this work exemplifies the usefulness of hierarchical Bayesian modelling techniques for cognitive psychology. Theoretically, human probabilistic inference might be best described as the application of individualized strategic policies for Bayesian belief revision.

  9. Geometric measure of pairwise quantum discord for superpositions of multipartite generalized coherent states

    International Nuclear Information System (INIS)

    Daoud, M.; Ahl Laamara, R.

    2012-01-01

    We give the explicit expressions of the pairwise quantum correlations present in superpositions of multipartite coherent states. A special attention is devoted to the evaluation of the geometric quantum discord. The dynamics of quantum correlations under a dephasing channel is analyzed. A comparison of geometric measure of quantum discord with that of concurrence shows that quantum discord in multipartite coherent states is more resilient to dissipative environments than is quantum entanglement. To illustrate our results, we consider some special superpositions of Weyl–Heisenberg, SU(2) and SU(1,1) coherent states which interpolate between Werner and Greenberger–Horne–Zeilinger states. -- Highlights: ► Pairwise quantum correlations multipartite coherent states. ► Explicit expression of geometric quantum discord. ► Entanglement sudden death and quantum discord robustness. ► Generalized coherent states interpolating between Werner and Greenberger–Horne–Zeilinger states

  10. Geometric measure of pairwise quantum discord for superpositions of multipartite generalized coherent states

    Energy Technology Data Exchange (ETDEWEB)

    Daoud, M., E-mail: m_daoud@hotmail.com [Department of Physics, Faculty of Sciences, University Ibnou Zohr, Agadir (Morocco); Ahl Laamara, R., E-mail: ahllaamara@gmail.com [LPHE-Modeling and Simulation, Faculty of Sciences, University Mohammed V, Rabat (Morocco); Centre of Physics and Mathematics, CPM, CNESTEN, Rabat (Morocco)

    2012-07-16

    We give the explicit expressions of the pairwise quantum correlations present in superpositions of multipartite coherent states. A special attention is devoted to the evaluation of the geometric quantum discord. The dynamics of quantum correlations under a dephasing channel is analyzed. A comparison of geometric measure of quantum discord with that of concurrence shows that quantum discord in multipartite coherent states is more resilient to dissipative environments than is quantum entanglement. To illustrate our results, we consider some special superpositions of Weyl–Heisenberg, SU(2) and SU(1,1) coherent states which interpolate between Werner and Greenberger–Horne–Zeilinger states. -- Highlights: ► Pairwise quantum correlations multipartite coherent states. ► Explicit expression of geometric quantum discord. ► Entanglement sudden death and quantum discord robustness. ► Generalized coherent states interpolating between Werner and Greenberger–Horne–Zeilinger states.

  11. Computation of Probabilities in Causal Models of History of Science

    Directory of Open Access Journals (Sweden)

    Osvaldo Pessoa Jr.

    2006-12-01

    Full Text Available : The aim of this paper is to investigate the ascription of probabilities in a causal model of an episode in the history of science. The aim of such a quantitative approach is to allow the implementation of the causal model in a computer, to run simulations. As an example, we look at the beginning of the science of magnetism, “explaining” — in a probabilistic way, in terms of a single causal model — why the field advanced in China but not in Europe (the difference is due to different prior probabilities of certain cultural manifestations. Given the number of years between the occurrences of two causally connected advances X and Y, one proposes a criterion for stipulating the value pY=X of the conditional probability of an advance Y occurring, given X. Next, one must assume a specific form for the cumulative probability function pY=X(t, which we take to be the time integral of an exponential distribution function, as is done in physics of radioactive decay. Rules for calculating the cumulative functions for more than two events are mentioned, involving composition, disjunction and conjunction of causes. We also consider the problems involved in supposing that the appearance of events in time follows an exponential distribution, which are a consequence of the fact that a composition of causes does not follow an exponential distribution, but a “hypoexponential” one. We suggest that a gamma distribution function might more adequately represent the appearance of advances.

  12. Reach/frequency for printed media: Personal probabilities or models

    DEFF Research Database (Denmark)

    Mortensen, Peter Stendahl

    2000-01-01

    The author evaluates two different ways of estimating reach and frequency of plans for printed media. The first assigns reading probabilities to groups of respondents and calculates reach and frequency by simulation. the second estimates parameters to a model for reach/frequency. It is concluded ...... and estiamtes from such models are shown to be closer to panel data. the problem, however, is to get valid input for such models from readership surveys. Means for this are discussed....

  13. Synchronization of pairwise-coupled, identical, relaxation oscillators based on metal-insulator phase transition devices: A model study

    Science.gov (United States)

    Parihar, Abhinav; Shukla, Nikhil; Datta, Suman; Raychowdhury, Arijit

    2015-02-01

    Computing with networks of synchronous oscillators has attracted wide-spread attention as novel materials and device topologies have enabled realization of compact, scalable and low-power coupled oscillatory systems. Of particular interest are compact and low-power relaxation oscillators that have been recently demonstrated using MIT (metal-insulator-transition) devices using properties of correlated oxides. Further the computational capability of pairwise coupled relaxation oscillators has also been shown to outperform traditional Boolean digital logic circuits. This paper presents an analysis of the dynamics and synchronization of a system of two such identical coupled relaxation oscillators implemented with MIT devices. We focus on two implementations of the oscillator: (a) a D-D configuration where complementary MIT devices (D) are connected in series to provide oscillations and (b) a D-R configuration where it is composed of a resistor (R) in series with a voltage-triggered state changing MIT device (D). The MIT device acts like a hysteresis resistor with different resistances in the two different states. The synchronization dynamics of such a system has been analyzed with purely charge based coupling using a resistive (RC) and a capacitive (CC) element in parallel. It is shown that in a D-D configuration symmetric, identical and capacitively coupled relaxation oscillator system synchronizes to an anti-phase locking state, whereas when coupled resistively the system locks in phase. Further, we demonstrate that for certain range of values of RC and CC, a bistable system is possible which can have potential applications in associative computing. In D-R configuration, we demonstrate the existence of rich dynamics including non-monotonic flows and complex phase relationship governed by the ratios of the coupling impedance. Finally, the developed theoretical formulations have been shown to explain experimentally measured waveforms of such pairwise coupled

  14. A formalism to generate probability distributions for performance-assessment modeling

    International Nuclear Information System (INIS)

    Kaplan, P.G.

    1990-01-01

    A formalism is presented for generating probability distributions of parameters used in performance-assessment modeling. The formalism is used when data are either sparse or nonexistent. The appropriate distribution is a function of the known or estimated constraints and is chosen to maximize a quantity known as Shannon's informational entropy. The formalism is applied to a parameter used in performance-assessment modeling. The functional form of the model that defines the parameter, data from the actual field site, and natural analog data are analyzed to estimate the constraints. A beta probability distribution of the example parameter is generated after finding four constraints. As an example of how the formalism is applied to the site characterization studies of Yucca Mountain, the distribution is generated for an input parameter in a performance-assessment model currently used to estimate compliance with disposal of high-level radioactive waste in geologic repositories, 10 CFR 60.113(a)(2), commonly known as the ground water travel time criterion. 8 refs., 2 figs

  15. Probability Distribution and Deviation Information Fusion Driven Support Vector Regression Model and Its Application

    Directory of Open Access Journals (Sweden)

    Changhao Fan

    2017-01-01

    Full Text Available In modeling, only information from the deviation between the output of the support vector regression (SVR model and the training sample is considered, whereas the other prior information of the training sample, such as probability distribution information, is ignored. Probabilistic distribution information describes the overall distribution of sample data in a training sample that contains different degrees of noise and potential outliers, as well as helping develop a high-accuracy model. To mine and use the probability distribution information of a training sample, a new support vector regression model that incorporates probability distribution information weight SVR (PDISVR is proposed. In the PDISVR model, the probability distribution of each sample is considered as the weight and is then introduced into the error coefficient and slack variables of SVR. Thus, the deviation and probability distribution information of the training sample are both used in the PDISVR model to eliminate the influence of noise and outliers in the training sample and to improve predictive performance. Furthermore, examples with different degrees of noise were employed to demonstrate the performance of PDISVR, which was then compared with those of three SVR-based methods. The results showed that PDISVR performs better than the three other methods.

  16. On the calculation of x-ray scattering signals from pairwise radial distribution functions

    DEFF Research Database (Denmark)

    Dohn, Asmus Ougaard; Biasin, Elisa; Haldrup, Kristoffer

    2015-01-01

    We derive a formulation for evaluating (time-resolved) x-ray scattering signals of solvated chemical systems, based on pairwise radial distribution functions, with the aim of this formulation to accompany molecular dynamics simulations. The derivation is described in detail to eliminate any possi...

  17. Modeling the radiation transfer of discontinuous canopies: results for gap probability and single-scattering contribution

    Science.gov (United States)

    Zhao, Feng; Zou, Kai; Shang, Hong; Ji, Zheng; Zhao, Huijie; Huang, Wenjiang; Li, Cunjun

    2010-10-01

    In this paper we present an analytical model for the computation of radiation transfer of discontinuous vegetation canopies. Some initial results of gap probability and bidirectional gap probability of discontinuous vegetation canopies, which are important parameters determining the radiative environment of the canopies, are given and compared with a 3- D computer simulation model. In the model, negative exponential attenuation of light within individual plant canopies is assumed. Then the computation of gap probability is resolved by determining the entry points and exiting points of the ray with the individual plants via their equations in space. For the bidirectional gap probability, which determines the single-scattering contribution of the canopy, a gap statistical analysis based model was adopted to correct the dependence of gap probabilities for both solar and viewing directions. The model incorporates the structural characteristics, such as plant sizes, leaf size, row spacing, foliage density, planting density, leaf inclination distribution. Available experimental data are inadequate for a complete validation of the model. So it was evaluated with a three dimensional computer simulation model for 3D vegetative scenes, which shows good agreement between these two models' results. This model should be useful to the quantification of light interception and the modeling of bidirectional reflectance distributions of discontinuous canopies.

  18. Assigning probability distributions to input parameters of performance assessment models

    Energy Technology Data Exchange (ETDEWEB)

    Mishra, Srikanta [INTERA Inc., Austin, TX (United States)

    2002-02-01

    This study presents an overview of various approaches for assigning probability distributions to input parameters and/or future states of performance assessment models. Specifically,three broad approaches are discussed for developing input distributions: (a) fitting continuous distributions to data, (b) subjective assessment of probabilities, and (c) Bayesian updating of prior knowledge based on new information. The report begins with a summary of the nature of data and distributions, followed by a discussion of several common theoretical parametric models for characterizing distributions. Next, various techniques are presented for fitting continuous distributions to data. These include probability plotting, method of moments, maximum likelihood estimation and nonlinear least squares analysis. The techniques are demonstrated using data from a recent performance assessment study for the Yucca Mountain project. Goodness of fit techniques are also discussed, followed by an overview of how distribution fitting is accomplished in commercial software packages. The issue of subjective assessment of probabilities is dealt with in terms of the maximum entropy distribution selection approach, as well as some common rules for codifying informal expert judgment. Formal expert elicitation protocols are discussed next, and are based primarily on the guidance provided by the US NRC. The Bayesian framework for updating prior distributions (beliefs) when new information becomes available is discussed. A simple numerical approach is presented for facilitating practical applications of the Bayes theorem. Finally, a systematic framework for assigning distributions is presented: (a) for the situation where enough data are available to define an empirical CDF or fit a parametric model to the data, and (b) to deal with the situation where only a limited amount of information is available.

  19. Assigning probability distributions to input parameters of performance assessment models

    International Nuclear Information System (INIS)

    Mishra, Srikanta

    2002-02-01

    This study presents an overview of various approaches for assigning probability distributions to input parameters and/or future states of performance assessment models. Specifically,three broad approaches are discussed for developing input distributions: (a) fitting continuous distributions to data, (b) subjective assessment of probabilities, and (c) Bayesian updating of prior knowledge based on new information. The report begins with a summary of the nature of data and distributions, followed by a discussion of several common theoretical parametric models for characterizing distributions. Next, various techniques are presented for fitting continuous distributions to data. These include probability plotting, method of moments, maximum likelihood estimation and nonlinear least squares analysis. The techniques are demonstrated using data from a recent performance assessment study for the Yucca Mountain project. Goodness of fit techniques are also discussed, followed by an overview of how distribution fitting is accomplished in commercial software packages. The issue of subjective assessment of probabilities is dealt with in terms of the maximum entropy distribution selection approach, as well as some common rules for codifying informal expert judgment. Formal expert elicitation protocols are discussed next, and are based primarily on the guidance provided by the US NRC. The Bayesian framework for updating prior distributions (beliefs) when new information becomes available is discussed. A simple numerical approach is presented for facilitating practical applications of the Bayes theorem. Finally, a systematic framework for assigning distributions is presented: (a) for the situation where enough data are available to define an empirical CDF or fit a parametric model to the data, and (b) to deal with the situation where only a limited amount of information is available

  20. Toward a generalized probability theory: conditional probabilities

    International Nuclear Information System (INIS)

    Cassinelli, G.

    1979-01-01

    The main mathematical object of interest in the quantum logic approach to the foundations of quantum mechanics is the orthomodular lattice and a set of probability measures, or states, defined by the lattice. This mathematical structure is studied per se, independently from the intuitive or physical motivation of its definition, as a generalized probability theory. It is thought that the building-up of such a probability theory could eventually throw light on the mathematical structure of Hilbert-space quantum mechanics as a particular concrete model of the generalized theory. (Auth.)

  1. Exact results for survival probability in the multistate Landau-Zener model

    International Nuclear Information System (INIS)

    Volkov, M V; Ostrovsky, V N

    2004-01-01

    An exact formula is derived for survival probability in the multistate Landau-Zener model in the special case where the initially populated state corresponds to the extremal (maximum or minimum) slope of a linear diabatic potential curve. The formula was originally guessed by S Brundobler and V Elzer (1993 J. Phys. A: Math. Gen. 26 1211) based on numerical calculations. It is a simple generalization of the expression for the probability of diabatic passage in the famous two-state Landau-Zener model. Our result is obtained via analysis and summation of the entire perturbation theory series

  2. A pragmatic pairwise group-decision method for selection of sites for nuclear power plants

    International Nuclear Information System (INIS)

    Kutbi, I.I.

    1987-01-01

    A pragmatic pairwise group-decision approach is applied to compare two regions in order to select the more suitable one for construction of nulcear power plants in the Kingdom of Saudi Arabia. The selection methodology is based on pairwise comparison by forced choice. The method facilitates rating of the regions or sites using simple calculations. Two regions, one close to Dhahran on the Arabian Gulf and another close to Jeddah on the Red Sea, are evaluated. No specific site in either region is considered at this stage. The comparison is based on a set of selection criteria which include (i) topography, (ii) geology, (iii) seismology, (iv) meteorology, (v) oceanography, (vi) hydrology and (vii) proximetry to oil and gas fields. The comparison shows that the Jeddah region is more suitable than the Dhahran region. (orig.)

  3. Fast pairwise structural RNA alignments by pruning of the dynamical programming matrix

    DEFF Research Database (Denmark)

    Havgaard, Jakob Hull; Torarinsson, Elfar; Gorodkin, Jan

    2007-01-01

    and backtracked in a normal fashion. Finally, the FOLDALIGN algorithm has also been updated with a better memory implementation and an improved energy model. With these improvements in the algorithm, the FOLDALIGN software package provides the molecular biologist with an efficient and user-friendly tool...... the advantage of providing the constraints dynamically. This has been included in a new implementation of the FOLDALIGN algorithm for pairwise local or global structural alignment of RNA sequences. It is shown that time and memory requirements are dramatically lowered while overall performance is maintained....... Furthermore, a new divide and conquer method is introduced to limit the memory requirement during global alignment and backtrack of local alignment. All branch points in the computed RNA structure are found and used to divide the structure into smaller unbranched segments. Each segment is then realigned...

  4. The effect of coupling hydrologic and hydrodynamic models on probable maximum flood estimation

    Science.gov (United States)

    Felder, Guido; Zischg, Andreas; Weingartner, Rolf

    2017-07-01

    Deterministic rainfall-runoff modelling usually assumes stationary hydrological system, as model parameters are calibrated with and therefore dependant on observed data. However, runoff processes are probably not stationary in the case of a probable maximum flood (PMF) where discharge greatly exceeds observed flood peaks. Developing hydrodynamic models and using them to build coupled hydrologic-hydrodynamic models can potentially improve the plausibility of PMF estimations. This study aims to assess the potential benefits and constraints of coupled modelling compared to standard deterministic hydrologic modelling when it comes to PMF estimation. The two modelling approaches are applied using a set of 100 spatio-temporal probable maximum precipitation (PMP) distribution scenarios. The resulting hydrographs, the resulting peak discharges as well as the reliability and the plausibility of the estimates are evaluated. The discussion of the results shows that coupling hydrologic and hydrodynamic models substantially improves the physical plausibility of PMF modelling, although both modelling approaches lead to PMF estimations for the catchment outlet that fall within a similar range. Using a coupled model is particularly suggested in cases where considerable flood-prone areas are situated within a catchment.

  5. Document Level Assessment of Document Retrieval Systems in a Pairwise System Evaluation

    Science.gov (United States)

    Rajagopal, Prabha; Ravana, Sri Devi

    2017-01-01

    Introduction: The use of averaged topic-level scores can result in the loss of valuable data and can cause misinterpretation of the effectiveness of system performance. This study aims to use the scores of each document to evaluate document retrieval systems in a pairwise system evaluation. Method: The chosen evaluation metrics are document-level…

  6. A Probability-Based Hybrid User Model for Recommendation System

    Directory of Open Access Journals (Sweden)

    Jia Hao

    2016-01-01

    Full Text Available With the rapid development of information communication technology, the available information or knowledge is exponentially increased, and this causes the well-known information overload phenomenon. This problem is more serious in product design corporations because over half of the valuable design time is consumed in knowledge acquisition, which highly extends the design cycle and weakens the competitiveness. Therefore, the recommender systems become very important in the domain of product domain. This research presents a probability-based hybrid user model, which is a combination of collaborative filtering and content-based filtering. This hybrid model utilizes user ratings and item topics or classes, which are available in the domain of product design, to predict the knowledge requirement. The comprehensive analysis of the experimental results shows that the proposed method gains better performance in most of the parameter settings. This work contributes a probability-based method to the community for implement recommender system when only user ratings and item topics are available.

  7. A stochastic model for the probability of malaria extinction by mass drug administration.

    Science.gov (United States)

    Pemberton-Ross, Peter; Chitnis, Nakul; Pothin, Emilie; Smith, Thomas A

    2017-09-18

    Mass drug administration (MDA) has been proposed as an intervention to achieve local extinction of malaria. Although its effect on the reproduction number is short lived, extinction may subsequently occur in a small population due to stochastic fluctuations. This paper examines how the probability of stochastic extinction depends on population size, MDA coverage and the reproduction number under control, R c . A simple compartmental model is developed which is used to compute the probability of extinction using probability generating functions. The expected time to extinction in small populations after MDA for various scenarios in this model is calculated analytically. The results indicate that mass drug administration (Firstly, R c must be sustained at R c  95% to have a non-negligible probability of successful elimination. Stochastic fluctuations only significantly affect the probability of extinction in populations of about 1000 individuals or less. The expected time to extinction via stochastic fluctuation is less than 10 years only in populations less than about 150 individuals. Clustering of secondary infections and of MDA distribution both contribute positively to the potential probability of success, indicating that MDA would most effectively be administered at the household level. There are very limited circumstances in which MDA will lead to local malaria elimination with a substantial probability.

  8. A Taxonomy of Latent Structure Assumptions for Probability Matrix Decomposition Models.

    Science.gov (United States)

    Meulders, Michel; De Boeck, Paul; Van Mechelen, Iven

    2003-01-01

    Proposed a taxonomy of latent structure assumptions for probability matrix decomposition (PMD) that includes the original PMD model and a three-way extension of the multiple classification latent class model. Simulation study results show the usefulness of the taxonomy. (SLD)

  9. Modelling the effects of selection temperature and mutation on the prisoner's dilemma game on a complete oriented star.

    Directory of Open Access Journals (Sweden)

    Jianguo Ren

    Full Text Available This paper models the prisoner's dilemma game based on pairwise comparison in finite populations on a complete oriented star (COS. First, we derive a linear system on a COS for calculating the corresponding fixation probabilities that imply dependence of the selection temperature and mutation. Then we observe and analyze the effects of two parameters on fixation probability under different population sizes. In particular, it is found through the experimental results that (1 high mutation is more sensitive to the fixation probability than the low one when population size is increasing, while the opposite is the case when the number of cooperators is increasing, and (2 selection temperature demotes the fixation probability.

  10. Modelling the Probability Density Function of IPTV Traffic Packet Delay Variation

    Directory of Open Access Journals (Sweden)

    Michal Halas

    2012-01-01

    Full Text Available This article deals with modelling the Probability density function of IPTV traffic packet delay variation. The use of this modelling is in an efficient de-jitter buffer estimation. When an IP packet travels across a network, it experiences delay and its variation. This variation is caused by routing, queueing systems and other influences like the processing delay of the network nodes. When we try to separate these at least three types of delay variation, we need a way to measure these types separately. This work is aimed to the delay variation caused by queueing systems which has the main implications to the form of the Probability density function.

  11. Trending in Probability of Collision Measurements via a Bayesian Zero-Inflated Beta Mixed Model

    Science.gov (United States)

    Vallejo, Jonathon; Hejduk, Matt; Stamey, James

    2015-01-01

    We investigate the performance of a generalized linear mixed model in predicting the Probabilities of Collision (Pc) for conjunction events. Specifically, we apply this model to the log(sub 10) transformation of these probabilities and argue that this transformation yields values that can be considered bounded in practice. Additionally, this bounded random variable, after scaling, is zero-inflated. Consequently, we model these values using the zero-inflated Beta distribution, and utilize the Bayesian paradigm and the mixed model framework to borrow information from past and current events. This provides a natural way to model the data and provides a basis for answering questions of interest, such as what is the likelihood of observing a probability of collision equal to the effective value of zero on a subsequent observation.

  12. Structural profiles of human miRNA families from pairwise clustering

    DEFF Research Database (Denmark)

    Kaczkowski, Bogumil; Þórarinsson, Elfar; Reiche, Kristin

    2009-01-01

    secondary structure already predicted, little is known about the patterns of structural conservation among pre-miRNAs. We address this issue by clustering the human pre-miRNA sequences based on pairwise, sequence and secondary structure alignment using FOLDALIGN, followed by global multiple alignment...... of obtained clusters by WAR. As a result, the common secondary structure was successfully determined for four FOLDALIGN clusters: the RF00027 structural family of the Rfam database and three clusters with previously undescribed consensus structures. Availability: http://genome.ku.dk/resources/mirclust...

  13. Modelling the Probability of Landslides Impacting Road Networks

    Science.gov (United States)

    Taylor, F. E.; Malamud, B. D.

    2012-04-01

    During a landslide triggering event, the threat of landslides blocking roads poses a risk to logistics, rescue efforts and communities dependant on those road networks. Here we present preliminary results of a stochastic model we have developed to evaluate the probability of landslides intersecting a simple road network during a landslide triggering event and apply simple network indices to measure the state of the road network in the affected region. A 4000 x 4000 cell array with a 5 m x 5 m resolution was used, with a pre-defined simple road network laid onto it, and landslides 'randomly' dropped onto it. Landslide areas (AL) were randomly selected from a three-parameter inverse gamma probability density function, consisting of a power-law decay of about -2.4 for medium and large values of AL and an exponential rollover for small values of AL; the rollover (maximum probability) occurs at about AL = 400 m2 This statistical distribution was chosen based on three substantially complete triggered landslide inventories recorded in existing literature. The number of landslide areas (NL) selected for each triggered event iteration was chosen to have an average density of 1 landslide km-2, i.e. NL = 400 landslide areas chosen randomly for each iteration, and was based on several existing triggered landslide event inventories. A simple road network was chosen, in a 'T' shape configuration, with one road 1 x 4000 cells (5 m x 20 km) in a 'T' formation with another road 1 x 2000 cells (5 m x 10 km). The landslide areas were then randomly 'dropped' over the road array and indices such as the location, size (ABL) and number of road blockages (NBL) recorded. This process was performed 500 times (iterations) in a Monte-Carlo type simulation. Initial results show that for a landslide triggering event with 400 landslides over a 400 km2 region, the number of road blocks per iteration, NBL,ranges from 0 to 7. The average blockage area for the 500 iterations (A¯ BL) is about 3000 m

  14. Analysis of Geographic and Pairwise Distances among Chinese Cashmere Goat Populations

    OpenAIRE

    Liu, Jian-Bin; Wang, Fan; Lang, Xia; Zha, Xi; Sun, Xiao-Ping; Yue, Yao-Jing; Feng, Rui-Lin; Yang, Bo-Hui; Guo, Jian

    2013-01-01

    This study investigated the geographic and pairwise distances of nine Chinese local Cashmere goat populations through the analysis of 20 microsatellite DNA markers. Fluorescence PCR was used to identify the markers, which were selected based on their significance as identified by the Food and Agriculture Organization of the United Nations (FAO) and the International Society for Animal Genetics (ISAG). In total, 206 alleles were detected; the average allele number was 10.30; the polymorphism i...

  15. The probabilities of one- and multi-track events for modeling radiation-induced cell kill

    Energy Technology Data Exchange (ETDEWEB)

    Schneider, Uwe; Vasi, Fabiano; Besserer, Juergen [University of Zuerich, Department of Physics, Science Faculty, Zurich (Switzerland); Radiotherapy Hirslanden, Zurich (Switzerland)

    2017-08-15

    In view of the clinical importance of hypofractionated radiotherapy, track models which are based on multi-hit events are currently reinvestigated. These models are often criticized, because it is believed that the probability of multi-track hits is negligible. In this work, the probabilities for one- and multi-track events are determined for different biological targets. The obtained probabilities can be used with nano-dosimetric cluster size distributions to obtain the parameters of track models. We quantitatively determined the probabilities for one- and multi-track events for 100, 500 and 1000 keV electrons, respectively. It is assumed that the single tracks are statistically independent and follow a Poisson distribution. Three different biological targets were investigated: (1) a DNA strand (2 nm scale); (2) two adjacent chromatin fibers (60 nm); and (3) fiber loops (300 nm). It was shown that the probabilities for one- and multi-track events are increasing with energy, size of the sensitive target structure, and dose. For a 2 x 2 x 2 nm{sup 3} target, one-track events are around 10,000 times more frequent than multi-track events. If the size of the sensitive structure is increased to 100-300 nm, the probabilities for one- and multi-track events are of the same order of magnitude. It was shown that target theories can play a role for describing radiation-induced cell death if the targets are of the size of two adjacent chromatin fibers or fiber loops. The obtained probabilities can be used together with the nano-dosimetric cluster size distributions to determine model parameters for target theories. (orig.)

  16. Capacity analysis in multi-state synaptic models: a retrieval probability perspective.

    Science.gov (United States)

    Huang, Yibi; Amit, Yali

    2011-06-01

    We define the memory capacity of networks of binary neurons with finite-state synapses in terms of retrieval probabilities of learned patterns under standard asynchronous dynamics with a predetermined threshold. The threshold is set to control the proportion of non-selective neurons that fire. An optimal inhibition level is chosen to stabilize network behavior. For any local learning rule we provide a computationally efficient and highly accurate approximation to the retrieval probability of a pattern as a function of its age. The method is applied to the sequential models (Fusi and Abbott, Nat Neurosci 10:485-493, 2007) and meta-plasticity models (Fusi et al., Neuron 45(4):599-611, 2005; Leibold and Kempter, Cereb Cortex 18:67-77, 2008). We show that as the number of synaptic states increases, the capacity, as defined here, either plateaus or decreases. In the few cases where multi-state models exceed the capacity of binary synapse models the improvement is small.

  17. Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study.

    Science.gov (United States)

    Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias; Salanti, Georgia

    2018-02-28

    To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) ("living" network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (Pmeta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided

  18. Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

    Science.gov (United States)

    Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

    2017-10-01

    A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

  19. Traffic simulation based ship collision probability modeling

    Energy Technology Data Exchange (ETDEWEB)

    Goerlandt, Floris, E-mail: floris.goerlandt@tkk.f [Aalto University, School of Science and Technology, Department of Applied Mechanics, Marine Technology, P.O. Box 15300, FI-00076 AALTO, Espoo (Finland); Kujala, Pentti [Aalto University, School of Science and Technology, Department of Applied Mechanics, Marine Technology, P.O. Box 15300, FI-00076 AALTO, Espoo (Finland)

    2011-01-15

    Maritime traffic poses various risks in terms of human, environmental and economic loss. In a risk analysis of ship collisions, it is important to get a reasonable estimate for the probability of such accidents and the consequences they lead to. In this paper, a method is proposed to assess the probability of vessels colliding with each other. The method is capable of determining the expected number of accidents, the locations where and the time when they are most likely to occur, while providing input for models concerned with the expected consequences. At the basis of the collision detection algorithm lays an extensive time domain micro-simulation of vessel traffic in the given area. The Monte Carlo simulation technique is applied to obtain a meaningful prediction of the relevant factors of the collision events. Data obtained through the Automatic Identification System is analyzed in detail to obtain realistic input data for the traffic simulation: traffic routes, the number of vessels on each route, the ship departure times, main dimensions and sailing speed. The results obtained by the proposed method for the studied case of the Gulf of Finland are presented, showing reasonable agreement with registered accident and near-miss data.

  20. Pairwise correlations via quantum discord and its geometric measure in a four-qubit spin chain

    Directory of Open Access Journals (Sweden)

    Abdel-Baset A. Mohamed

    2013-04-01

    Full Text Available The dynamic of pairwise correlations, including quantum entanglement (QE and discord (QD with geometric measure of quantum discord (GMQD, are shown in the four-qubit Heisenberg XX spin chain. The results show that the effect of the entanglement degree of the initial state on the pairwise correlations is stronger for alternate qubits than it is for nearest-neighbor qubits. This parameter results in sudden death for QE, but it cannot do so for QD and GMQD. With different values for this entanglement parameter of the initial state, QD and GMQD differ and are sensitive for any change in this parameter. It is found that GMQD is more robust than both QD and QE to describe correlations with nonzero values, which offers a valuable resource for quantum computation.

  1. Extraction of tacit knowledge from large ADME data sets via pairwise analysis.

    Science.gov (United States)

    Keefer, Christopher E; Chang, George; Kauffman, Gregory W

    2011-06-15

    Pharmaceutical companies routinely collect data across multiple projects for common ADME endpoints. Although at the time of collection the data is intended for use in decision making within a specific project, knowledge can be gained by data mining the entire cross-project data set for patterns of structure-activity relationships (SAR) that may be applied to any project. One such data mining method is pairwise analysis. This method has the advantage of being able to identify small structural changes that lead to significant changes in activity. In this paper, we describe the process for full pairwise analysis of our high-throughput ADME assays routinely used for compound discovery efforts at Pfizer (microsomal clearance, passive membrane permeability, P-gp efflux, and lipophilicity). We also describe multiple strategies for the application of these transforms in a prospective manner during compound design. Finally, a detailed analysis of the activity patterns in pairs of compounds that share the same molecular transformation reveals multiple types of transforms from an SAR perspective. These include bioisosteres, additives, multiplicatives, and a type we call switches as they act to either turn on or turn off an activity. Copyright © 2011 Elsevier Ltd. All rights reserved.

  2. Success probability orientated optimization model for resource allocation of the technological innovation multi-project system

    Institute of Scientific and Technical Information of China (English)

    Weixu Dai; Weiwei Wu; Bo Yu; Yunhao Zhu

    2016-01-01

    A success probability orientated optimization model for resource al ocation of the technological innovation multi-project system is studied. Based on the definition of the technological in-novation multi-project system, the leveling optimization of cost and success probability is set as the objective of resource al ocation. The cost function and the probability function of the optimization model are constructed. Then the objective function of the model is constructed and the solving process is explained. The model is applied to the resource al ocation of an enterprise’s technological innovation multi-project system. The results show that the pro-posed model is more effective in rational resource al ocation, and is more applicable in maximizing the utility of the technological innovation multi-project system.

  3. Probability, Nondeterminism and Concurrency

    DEFF Research Database (Denmark)

    Varacca, Daniele

    Nondeterminism is modelled in domain theory by the notion of a powerdomain, while probability is modelled by that of the probabilistic powerdomain. Some problems arise when we want to combine them in order to model computation in which both nondeterminism and probability are present. In particula...

  4. Estimating Model Probabilities using Thermodynamic Markov Chain Monte Carlo Methods

    Science.gov (United States)

    Ye, M.; Liu, P.; Beerli, P.; Lu, D.; Hill, M. C.

    2014-12-01

    Markov chain Monte Carlo (MCMC) methods are widely used to evaluate model probability for quantifying model uncertainty. In a general procedure, MCMC simulations are first conducted for each individual model, and MCMC parameter samples are then used to approximate marginal likelihood of the model by calculating the geometric mean of the joint likelihood of the model and its parameters. It has been found the method of evaluating geometric mean suffers from the numerical problem of low convergence rate. A simple test case shows that even millions of MCMC samples are insufficient to yield accurate estimation of the marginal likelihood. To resolve this problem, a thermodynamic method is used to have multiple MCMC runs with different values of a heating coefficient between zero and one. When the heating coefficient is zero, the MCMC run is equivalent to a random walk MC in the prior parameter space; when the heating coefficient is one, the MCMC run is the conventional one. For a simple case with analytical form of the marginal likelihood, the thermodynamic method yields more accurate estimate than the method of using geometric mean. This is also demonstrated for a case of groundwater modeling with consideration of four alternative models postulated based on different conceptualization of a confining layer. This groundwater example shows that model probabilities estimated using the thermodynamic method are more reasonable than those obtained using the geometric method. The thermodynamic method is general, and can be used for a wide range of environmental problem for model uncertainty quantification.

  5. Modeling Perceived Quality, Customer Satisfaction and Probability of Guest Returning to the Destination

    Directory of Open Access Journals (Sweden)

    Olivera Blagojevic Popovic

    2018-03-01

    Full Text Available In the hotel industry, it is a well-known fact that, despite of quality and variety of services provided, there is a low probability that the guests will return. This research is focused on identifying the basic factors of the hotel offer, which could determine the influence on the correlation between the guests’ satisfaction and the probability of their return. The objective of the article is to explore the relationship between the guests’ satisfaction with the quality hotel services in total (including the tourist offer of the place and the probability of his return to the same destination. The questionnaire method was applied in the survey, and the data were analysed based on factor analysis. Thereafter, the model for forecasting the probability of the guests returning to the destination was established, by using the example of Montenegrin tourism. The model represents a defined framework for the guest’s decision-making process. It identifies two main characteristics of guest experiences: satisfaction and rated quality (of the destination’s overall hotel service and tourist offer. The same model evaluates the impact of the above factors on the probability of the guests’ returning to the same destination. The starting hypothesis was the existence of a high degree of correlation between the guests’ satisfaction (with the destination’s hotel services and tourist offer and the probability of returning to the selected Montenegrin destinations. The research confirmed the above-mentioned hypothesis. The results have revealed that there are significant differences in perceived quality, i.e. satisfaction between the target groups of Eastern and Western European tourists

  6. Diagnostics of enterprise bankruptcy occurrence probability in an anti-crisis management: modern approaches and classification of models

    Directory of Open Access Journals (Sweden)

    I.V. Zhalinska

    2015-09-01

    Full Text Available Diagnostics of enterprise bankruptcy occurrence probability is defined as an important tool ensuring the viability of an organization under conditions of unpredictable dynamic environment. The paper aims to define the basic features of diagnostics of bankruptcy occurrence probability models and their classification. The article grounds the objective increasing of crisis probability in modern enterprises where such increasing leads to the need to improve the efficiency of anti-crisis enterprise activities. The system of anti-crisis management is based on the subsystem of diagnostics of bankruptcy occurrence probability. Such a subsystem is the main one for further measures to prevent and overcome the crisis. The classification of existing models of enterprise bankruptcy occurrence probability has been suggested. The classification is based on methodical and methodological principles of models. The following main groups of models are determined: the models using financial ratios, aggregates and scores, the models of discriminated analysis, the methods of strategic analysis, informal models, artificial intelligence systems and the combination of the models. The classification made it possible to identify the analytical capabilities of each of the groups of models suggested.

  7. PairWise Neighbours database: overlaps and spacers among prokaryote genomes

    Directory of Open Access Journals (Sweden)

    Garcia-Vallvé Santiago

    2009-06-01

    Full Text Available Abstract Background Although prokaryotes live in a variety of habitats and possess different metabolic and genomic complexity, they have several genomic architectural features in common. The overlapping genes are a common feature of the prokaryote genomes. The overlapping lengths tend to be short because as the overlaps become longer they have more risk of deleterious mutations. The spacers between genes tend to be short too because of the tendency to reduce the non coding DNA among prokaryotes. However they must be long enough to maintain essential regulatory signals such as the Shine-Dalgarno (SD sequence, which is responsible of an efficient translation. Description PairWise Neighbours is an interactive and intuitive database used for retrieving information about the spacers and overlapping genes among bacterial and archaeal genomes. It contains 1,956,294 gene pairs from 678 fully sequenced prokaryote genomes and is freely available at the URL http://genomes.urv.cat/pwneigh. This database provides information about the overlaps and their conservation across species. Furthermore, it allows the wide analysis of the intergenic regions providing useful information such as the location and strength of the SD sequence. Conclusion There are experiments and bioinformatic analysis that rely on correct annotations of the initiation site. Therefore, a database that studies the overlaps and spacers among prokaryotes appears to be desirable. PairWise Neighbours database permits the reliability analysis of the overlapping structures and the study of the SD presence and location among the adjacent genes, which may help to check the annotation of the initiation sites.

  8. An extended car-following model considering random safety distance with different probabilities

    Science.gov (United States)

    Wang, Jufeng; Sun, Fengxin; Cheng, Rongjun; Ge, Hongxia; Wei, Qi

    2018-02-01

    Because of the difference in vehicle type or driving skill, the driving strategy is not exactly the same. The driving speeds of the different vehicles may be different for the same headway. Since the optimal velocity function is just determined by the safety distance besides the maximum velocity and headway, an extended car-following model accounting for random safety distance with different probabilities is proposed in this paper. The linear stable condition for this extended traffic model is obtained by using linear stability theory. Numerical simulations are carried out to explore the complex phenomenon resulting from multiple safety distance in the optimal velocity function. The cases of multiple types of safety distances selected with different probabilities are presented. Numerical results show that the traffic flow with multiple safety distances with different probabilities will be more unstable than that with single type of safety distance, and will result in more stop-and-go phenomena.

  9. Pairwise comparisons and visual perceptions of equal area polygons.

    Science.gov (United States)

    Adamic, P; Babiy, V; Janicki, R; Kakiashvili, T; Koczkodaj, W W; Tadeusiewicz, R

    2009-02-01

    The number of studies related to visual perception has been plentiful in recent years. Participants rated the areas of five randomly generated shapes of equal area, using a reference unit area that was displayed together with the shapes. Respondents were 179 university students from Canada and Poland. The average error estimated by respondents using the unit square was 25.75%. The error was substantially decreased to 5.51% when the shapes were compared to one another in pairs. This gain of 20.24% for this two-dimensional experiment was substantially better than the 11.78% gain reported in the previous one-dimensional experiments. This is the first statistically sound two-dimensional experiment demonstrating that pairwise comparisons improve accuracy.

  10. Protein single-model quality assessment by feature-based probability density functions.

    Science.gov (United States)

    Cao, Renzhi; Cheng, Jianlin

    2016-04-04

    Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method-Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

  11. On New Cautious Structural Reliability Models in the Framework of imprecise Probabilities

    DEFF Research Database (Denmark)

    Utkin, Lev V.; Kozine, Igor

    2010-01-01

    models and gen-eralizing conventional ones to imprecise probabili-ties. The theoretical setup employed for this purpose is imprecise statistical reasoning (Walley 1991), whose general framework is provided by upper and lower previsions (expectations). The appeal of this theory is its ability to capture......Uncertainty of parameters in engineering design has been modeled in different frameworks such as inter-val analysis, fuzzy set and possibility theories, ran-dom set theory and imprecise probability theory. The authors of this paper for many years have been de-veloping new imprecise reliability...... both aleatory (stochas-tic) and epistemic uncertainty and the flexibility with which information can be represented. The previous research of the authors related to generalizing structural reliability models to impre-cise statistical measures is summarized in Utkin & Kozine (2002) and Utkin (2004...

  12. The structure of pairwise correlation in mouse primary visual cortex reveals functional organization in the absence of an orientation map.

    Science.gov (United States)

    Denman, Daniel J; Contreras, Diego

    2014-10-01

    Neural responses to sensory stimuli are not independent. Pairwise correlation can reduce coding efficiency, occur independent of stimulus representation, or serve as an additional channel of information, depending on the timescale of correlation and the method of decoding. Any role for correlation depends on its magnitude and structure. In sensory areas with maps, like the orientation map in primary visual cortex (V1), correlation is strongly related to the underlying functional architecture, but it is unclear whether this correlation structure is an essential feature of the system or arises from the arrangement of cells in the map. We assessed the relationship between functional architecture and pairwise correlation by measuring both synchrony and correlated spike count variability in mouse V1, which lacks an orientation map. We observed significant pairwise synchrony, which was organized by distance and relative orientation preference between cells. We also observed nonzero correlated variability in both the anesthetized (0.16) and awake states (0.18). Our results indicate that the structure of pairwise correlation is maintained in the absence of an underlying anatomical organization and may be an organizing principle of the mammalian visual system preserved by nonrandom connectivity within local networks. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  13. Compact baby universe model in ten dimension and probability function of quantum gravity

    International Nuclear Information System (INIS)

    Yan Jun; Hu Shike

    1991-01-01

    The quantum probability functions are calculated for ten-dimensional compact baby universe model. The authors find that the probability for the Yang-Mills baby universe to undergo a spontaneous compactification down to a four-dimensional spacetime is greater than that to remain in the original homogeneous multidimensional state. Some questions about large-wormhole catastrophe are also discussed

  14. Geometric modeling in probability and statistics

    CERN Document Server

    Calin, Ovidiu

    2014-01-01

    This book covers topics of Informational Geometry, a field which deals with the differential geometric study of the manifold probability density functions. This is a field that is increasingly attracting the interest of researchers from many different areas of science, including mathematics, statistics, geometry, computer science, signal processing, physics and neuroscience. It is the authors’ hope that the present book will be a valuable reference for researchers and graduate students in one of the aforementioned fields. This textbook is a unified presentation of differential geometry and probability theory, and constitutes a text for a course directed at graduate or advanced undergraduate students interested in applications of differential geometry in probability and statistics. The book contains over 100 proposed exercises meant to help students deepen their understanding, and it is accompanied by software that is able to provide numerical computations of several information geometric objects. The reader...

  15. Galaxy and Mass Assembly (GAMA): small-scale anisotropic galaxy clustering and the pairwise velocity dispersion of galaxies

    Science.gov (United States)

    Loveday, J.; Christodoulou, L.; Norberg, P.; Peacock, J. A.; Baldry, I. K.; Bland-Hawthorn, J.; Brown, M. J. I.; Colless, M.; Driver, S. P.; Holwerda, B. W.; Hopkins, A. M.; Kafle, P. R.; Liske, J.; Lopez-Sanchez, A. R.; Taylor, E. N.

    2018-03-01

    The galaxy pairwise velocity dispersion (PVD) can provide important tests of non-standard gravity and galaxy formation models. We describe measurements of the PVD of galaxies in the Galaxy and Mass Assembly (GAMA) survey as a function of projected separation and galaxy luminosity. Due to the faint magnitude limit (r PVD to smaller scales (r⊥ = 0.01 h - 1 Mpc) than previous work. The measured PVD at projected separations r⊥ ≲ 1 h - 1 Mpc increases near monotonically with increasing luminosity from σ12 ≈ 200 km s - 1 at Mr = -17 mag to σ12 ≈ 600 km s - 1 at Mr ≈ -22 mag. Analysis of the Gonzalez-Perez et al. (2014) GALFORM semi-analytic model yields no such trend of PVD with luminosity: the model overpredicts the PVD for faint galaxies. This is most likely a result of the model placing too many low-luminosity galaxies in massive haloes.

  16. Height probabilities in the Abelian sandpile model on the generalized finite Bethe lattice

    Science.gov (United States)

    Chen, Haiyan; Zhang, Fuji

    2013-08-01

    In this paper, we study the sandpile model on the generalized finite Bethe lattice with a particular boundary condition. Using a combinatorial method, we give the exact expressions for all single-site probabilities and some two-site joint probabilities. As a by-product, we prove that the height probabilities of bulk vertices are all the same for the Bethe lattice with certain given boundary condition, which was found from numerical evidence by Grassberger and Manna ["Some more sandpiles," J. Phys. (France) 51, 1077-1098 (1990)], 10.1051/jphys:0199000510110107700 but without a proof.

  17. Direct modeling of regression effects for transition probabilities in the progressive illness-death model

    DEFF Research Database (Denmark)

    Azarang, Leyla; Scheike, Thomas; de Uña-Álvarez, Jacobo

    2017-01-01

    In this work, we present direct regression analysis for the transition probabilities in the possibly non-Markov progressive illness–death model. The method is based on binomial regression, where the response is the indicator of the occupancy for the given state along time. Randomly weighted score...

  18. An empirical probability model of detecting species at low densities.

    Science.gov (United States)

    Delaney, David G; Leung, Brian

    2010-06-01

    False negatives, not detecting things that are actually present, are an important but understudied problem. False negatives are the result of our inability to perfectly detect species, especially those at low density such as endangered species or newly arriving introduced species. They reduce our ability to interpret presence-absence survey data and make sound management decisions (e.g., rapid response). To reduce the probability of false negatives, we need to compare the efficacy and sensitivity of different sampling approaches and quantify an unbiased estimate of the probability of detection. We conducted field experiments in the intertidal zone of New England and New York to test the sensitivity of two sampling approaches (quadrat vs. total area search, TAS), given different target characteristics (mobile vs. sessile). Using logistic regression we built detection curves for each sampling approach that related the sampling intensity and the density of targets to the probability of detection. The TAS approach reduced the probability of false negatives and detected targets faster than the quadrat approach. Mobility of targets increased the time to detection but did not affect detection success. Finally, we interpreted two years of presence-absence data on the distribution of the Asian shore crab (Hemigrapsus sanguineus) in New England and New York, using our probability model for false negatives. The type of experimental approach in this paper can help to reduce false negatives and increase our ability to detect species at low densities by refining sampling approaches, which can guide conservation strategies and management decisions in various areas of ecology such as conservation biology and invasion ecology.

  19. Fitting the Probability Distribution Functions to Model Particulate Matter Concentrations

    International Nuclear Information System (INIS)

    El-Shanshoury, Gh.I.

    2017-01-01

    The main objective of this study is to identify the best probability distribution and the plotting position formula for modeling the concentrations of Total Suspended Particles (TSP) as well as the Particulate Matter with an aerodynamic diameter<10 μm (PM 10 ). The best distribution provides the estimated probabilities that exceed the threshold limit given by the Egyptian Air Quality Limit value (EAQLV) as well the number of exceedance days is estimated. The standard limits of the EAQLV for TSP and PM 10 concentrations are 24-h average of 230 μg/m 3 and 70 μg/m 3 , respectively. Five frequency distribution functions with seven formula of plotting positions (empirical cumulative distribution functions) are compared to fit the average of daily TSP and PM 10 concentrations in year 2014 for Ain Sokhna city. The Quantile-Quantile plot (Q-Q plot) is used as a method for assessing how closely a data set fits a particular distribution. A proper probability distribution that represents the TSP and PM 10 has been chosen based on the statistical performance indicator values. The results show that Hosking and Wallis plotting position combined with Frechet distribution gave the highest fit for TSP and PM 10 concentrations. Burr distribution with the same plotting position follows Frechet distribution. The exceedance probability and days over the EAQLV are predicted using Frechet distribution. In 2014, the exceedance probability and days for TSP concentrations are 0.052 and 19 days, respectively. Furthermore, the PM 10 concentration is found to exceed the threshold limit by 174 days

  20. Analysis of pairwise correlations in multi-parametric PET/MR data for biological tumor characterization and treatment individualization strategies

    Energy Technology Data Exchange (ETDEWEB)

    Leibfarth, Sara; Moennich, David; Thorwarth, Daniela [University Hospital Tuebingen, Section for Biomedical Physics, Department of Radiation Oncology, Tuebingen (Germany); Simoncic, Urban [University Hospital Tuebingen, Section for Biomedical Physics, Department of Radiation Oncology, Tuebingen (Germany); University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana (Slovenia); Jozef Stefan Institute, Ljubljana (Slovenia); Welz, Stefan; Zips, Daniel [University Hospital Tuebingen, Department of Radiation Oncology, Tuebingen (Germany); Schmidt, Holger; Schwenzer, Nina [University Hospital Tuebingen, Department of Diagnostic and Interventional Radiology, Tuebingen (Germany)

    2016-07-15

    The aim of this pilot study was to explore simultaneous functional PET/MR for biological characterization of tumors and potential future treatment adaptations. To investigate the extent of complementarity between different PET/MR-based functional datasets, a pairwise correlation analysis was performed. Functional datasets of N=15 head and neck (HN) cancer patients were evaluated. For patients of group A (N=7), combined PET/MR datasets including FDG-PET and ADC maps were available. Patients of group B (N=8) had FMISO-PET, DCE-MRI and ADC maps from combined PET/MRI, an additional dynamic FMISO-PET/CT acquired directly after FMISO tracer injection as well as an FDG-PET/CT acquired a few days earlier. From DCE-MR, parameter maps K{sup trans}, v{sub e} and v{sub p} were obtained with the extended Tofts model. Moreover, parameter maps of mean DCE enhancement, ΔS{sub DCE}, and mean FMISO signal 0-4 min p.i., anti A{sub FMISO}, were derived. Pairwise correlations were quantified using the Spearman correlation coefficient (r) on both a voxel and a regional level within the gross tumor volume. Between some pairs of functional imaging modalities moderate correlations were observed with respect to the median over all patient datasets, whereas distinct correlations were only present on an individual basis. Highest inter-modality median correlations on the voxel level were obtained for FDG/FMISO (r = 0.56), FDG/ anti A{sub FMISO} (r = 0.55), anti A{sub FMISO}/ΔS{sub DCE} (r = 0.46), and FDG/ADC (r = -0.39). Correlations on the regional level showed comparable results. The results of this study suggest that the examined functional datasets provide complementary information. However, only pairwise correlations were examined, and correlations could still exist between combinations of three or more datasets. These results might contribute to the future design of individually adapted treatment approaches based on multiparametric functional imaging.

  1. Fishnet model for failure probability tail of nacre-like imbricated lamellar materials

    Science.gov (United States)

    Luo, Wen; Bažant, Zdeněk P.

    2017-12-01

    Nacre, the iridescent material of the shells of pearl oysters and abalone, consists mostly of aragonite (a form of CaCO3), a brittle constituent of relatively low strength (≈10 MPa). Yet it has astonishing mean tensile strength (≈150 MPa) and fracture energy (≈350 to 1,240 J/m2). The reasons have recently become well understood: (i) the nanoscale thickness (≈300 nm) of nacre's building blocks, the aragonite lamellae (or platelets), and (ii) the imbricated, or staggered, arrangement of these lamellea, bound by biopolymer layers only ≈25 nm thick, occupying engineering applications, however, the failure probability of ≤10-6 is generally required. To guarantee it, the type of probability density function (pdf) of strength, including its tail, must be determined. This objective, not pursued previously, is hardly achievable by experiments alone, since >10^8 tests of specimens would be needed. Here we outline a statistical model of strength that resembles a fishnet pulled diagonally, captures the tail of pdf of strength and, importantly, allows analytical safety assessments of nacreous materials. The analysis shows that, in terms of safety, the imbricated lamellar structure provides a major additional advantage—˜10% strength increase at tail failure probability 10^-6 and a 1 to 2 orders of magnitude tail probability decrease at fixed stress. Another advantage is that a high scatter of microstructure properties diminishes the strength difference between the mean and the probability tail, compared with the weakest link model. These advantages of nacre-like materials are here justified analytically and supported by millions of Monte Carlo simulations.

  2. A statistical model for investigating binding probabilities of DNA nucleotide sequences using microarrays.

    Science.gov (United States)

    Lee, Mei-Ling Ting; Bulyk, Martha L; Whitmore, G A; Church, George M

    2002-12-01

    There is considerable scientific interest in knowing the probability that a site-specific transcription factor will bind to a given DNA sequence. Microarray methods provide an effective means for assessing the binding affinities of a large number of DNA sequences as demonstrated by Bulyk et al. (2001, Proceedings of the National Academy of Sciences, USA 98, 7158-7163) in their study of the DNA-binding specificities of Zif268 zinc fingers using microarray technology. In a follow-up investigation, Bulyk, Johnson, and Church (2002, Nucleic Acid Research 30, 1255-1261) studied the interdependence of nucleotides on the binding affinities of transcription proteins. Our article is motivated by this pair of studies. We present a general statistical methodology for analyzing microarray intensity measurements reflecting DNA-protein interactions. The log probability of a protein binding to a DNA sequence on an array is modeled using a linear ANOVA model. This model is convenient because it employs familiar statistical concepts and procedures and also because it is effective for investigating the probability structure of the binding mechanism.

  3. Solving probability reasoning based on DNA strand displacement and probability modules.

    Science.gov (United States)

    Zhang, Qiang; Wang, Xiaobiao; Wang, Xiaojun; Zhou, Changjun

    2017-12-01

    In computation biology, DNA strand displacement technology is used to simulate the computation process and has shown strong computing ability. Most researchers use it to solve logic problems, but it is only rarely used in probabilistic reasoning. To process probabilistic reasoning, a conditional probability derivation model and total probability model based on DNA strand displacement were established in this paper. The models were assessed through the game "read your mind." It has been shown to enable the application of probabilistic reasoning in genetic diagnosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Estimating the Probability of Vegetation to Be Groundwater Dependent Based on the Evaluation of Tree Models

    Directory of Open Access Journals (Sweden)

    Isabel C. Pérez Hoyos

    2016-04-01

    Full Text Available Groundwater Dependent Ecosystems (GDEs are increasingly threatened by humans’ rising demand for water resources. Consequently, it is imperative to identify the location of GDEs to protect them. This paper develops a methodology to identify the probability of an ecosystem to be groundwater dependent. Probabilities are obtained by modeling the relationship between the known locations of GDEs and factors influencing groundwater dependence, namely water table depth and climatic aridity index. Probabilities are derived for the state of Nevada, USA, using modeled water table depth and aridity index values obtained from the Global Aridity database. The model selected results from the performance comparison of classification trees (CT and random forests (RF. Based on a threshold-independent accuracy measure, RF has a better ability to generate probability estimates. Considering a threshold that minimizes the misclassification rate for each model, RF also proves to be more accurate. Regarding training accuracy, performance measures such as accuracy, sensitivity, and specificity are higher for RF. For the test set, higher values of accuracy and kappa for CT highlight the fact that these measures are greatly affected by low prevalence. As shown for RF, the choice of the cutoff probability value has important consequences on model accuracy and the overall proportion of locations where GDEs are found.

  5. Modelling detection probabilities to evaluate management and control tools for an invasive species

    Science.gov (United States)

    Christy, M.T.; Yackel Adams, A.A.; Rodda, G.H.; Savidge, J.A.; Tyrrell, C.L.

    2010-01-01

    For most ecologists, detection probability (p) is a nuisance variable that must be modelled to estimate the state variable of interest (i.e. survival, abundance, or occupancy). However, in the realm of invasive species control, the rate of detection and removal is the rate-limiting step for management of this pervasive environmental problem. For strategic planning of an eradication (removal of every individual), one must identify the least likely individual to be removed, and determine the probability of removing it. To evaluate visual searching as a control tool for populations of the invasive brown treesnake Boiga irregularis, we designed a mark-recapture study to evaluate detection probability as a function of time, gender, size, body condition, recent detection history, residency status, searcher team and environmental covariates. We evaluated these factors using 654 captures resulting from visual detections of 117 snakes residing in a 5-ha semi-forested enclosure on Guam, fenced to prevent immigration and emigration of snakes but not their prey. Visual detection probability was low overall (= 0??07 per occasion) but reached 0??18 under optimal circumstances. Our results supported sex-specific differences in detectability that were a quadratic function of size, with both small and large females having lower detection probabilities than males of those sizes. There was strong evidence for individual periodic changes in detectability of a few days duration, roughly doubling detection probability (comparing peak to non-elevated detections). Snakes in poor body condition had estimated mean detection probabilities greater than snakes with high body condition. Search teams with high average detection rates exhibited detection probabilities about twice that of search teams with low average detection rates. Surveys conducted with bright moonlight and strong wind gusts exhibited moderately decreased probabilities of detecting snakes. Synthesis and applications. By

  6. Probabilities in physics

    CERN Document Server

    Hartmann, Stephan

    2011-01-01

    Many results of modern physics--those of quantum mechanics, for instance--come in a probabilistic guise. But what do probabilistic statements in physics mean? Are probabilities matters of objective fact and part of the furniture of the world, as objectivists think? Or do they only express ignorance or belief, as Bayesians suggest? And how are probabilistic hypotheses justified and supported by empirical evidence? Finally, what does the probabilistic nature of physics imply for our understanding of the world? This volume is the first to provide a philosophical appraisal of probabilities in all of physics. Its main aim is to make sense of probabilistic statements as they occur in the various physical theories and models and to provide a plausible epistemology and metaphysics of probabilities. The essays collected here consider statistical physics, probabilistic modelling, and quantum mechanics, and critically assess the merits and disadvantages of objectivist and subjectivist views of probabilities in these fie...

  7. Ruin probabilities

    DEFF Research Database (Denmark)

    Asmussen, Søren; Albrecher, Hansjörg

    The book gives a comprehensive treatment of the classical and modern ruin probability theory. Some of the topics are Lundberg's inequality, the Cramér-Lundberg approximation, exact solutions, other approximations (e.g., for heavy-tailed claim size distributions), finite horizon ruin probabilities......, extensions of the classical compound Poisson model to allow for reserve-dependent premiums, Markov-modulation, periodicity, change of measure techniques, phase-type distributions as a computational vehicle and the connection to other applied probability areas, like queueing theory. In this substantially...... updated and extended second version, new topics include stochastic control, fluctuation theory for Levy processes, Gerber–Shiu functions and dependence....

  8. A probabilistic model for the evolution of RNA structure

    Directory of Open Access Journals (Sweden)

    Holmes Ian

    2004-10-01

    Full Text Available Abstract Background For the purposes of finding and aligning noncoding RNA gene- and cis-regulatory elements in multiple-genome datasets, it is useful to be able to derive multi-sequence stochastic grammars (and hence multiple alignment algorithms systematically, starting from hypotheses about the various kinds of random mutation event and their rates. Results Here, we consider a highly simplified evolutionary model for RNA, called "The TKF91 Structure Tree" (following Thorne, Kishino and Felsenstein's 1991 model of sequence evolution with indels, which we have implemented for pairwise alignment as proof of principle for such an approach. The model, its strengths and its weaknesses are discussed with reference to four examples of functional ncRNA sequences: a riboswitch (guanine, a zipcode (nanos, a splicing factor (U4 and a ribozyme (RNase P. As shown by our visualisations of posterior probability matrices, the selected examples illustrate three different signatures of natural selection that are highly characteristic of ncRNA: (i co-ordinated basepair substitutions, (ii co-ordinated basepair indels and (iii whole-stem indels. Conclusions Although all three types of mutation "event" are built into our model, events of type (i and (ii are found to be better modeled than events of type (iii. Nevertheless, we hypothesise from the model's performance on pairwise alignments that it would form an adequate basis for a prototype multiple alignment and genefinding tool.

  9. GraphAlignment: Bayesian pairwise alignment of biological networks

    Directory of Open Access Journals (Sweden)

    Kolář Michal

    2012-11-01

    Full Text Available Abstract Background With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. Results We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0. On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6. On empirical bacterial protein-protein interaction networks (PIN and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment. Conclusions The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity. The simplicity and generality of GraphAlignment edge scoring makes the algorithm an appropriate choice for global alignment of networks.

  10. Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers

    NARCIS (Netherlands)

    Eisinga, R.N.; Heskes, T.M.; Pelzer, B.J.; Grotenhuis, H.F. te

    2017-01-01

    Background: The Friedman rank sum test is a widely-used nonparametric method in computational biology. In addition to examining the overall null hypothesis of no significant difference among any of the rank sums, it is typically of interest to conduct pairwise comparison tests. Current approaches to

  11. Modeling highway travel time distribution with conditional probability models

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira Neto, Francisco Moraes [ORNL; Chin, Shih-Miao [ORNL; Hwang, Ho-Ling [ORNL; Han, Lee [University of Tennessee, Knoxville (UTK)

    2014-01-01

    ABSTRACT Under the sponsorship of the Federal Highway Administration's Office of Freight Management and Operations, the American Transportation Research Institute (ATRI) has developed performance measures through the Freight Performance Measures (FPM) initiative. Under this program, travel speed information is derived from data collected using wireless based global positioning systems. These telemetric data systems are subscribed and used by trucking industry as an operations management tool. More than one telemetric operator submits their data dumps to ATRI on a regular basis. Each data transmission contains truck location, its travel time, and a clock time/date stamp. Data from the FPM program provides a unique opportunity for studying the upstream-downstream speed distributions at different locations, as well as different time of the day and day of the week. This research is focused on the stochastic nature of successive link travel speed data on the continental United States Interstates network. Specifically, a method to estimate route probability distributions of travel time is proposed. This method uses the concepts of convolution of probability distributions and bivariate, link-to-link, conditional probability to estimate the expected distributions for the route travel time. Major contribution of this study is the consideration of speed correlation between upstream and downstream contiguous Interstate segments through conditional probability. The established conditional probability distributions, between successive segments, can be used to provide travel time reliability measures. This study also suggests an adaptive method for calculating and updating route travel time distribution as new data or information is added. This methodology can be useful to estimate performance measures as required by the recent Moving Ahead for Progress in the 21st Century Act (MAP 21).

  12. Application of damping mechanism model and stacking fault probability in Fe-Mn alloy

    International Nuclear Information System (INIS)

    Huang, S.K.; Wen, Y.H.; Li, N.; Teng, J.; Ding, S.; Xu, Y.G.

    2008-01-01

    In this paper, the damping mechanism model of Fe-Mn alloy was analyzed using dislocation theory. Moreover, as an important parameter in Fe-Mn based alloy, the effect of stacking fault probability on the damping capacity of Fe-19.35Mn alloy after deep-cooling or tensile deformation was also studied. The damping capacity was measured using reversal torsion pendulum. The stacking fault probability of γ-austenite and ε-martensite was determined by means of X-ray diffraction (XRD) profile analysis. The microstructure was observed using scanning electronic microscope (SEM). The results indicated that with the strain amplitude increasing above a critical value, the damping capacity of Fe-19.35Mn alloy increased rapidly which could be explained using the breakaway model of Shockley partial dislocations. Deep-cooling and suitable tensile deformation could improve the damping capacity owning to the increasing of stacking fault probability of Fe-19.35Mn alloy

  13. Choice probability generating functions

    DEFF Research Database (Denmark)

    Fosgerau, Mogens; McFadden, Daniel; Bierlaire, Michel

    2010-01-01

    This paper establishes that every random utility discrete choice model (RUM) has a representation that can be characterized by a choice-probability generating function (CPGF) with specific properties, and that every function with these specific properties is consistent with a RUM. The choice...... probabilities from the RUM are obtained from the gradient of the CPGF. Mixtures of RUM are characterized by logarithmic mixtures of their associated CPGF. The paper relates CPGF to multivariate extreme value distributions, and reviews and extends methods for constructing generating functions for applications....... The choice probabilities of any ARUM may be approximated by a cross-nested logit model. The results for ARUM are extended to competing risk survival models....

  14. On the Probability of Occurrence of Clusters in Abelian Sandpile Model

    OpenAIRE

    Moradi, M.; Rouhani, S.

    2004-01-01

    We have performed extensive simulations on the Abelian Sandpile Model (ASM) on square lattice. We have estimated the probability of observation of many clusters. Some are in good agreement with previous analytical results, while some show discrepancies between simulation and analytical results.

  15. Assessment of different models for computing the probability of a clear line of sight

    Science.gov (United States)

    Bojin, Sorin; Paulescu, Marius; Badescu, Viorel

    2017-12-01

    This paper is focused on modeling the morphological properties of the cloud fields in terms of the probability of a clear line of sight (PCLOS). PCLOS is defined as the probability that a line of sight between observer and a given point of the celestial vault goes freely without intersecting a cloud. A variety of PCLOS models assuming the cloud shape hemisphere, semi-ellipsoid and ellipsoid are tested. The effective parameters (cloud aspect ratio and absolute cloud fraction) are extracted from high-resolution series of sunshine number measurements. The performance of the PCLOS models is evaluated from the perspective of their ability in retrieving the point cloudiness. The advantages and disadvantages of the tested models are discussed, aiming to a simplified parameterization of PCLOS models.

  16. Human Inferences about Sequences: A Minimal Transition Probability Model.

    Directory of Open Access Journals (Sweden)

    Florent Meyniel

    2016-12-01

    Full Text Available The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.

  17. An extended car-following model considering the appearing probability of truck and driver's characteristics

    Science.gov (United States)

    Rong, Ying; Wen, Huiying

    2018-05-01

    In this paper, the appearing probability of truck is introduced and an extended car-following model is presented to analyze the traffic flow based on the consideration of driver's characteristics, under honk environment. The stability condition of this proposed model is obtained through linear stability analysis. In order to study the evolution properties of traffic wave near the critical point, the mKdV equation is derived by the reductive perturbation method. The results show that the traffic flow will become more disorder for the larger appearing probability of truck. Besides, the appearance of leading truck affects not only the stability of traffic flow, but also the effect of other aspects on traffic flow, such as: driver's reaction and honk effect. The effects of them on traffic flow are closely correlated with the appearing probability of truck. Finally, the numerical simulations under the periodic boundary condition are carried out to verify the proposed model. And they are consistent with the theoretical findings.

  18. Probability Model for Data Redundancy Detection in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Suman Kumar

    2009-01-01

    Full Text Available Sensor networks are made of autonomous devices that are able to collect, store, process and share data with other devices. Large sensor networks are often redundant in the sense that the measurements of some nodes can be substituted by other nodes with a certain degree of confidence. This spatial correlation results in wastage of link bandwidth and energy. In this paper, a model for two associated Poisson processes, through which sensors are distributed in a plane, is derived. A probability condition is established for data redundancy among closely located sensor nodes. The model generates a spatial bivariate Poisson process whose parameters depend on the parameters of the two individual Poisson processes and on the distance between the associated points. The proposed model helps in building efficient algorithms for data dissemination in the sensor network. A numerical example is provided investigating the advantage of this model.

  19. Analysis of Geographic and Pairwise Distances among Chinese Cashmere Goat Populations

    Directory of Open Access Journals (Sweden)

    Jian-Bin Liu

    2013-03-01

    Full Text Available This study investigated the geographic and pairwise distances of nine Chinese local Cashmere goat populations through the analysis of 20 microsatellite DNA markers. Fluorescence PCR was used to identify the markers, which were selected based on their significance as identified by the Food and Agriculture Organization of the United Nations (FAO and the International Society for Animal Genetics (ISAG. In total, 206 alleles were detected; the average allele number was 10.30; the polymorphism information content of loci ranged from 0.5213 to 0.7582; the number of effective alleles ranged from 4.0484 to 4.6178; the observed heterozygosity was from 0.5023 to 0.5602 for the practical sample; the expected heterozygosity ranged from 0.5783 to 0.6464; and Allelic richness ranged from 4.7551 to 8.0693. These results indicated that Chinese Cashmere goat populations exhibited rich genetic diversity. Further, the Wright’s F-statistics of subpopulation within total (FST was 0.1184; the genetic differentiation coefficient (GST was 0.0940; and the average gene flow (Nm was 2.0415. All pairwise FST values among the populations were highly significant (p<0.01 or p<0.001, suggesting that the populations studied should all be considered to be separate breeds. Finally, the clustering analysis divided the Chinese Cashmere goat populations into at least four clusters, with the Hexi and Yashan goat populations alone in one cluster. These results have provided useful, practical, and important information for the future of Chinese Cashmere goat breeding.

  20. Survival modeling for the estimation of transition probabilities in model-based economic evaluations in the absence of individual patient data: a tutorial.

    Science.gov (United States)

    Diaby, Vakaramoko; Adunlin, Georges; Montero, Alberto J

    2014-02-01

    Survival modeling techniques are increasingly being used as part of decision modeling for health economic evaluations. As many models are available, it is imperative for interested readers to know about the steps in selecting and using the most suitable ones. The objective of this paper is to propose a tutorial for the application of appropriate survival modeling techniques to estimate transition probabilities, for use in model-based economic evaluations, in the absence of individual patient data (IPD). An illustration of the use of the tutorial is provided based on the final progression-free survival (PFS) analysis of the BOLERO-2 trial in metastatic breast cancer (mBC). An algorithm was adopted from Guyot and colleagues, and was then run in the statistical package R to reconstruct IPD, based on the final PFS analysis of the BOLERO-2 trial. It should be emphasized that the reconstructed IPD represent an approximation of the original data. Afterwards, we fitted parametric models to the reconstructed IPD in the statistical package Stata. Both statistical and graphical tests were conducted to verify the relative and absolute validity of the findings. Finally, the equations for transition probabilities were derived using the general equation for transition probabilities used in model-based economic evaluations, and the parameters were estimated from fitted distributions. The results of the application of the tutorial suggest that the log-logistic model best fits the reconstructed data from the latest published Kaplan-Meier (KM) curves of the BOLERO-2 trial. Results from the regression analyses were confirmed graphically. An equation for transition probabilities was obtained for each arm of the BOLERO-2 trial. In this paper, a tutorial was proposed and used to estimate the transition probabilities for model-based economic evaluation, based on the results of the final PFS analysis of the BOLERO-2 trial in mBC. The results of our study can serve as a basis for any model

  1. Dependence in probabilistic modeling Dempster-Shafer theory and probability bounds analysis

    Energy Technology Data Exchange (ETDEWEB)

    Ferson, Scott [Applied Biomathematics, Setauket, NY (United States); Nelsen, Roger B. [Lewis & Clark College, Portland OR (United States); Hajagos, Janos [Applied Biomathematics, Setauket, NY (United States); Berleant, Daniel J. [Iowa State Univ., Ames, IA (United States); Zhang, Jianzhong [Iowa State Univ., Ames, IA (United States); Tucker, W. Troy [Applied Biomathematics, Setauket, NY (United States); Ginzburg, Lev R. [Applied Biomathematics, Setauket, NY (United States); Oberkampf, William L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-05-01

    This report summarizes methods to incorporate information (or lack of information) about inter-variable dependence into risk assessments that use Dempster-Shafer theory or probability bounds analysis to address epistemic and aleatory uncertainty. The report reviews techniques for simulating correlated variates for a given correlation measure and dependence model, computation of bounds on distribution functions under a specified dependence model, formulation of parametric and empirical dependence models, and bounding approaches that can be used when information about the intervariable dependence is incomplete. The report also reviews several of the most pervasive and dangerous myths among risk analysts about dependence in probabilistic models.

  2. Predicting critical transitions in dynamical systems from time series using nonstationary probability density modeling.

    Science.gov (United States)

    Kwasniok, Frank

    2013-11-01

    A time series analysis method for predicting the probability density of a dynamical system is proposed. A nonstationary parametric model of the probability density is estimated from data within a maximum likelihood framework and then extrapolated to forecast the future probability density and explore the system for critical transitions or tipping points. A full systematic account of parameter uncertainty is taken. The technique is generic, independent of the underlying dynamics of the system. The method is verified on simulated data and then applied to prediction of Arctic sea-ice extent.

  3. Robust mislabel logistic regression without modeling mislabel probabilities.

    Science.gov (United States)

    Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

    2018-03-01

    Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

  4. Posterior Probability Matching and Human Perceptual Decision Making.

    Directory of Open Access Journals (Sweden)

    Richard F Murray

    2015-06-01

    Full Text Available Probability matching is a classic theory of decision making that was first developed in models of cognition. Posterior probability matching, a variant in which observers match their response probabilities to the posterior probability of each response being correct, is being used increasingly often in models of perception. However, little is known about whether posterior probability matching is consistent with the vast literature on vision and hearing that has developed within signal detection theory. Here we test posterior probability matching models using two tools from detection theory. First, we examine the models' performance in a two-pass experiment, where each block of trials is presented twice, and we measure the proportion of times that the model gives the same response twice to repeated stimuli. We show that at low performance levels, posterior probability matching models give highly inconsistent responses across repeated presentations of identical trials. We find that practised human observers are more consistent across repeated trials than these models predict, and we find some evidence that less practised observers more consistent as well. Second, we compare the performance of posterior probability matching models on a discrimination task to the performance of a theoretical ideal observer that achieves the best possible performance. We find that posterior probability matching is very inefficient at low-to-moderate performance levels, and that human observers can be more efficient than is ever possible according to posterior probability matching models. These findings support classic signal detection models, and rule out a broad class of posterior probability matching models for expert performance on perceptual tasks that range in complexity from contrast discrimination to symmetry detection. However, our findings leave open the possibility that inexperienced observers may show posterior probability matching behaviour, and our methods

  5. The perception of probability.

    Science.gov (United States)

    Gallistel, C R; Krishan, Monika; Liu, Ye; Miller, Reilly; Latham, Peter E

    2014-01-01

    We present a computational model to explain the results from experiments in which subjects estimate the hidden probability parameter of a stepwise nonstationary Bernoulli process outcome by outcome. The model captures the following results qualitatively and quantitatively, with only 2 free parameters: (a) Subjects do not update their estimate after each outcome; they step from one estimate to another at irregular intervals. (b) The joint distribution of step widths and heights cannot be explained on the assumption that a threshold amount of change must be exceeded in order for them to indicate a change in their perception. (c) The mapping of observed probability to the median perceived probability is the identity function over the full range of probabilities. (d) Precision (how close estimates are to the best possible estimate) is good and constant over the full range. (e) Subjects quickly detect substantial changes in the hidden probability parameter. (f) The perceived probability sometimes changes dramatically from one observation to the next. (g) Subjects sometimes have second thoughts about a previous change perception, after observing further outcomes. (h) The frequency with which they perceive changes moves in the direction of the true frequency over sessions. (Explaining this finding requires 2 additional parametric assumptions.) The model treats the perception of the current probability as a by-product of the construction of a compact encoding of the experienced sequence in terms of its change points. It illustrates the why and the how of intermittent Bayesian belief updating and retrospective revision in simple perception. It suggests a reinterpretation of findings in the recent literature on the neurobiology of decision making. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

  6. An analytical calculation of neighbourhood order probabilities for high dimensional Poissonian processes and mean field models

    International Nuclear Information System (INIS)

    Tercariol, Cesar Augusto Sangaletti; Kiipper, Felipe de Moura; Martinez, Alexandre Souto

    2007-01-01

    Consider that the coordinates of N points are randomly generated along the edges of a d-dimensional hypercube (random point problem). The probability P (d,N) m,n that an arbitrary point is the mth nearest neighbour to its own nth nearest neighbour (Cox probabilities) plays an important role in spatial statistics. Also, it has been useful in the description of physical processes in disordered media. Here we propose a simpler derivation of Cox probabilities, where we stress the role played by the system dimensionality d. In the limit d → ∞, the distances between pair of points become independent (random link model) and closed analytical forms for the neighbourhood probabilities are obtained both for the thermodynamic limit and finite-size system. Breaking the distance symmetry constraint drives us to the random map model, for which the Cox probabilities are obtained for two cases: whether a point is its own nearest neighbour or not

  7. On new cautious structural reliability models in the framework of imprecise probabilities

    DEFF Research Database (Denmark)

    Utkin, Lev; Kozine, Igor

    2010-01-01

    measures when the number of events of interest or observations is very small. The main feature of the models is that prior ignorance is not modelled by a fixed single prior distribution, but by a class of priors which is defined by upper and lower probabilities that can converge as statistical data......New imprecise structural reliability models are described in this paper. They are developed based on the imprecise Bayesian inference and are imprecise Dirichlet, imprecise negative binomial, gamma-exponential and normal models. The models are applied to computing cautious structural reliability...

  8. Modeling Stochastic Complexity in Complex Adaptive Systems: Non-Kolmogorov Probability and the Process Algebra Approach.

    Science.gov (United States)

    Sulis, William H

    2017-10-01

    Walter Freeman III pioneered the application of nonlinear dynamical systems theories and methodologies in his work on mesoscopic brain dynamics.Sadly, mainstream psychology and psychiatry still cling to linear correlation based data analysis techniques, which threaten to subvert the process of experimentation and theory building. In order to progress, it is necessary to develop tools capable of managing the stochastic complexity of complex biopsychosocial systems, which includes multilevel feedback relationships, nonlinear interactions, chaotic dynamics and adaptability. In addition, however, these systems exhibit intrinsic randomness, non-Gaussian probability distributions, non-stationarity, contextuality, and non-Kolmogorov probabilities, as well as the absence of mean and/or variance and conditional probabilities. These properties and their implications for statistical analysis are discussed. An alternative approach, the Process Algebra approach, is described. It is a generative model, capable of generating non-Kolmogorov probabilities. It has proven useful in addressing fundamental problems in quantum mechanics and in the modeling of developing psychosocial systems.

  9. Choice probability generating functions

    DEFF Research Database (Denmark)

    Fosgerau, Mogens; McFadden, Daniel; Bierlaire, Michel

    2013-01-01

    This paper considers discrete choice, with choice probabilities coming from maximization of preferences from a random utility field perturbed by additive location shifters (ARUM). Any ARUM can be characterized by a choice-probability generating function (CPGF) whose gradient gives the choice...... probabilities, and every CPGF is consistent with an ARUM. We relate CPGF to multivariate extreme value distributions, and review and extend methods for constructing CPGF for applications. The choice probabilities of any ARUM may be approximated by a cross-nested logit model. The results for ARUM are extended...

  10. Transition probabilities of health states for workers in Malaysia using a Markov chain model

    Science.gov (United States)

    Samsuddin, Shamshimah; Ismail, Noriszura

    2017-04-01

    The aim of our study is to estimate the transition probabilities of health states for workers in Malaysia who contribute to the Employment Injury Scheme under the Social Security Organization Malaysia using the Markov chain model. Our study uses four states of health (active, temporary disability, permanent disability and death) based on the data collected from the longitudinal studies of workers in Malaysia for 5 years. The transition probabilities vary by health state, age and gender. The results show that men employees are more likely to have higher transition probabilities to any health state compared to women employees. The transition probabilities can be used to predict the future health of workers in terms of a function of current age, gender and health state.

  11. Convergent cross-mapping and pairwise asymmetric inference.

    Science.gov (United States)

    McCracken, James M; Weigel, Robert S

    2014-12-01

    Convergent cross-mapping (CCM) is a technique for computing specific kinds of correlations between sets of times series. It was introduced by Sugihara et al. [Science 338, 496 (2012).] and is reported to be "a necessary condition for causation" capable of distinguishing causality from standard correlation. We show that the relationships between CCM correlations proposed by Sugihara et al. do not, in general, agree with intuitive concepts of "driving" and as such should not be considered indicative of causality. It is shown that the fact that the CCM algorithm implies causality is a function of system parameters for simple linear and nonlinear systems. For example, in a circuit containing a single resistor and inductor, both voltage and current can be identified as the driver depending on the frequency of the source voltage. It is shown that the CCM algorithm, however, can be modified to identify relationships between pairs of time series that are consistent with intuition for the considered example systems for which CCM causality analysis provided nonintuitive driver identifications. This modification of the CCM algorithm is introduced as "pairwise asymmetric inference" (PAI) and examples of its use are presented.

  12. Estimation and prediction of maximum daily rainfall at Sagar Island using best fit probability models

    Science.gov (United States)

    Mandal, S.; Choudhury, B. U.

    2015-07-01

    Sagar Island, setting on the continental shelf of Bay of Bengal, is one of the most vulnerable deltas to the occurrence of extreme rainfall-driven climatic hazards. Information on probability of occurrence of maximum daily rainfall will be useful in devising risk management for sustaining rainfed agrarian economy vis-a-vis food and livelihood security. Using six probability distribution models and long-term (1982-2010) daily rainfall data, we studied the probability of occurrence of annual, seasonal and monthly maximum daily rainfall (MDR) in the island. To select the best fit distribution models for annual, seasonal and monthly time series based on maximum rank with minimum value of test statistics, three statistical goodness of fit tests, viz. Kolmogorove-Smirnov test (K-S), Anderson Darling test ( A 2 ) and Chi-Square test ( X 2) were employed. The fourth probability distribution was identified from the highest overall score obtained from the three goodness of fit tests. Results revealed that normal probability distribution was best fitted for annual, post-monsoon and summer seasons MDR, while Lognormal, Weibull and Pearson 5 were best fitted for pre-monsoon, monsoon and winter seasons, respectively. The estimated annual MDR were 50, 69, 86, 106 and 114 mm for return periods of 2, 5, 10, 20 and 25 years, respectively. The probability of getting an annual MDR of >50, >100, >150, >200 and >250 mm were estimated as 99, 85, 40, 12 and 03 % level of exceedance, respectively. The monsoon, summer and winter seasons exhibited comparatively higher probabilities (78 to 85 %) for MDR of >100 mm and moderate probabilities (37 to 46 %) for >150 mm. For different recurrence intervals, the percent probability of MDR varied widely across intra- and inter-annual periods. In the island, rainfall anomaly can pose a climatic threat to the sustainability of agricultural production and thus needs adequate adaptation and mitigation measures.

  13. Probability-based collaborative filtering model for predicting gene–disease associations

    OpenAIRE

    Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

    2017-01-01

    Background Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene–disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. Methods We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our mo...

  14. Development of a new model to evaluate the probability of automatic plant trips for pressurized water reactors

    Energy Technology Data Exchange (ETDEWEB)

    Shimada, Yoshio [Institute of Nuclear Safety System Inc., Mihama, Fukui (Japan); Kawai, Katsunori; Suzuki, Hiroshi [Mitsubishi Heavy Industries Ltd., Tokyo (Japan)

    2001-09-01

    In order to improve the reliability of plant operations for pressurized water reactors, a new fault tree model was developed to evaluate the probability of automatic plant trips. This model consists of fault trees for sixteen systems. It has the following features: (1) human errors and transmission line incidents are modeled by the existing data, (2) the repair of failed components is considered to calculate the failure probability of components, (3) uncertainty analysis is performed by an exact method. From the present results, it is confirmed that the obtained upper and lower bound values of the automatic plant trip probability are within the existing data bound in Japan. Thereby this model can be applicable to the prediction of plant performance and reliability. (author)

  15. Predicting Flow Breakdown Probability and Duration in Stochastic Network Models: Impact on Travel Time Reliability

    Energy Technology Data Exchange (ETDEWEB)

    Dong, Jing [ORNL; Mahmassani, Hani S. [Northwestern University, Evanston

    2011-01-01

    This paper proposes a methodology to produce random flow breakdown endogenously in a mesoscopic operational model, by capturing breakdown probability and duration. Based on previous research findings that probability of flow breakdown can be represented as a function of flow rate and the duration can be characterized by a hazard model. By generating random flow breakdown at various levels and capturing the traffic characteristics at the onset of the breakdown, the stochastic network simulation model provides a tool for evaluating travel time variability. The proposed model can be used for (1) providing reliability related traveler information; (2) designing ITS (intelligent transportation systems) strategies to improve reliability; and (3) evaluating reliability-related performance measures of the system.

  16. On the Inclusion of Short-distance Bystander Effects into a Logistic Tumor Control Probability Model.

    Science.gov (United States)

    Tempel, David G; Brodin, N Patrik; Tomé, Wolfgang A

    2018-01-01

    Currently, interactions between voxels are neglected in the tumor control probability (TCP) models used in biologically-driven intensity-modulated radiotherapy treatment planning. However, experimental data suggests that this may not always be justified when bystander effects are important. We propose a model inspired by the Ising model, a short-range interaction model, to investigate if and when it is important to include voxel to voxel interactions in biologically-driven treatment planning. This Ising-like model for TCP is derived by first showing that the logistic model of tumor control is mathematically equivalent to a non-interacting Ising model. Using this correspondence, the parameters of the logistic model are mapped to the parameters of an Ising-like model and bystander interactions are introduced as a short-range interaction as is the case for the Ising model. As an example, we apply the model to study the effect of bystander interactions in the case of radiation therapy for prostate cancer. The model shows that it is adequate to neglect bystander interactions for dose distributions that completely cover the treatment target and yield TCP estimates that lie in the shoulder of the dose response curve. However, for dose distributions that yield TCP estimates that lie on the steep part of the dose response curve or for inhomogeneous dose distributions having significant hot and/or cold regions, bystander effects may be important. Furthermore, the proposed model highlights a previously unexplored and potentially fruitful connection between the fields of statistical mechanics and tumor control probability/normal tissue complication probability modeling.

  17. Pre-Service Mathematics Teachers' Use of Probability Models in Making Informal Inferences about a Chance Game

    Science.gov (United States)

    Kazak, Sibel; Pratt, Dave

    2017-01-01

    This study considers probability models as tools for both making informal statistical inferences and building stronger conceptual connections between data and chance topics in teaching statistics. In this paper, we aim to explore pre-service mathematics teachers' use of probability models for a chance game, where the sum of two dice matters in…

  18. Benefits of Using Pairwise Trajectory Management in the Central East Pacific

    Science.gov (United States)

    Chartrand, Ryan; Ballard, Kathryn

    2017-01-01

    Pairwise Trajectory Management (PTM) is a concept that utilizes airborne and ground-based capabilities to enable airborne spacing operations in procedural airspace. This concept makes use of updated ground automation, Automatic Dependent Surveillance-Broadcast (ADS-B) and on board avionics generating real time guidance. An experiment was conducted to examine the potential benefits of implementing PTM in the Central East Pacific oceanic region. An explanation of the experiment and some of the results are included in this paper. The PTM concept allowed for an increase in the average time an aircraft is able to spend at its desired flight level and a reduction in fuel burn.

  19. Exploring the Subtleties of Inverse Probability Weighting and Marginal Structural Models.

    Science.gov (United States)

    Breskin, Alexander; Cole, Stephen R; Westreich, Daniel

    2018-05-01

    Since being introduced to epidemiology in 2000, marginal structural models have become a commonly used method for causal inference in a wide range of epidemiologic settings. In this brief report, we aim to explore three subtleties of marginal structural models. First, we distinguish marginal structural models from the inverse probability weighting estimator, and we emphasize that marginal structural models are not only for longitudinal exposures. Second, we explore the meaning of the word "marginal" in "marginal structural model." Finally, we show that the specification of a marginal structural model can have important implications for the interpretation of its parameters. Each of these concepts have important implications for the use and understanding of marginal structural models, and thus providing detailed explanations of them may lead to better practices for the field of epidemiology.

  20. Multilevel summation with B-spline interpolation for pairwise interactions in molecular dynamics simulations

    International Nuclear Information System (INIS)

    Hardy, David J.; Schulten, Klaus; Wolff, Matthew A.; Skeel, Robert D.; Xia, Jianlin

    2016-01-01

    The multilevel summation method for calculating electrostatic interactions in molecular dynamics simulations constructs an approximation to a pairwise interaction kernel and its gradient, which can be evaluated at a cost that scales linearly with the number of atoms. The method smoothly splits the kernel into a sum of partial kernels of increasing range and decreasing variability with the longer-range parts interpolated from grids of increasing coarseness. Multilevel summation is especially appropriate in the context of dynamics and minimization, because it can produce continuous gradients. This article explores the use of B-splines to increase the accuracy of the multilevel summation method (for nonperiodic boundaries) without incurring additional computation other than a preprocessing step (whose cost also scales linearly). To obtain accurate results efficiently involves technical difficulties, which are overcome by a novel preprocessing algorithm. Numerical experiments demonstrate that the resulting method offers substantial improvements in accuracy and that its performance is competitive with an implementation of the fast multipole method in general and markedly better for Hamiltonian formulations of molecular dynamics. The improvement is great enough to establish multilevel summation as a serious contender for calculating pairwise interactions in molecular dynamics simulations. In particular, the method appears to be uniquely capable for molecular dynamics in two situations, nonperiodic boundary conditions and massively parallel computation, where the fast Fourier transform employed in the particle–mesh Ewald method falls short.

  1. Aggregate and Individual Replication Probability within an Explicit Model of the Research Process

    Science.gov (United States)

    Miller, Jeff; Schwarz, Wolf

    2011-01-01

    We study a model of the research process in which the true effect size, the replication jitter due to changes in experimental procedure, and the statistical error of effect size measurement are all normally distributed random variables. Within this model, we analyze the probability of successfully replicating an initial experimental result by…

  2. Sampling the stream landscape: Improving the applicability of an ecoregion-level capture probability model for stream fishes

    Science.gov (United States)

    Mollenhauer, Robert; Mouser, Joshua B.; Brewer, Shannon K.

    2018-01-01

    Temporal and spatial variability in streams result in heterogeneous gear capture probability (i.e., the proportion of available individuals identified) that confounds interpretation of data used to monitor fish abundance. We modeled tow-barge electrofishing capture probability at multiple spatial scales for nine Ozark Highland stream fishes. In addition to fish size, we identified seven reach-scale environmental characteristics associated with variable capture probability: stream discharge, water depth, conductivity, water clarity, emergent vegetation, wetted width–depth ratio, and proportion of riffle habitat. The magnitude of the relationship between capture probability and both discharge and depth varied among stream fishes. We also identified lithological characteristics among stream segments as a coarse-scale source of variable capture probability. The resulting capture probability model can be used to adjust catch data and derive reach-scale absolute abundance estimates across a wide range of sampling conditions with similar effort as used in more traditional fisheries surveys (i.e., catch per unit effort). Adjusting catch data based on variable capture probability improves the comparability of data sets, thus promoting both well-informed conservation and management decisions and advances in stream-fish ecology.

  3. Modeling co-occurrence of northern spotted and barred owls: accounting for detection probability differences

    Science.gov (United States)

    Bailey, Larissa L.; Reid, Janice A.; Forsman, Eric D.; Nichols, James D.

    2009-01-01

    Barred owls (Strix varia) have recently expanded their range and now encompass the entire range of the northern spotted owl (Strix occidentalis caurina). This expansion has led to two important issues of concern for management of northern spotted owls: (1) possible competitive interactions between the two species that could contribute to population declines of northern spotted owls, and (2) possible changes in vocalization behavior and detection probabilities of northern spotted owls induced by presence of barred owls. We used a two-species occupancy model to investigate whether there was evidence of competitive exclusion between the two species at study locations in Oregon, USA. We simultaneously estimated detection probabilities for both species and determined if the presence of one species influenced the detection of the other species. Model selection results and associated parameter estimates provided no evidence that barred owls excluded spotted owls from territories. We found strong evidence that detection probabilities differed for the two species, with higher probabilities for northern spotted owls that are the object of current surveys. Non-detection of barred owls is very common in surveys for northern spotted owls, and detection of both owl species was negatively influenced by the presence of the congeneric species. Our results suggest that analyses directed at hypotheses of barred owl effects on demographic or occupancy vital rates of northern spotted owls need to deal adequately with imperfect and variable detection probabilities for both species.

  4. Probability-based collaborative filtering model for predicting gene-disease associations.

    Science.gov (United States)

    Zeng, Xiangxiang; Ding, Ningxiang; Rodríguez-Patón, Alfonso; Zou, Quan

    2017-12-28

    Accurately predicting pathogenic human genes has been challenging in recent research. Considering extensive gene-disease data verified by biological experiments, we can apply computational methods to perform accurate predictions with reduced time and expenses. We propose a probability-based collaborative filtering model (PCFM) to predict pathogenic human genes. Several kinds of data sets, containing data of humans and data of other nonhuman species, are integrated in our model. Firstly, on the basis of a typical latent factorization model, we propose model I with an average heterogeneous regularization. Secondly, we develop modified model II with personal heterogeneous regularization to enhance the accuracy of aforementioned models. In this model, vector space similarity or Pearson correlation coefficient metrics and data on related species are also used. We compared the results of PCFM with the results of four state-of-arts approaches. The results show that PCFM performs better than other advanced approaches. PCFM model can be leveraged for predictions of disease genes, especially for new human genes or diseases with no known relationships.

  5. Pareto-Optimal Model Selection via SPRINT-Race.

    Science.gov (United States)

    Zhang, Tiantian; Georgiopoulos, Michael; Anagnostopoulos, Georgios C

    2018-02-01

    In machine learning, the notion of multi-objective model selection (MOMS) refers to the problem of identifying the set of Pareto-optimal models that optimize by compromising more than one predefined objectives simultaneously. This paper introduces SPRINT-Race, the first multi-objective racing algorithm in a fixed-confidence setting, which is based on the sequential probability ratio with indifference zone test. SPRINT-Race addresses the problem of MOMS with multiple stochastic optimization objectives in the proper Pareto-optimality sense. In SPRINT-Race, a pairwise dominance or non-dominance relationship is statistically inferred via a non-parametric, ternary-decision, dual-sequential probability ratio test. The overall probability of falsely eliminating any Pareto-optimal models or mistakenly returning any clearly dominated models is strictly controlled by a sequential Holm's step-down family-wise error rate control method. As a fixed-confidence model selection algorithm, the objective of SPRINT-Race is to minimize the computational effort required to achieve a prescribed confidence level about the quality of the returned models. The performance of SPRINT-Race is first examined via an artificially constructed MOMS problem with known ground truth. Subsequently, SPRINT-Race is applied on two real-world applications: 1) hybrid recommender system design and 2) multi-criteria stock selection. The experimental results verify that SPRINT-Race is an effective and efficient tool for such MOMS problems. code of SPRINT-Race is available at https://github.com/watera427/SPRINT-Race.

  6. Calibration of Smartphone-Based Weather Measurements Using Pairwise Gossip.

    Science.gov (United States)

    Zamora, Jane Louie Fresco; Kashihara, Shigeru; Yamaguchi, Suguru

    2015-01-01

    Accurate and reliable daily global weather reports are necessary for weather forecasting and climate analysis. However, the availability of these reports continues to decline due to the lack of economic support and policies in maintaining ground weather measurement systems from where these reports are obtained. Thus, to mitigate data scarcity, it is required to utilize weather information from existing sensors and built-in smartphone sensors. However, as smartphone usage often varies according to human activity, it is difficult to obtain accurate measurement data. In this paper, we present a heuristic-based pairwise gossip algorithm that will calibrate smartphone-based pressure sensors with respect to fixed weather stations as our referential ground truth. Based on actual measurements, we have verified that smartphone-based readings are unstable when observed during movement. Using our calibration algorithm on actual smartphone-based pressure readings, the updated values were significantly closer to the ground truth values.

  7. Uncertainty Analysis of Multi-Model Flood Forecasts

    Directory of Open Access Journals (Sweden)

    Erich J. Plate

    2015-12-01

    Full Text Available This paper demonstrates, by means of a systematic uncertainty analysis, that the use of outputs from more than one model can significantly improve conditional forecasts of discharges or water stages, provided the models are structurally different. Discharge forecasts from two models and the actual forecasted discharge are assumed to form a three-dimensional joint probability density distribution (jpdf, calibrated on long time series of data. The jpdf is decomposed into conditional probability density distributions (cpdf by means of Bayes formula, as suggested and explored by Krzysztofowicz in a series of papers. In this paper his approach is simplified to optimize conditional forecasts for any set of two forecast models. Its application is demonstrated by means of models developed in a study of flood forecasting for station Stung Treng on the middle reach of the Mekong River in South-East Asia. Four different forecast models were used and pairwise combined: forecast with no model, with persistence model, with a regression model, and with a rainfall-runoff model. Working with cpdfs requires determination of dependency among variables, for which linear regressions are required, as was done by Krzysztofowicz. His Bayesian approach based on transforming observed probability distributions of discharges and forecasts into normal distributions is also explored. Results obtained with his method for normal prior and likelihood distributions are identical to results from direct multiple regressions. Furthermore, it is shown that in the present case forecast accuracy is only marginally improved, if Weibull distributed basic data were converted into normally distributed variables.

  8. Introduction to probability with R

    CERN Document Server

    Baclawski, Kenneth

    2008-01-01

    FOREWORD PREFACE Sets, Events, and Probability The Algebra of Sets The Bernoulli Sample Space The Algebra of Multisets The Concept of Probability Properties of Probability Measures Independent Events The Bernoulli Process The R Language Finite Processes The Basic Models Counting Rules Computing Factorials The Second Rule of Counting Computing Probabilities Discrete Random Variables The Bernoulli Process: Tossing a Coin The Bernoulli Process: Random Walk Independence and Joint Distributions Expectations The Inclusion-Exclusion Principle General Random Variable

  9. Assessing the clinical probability of pulmonary embolism

    International Nuclear Information System (INIS)

    Miniati, M.; Pistolesi, M.

    2001-01-01

    Clinical assessment is a cornerstone of the recently validated diagnostic strategies for pulmonary embolism (PE). Although the diagnostic yield of individual symptoms, signs, and common laboratory tests is limited, the combination of these variables, either by empirical assessment or by a prediction rule, can be used to express a clinical probability of PE. The latter may serve as pretest probability to predict the probability of PE after further objective testing (posterior or post-test probability). Over the last few years, attempts have been made to develop structured prediction models for PE. In a Canadian multicenter prospective study, the clinical probability of PE was rated as low, intermediate, or high according to a model which included assessment of presenting symptoms and signs, risk factors, and presence or absence of an alternative diagnosis at least as likely as PE. Recently, a simple clinical score was developed to stratify outpatients with suspected PE into groups with low, intermediate, or high clinical probability. Logistic regression was used to predict parameters associated with PE. A score ≤ 4 identified patients with low probability of whom 10% had PE. The prevalence of PE in patients with intermediate (score 5-8) and high probability (score ≥ 9) was 38 and 81%, respectively. As opposed to the Canadian model, this clinical score is standardized. The predictor variables identified in the model, however, were derived from a database of emergency ward patients. This model may, therefore, not be valid in assessing the clinical probability of PE in inpatients. In the PISA-PED study, a clinical diagnostic algorithm was developed which rests on the identification of three relevant clinical symptoms and on their association with electrocardiographic and/or radiographic abnormalities specific for PE. Among patients who, according to the model, had been rated as having a high clinical probability, the prevalence of proven PE was 97%, while it was 3

  10. Truth, possibility and probability new logical foundations of probability and statistical inference

    CERN Document Server

    Chuaqui, R

    1991-01-01

    Anyone involved in the philosophy of science is naturally drawn into the study of the foundations of probability. Different interpretations of probability, based on competing philosophical ideas, lead to different statistical techniques, and frequently to mutually contradictory consequences. This unique book presents a new interpretation of probability, rooted in the traditional interpretation that was current in the 17th and 18th centuries. Mathematical models are constructed based on this interpretation, and statistical inference and decision theory are applied, including some examples in artificial intelligence, solving the main foundational problems. Nonstandard analysis is extensively developed for the construction of the models and in some of the proofs. Many nonstandard theorems are proved, some of them new, in particular, a representation theorem that asserts that any stochastic process can be approximated by a process defined over a space with equiprobable outcomes.

  11. Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model.

    Science.gov (United States)

    Rajeswaran, Jeevanantham; Blackstone, Eugene H; Ehrlinger, John; Li, Liang; Ishwaran, Hemant; Parides, Michael K

    2018-01-01

    Atrial fibrillation is an arrhythmic disorder where the electrical signals of the heart become irregular. The probability of atrial fibrillation (binary response) is often time varying in a structured fashion, as is the influence of associated risk factors. A generalized nonlinear mixed effects model is presented to estimate the time-related probability of atrial fibrillation using a temporal decomposition approach to reveal the pattern of the probability of atrial fibrillation and their determinants. This methodology generalizes to patient-specific analysis of longitudinal binary data with possibly time-varying effects of covariates and with different patient-specific random effects influencing different temporal phases. The motivation and application of this model is illustrated using longitudinally measured atrial fibrillation data obtained through weekly trans-telephonic monitoring from an NIH sponsored clinical trial being conducted by the Cardiothoracic Surgery Clinical Trials Network.

  12. Enhancing pairwise state-transition weights: A new weighting scheme in simulated tempering that can minimize transition time between a pair of conformational states

    Energy Technology Data Exchange (ETDEWEB)

    Qiao, Qin, E-mail: qqiao@ust.hk; Zhang, Hou-Dao [Department of Chemistry, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon (Hong Kong); Huang, Xuhui, E-mail: xuhuihuang@ust.hk [Department of Chemistry, Division of Biomedical Engineering, Center of Systems Biology and Human Health, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon (Hong Kong); The HKUST Shenzhen Research Institute, Shenzhen (China)

    2016-04-21

    Simulated tempering (ST) is a widely used enhancing sampling method for Molecular Dynamics simulations. As one expanded ensemble method, ST is a combination of canonical ensembles at different temperatures and the acceptance probability of cross-temperature transitions is determined by both the temperature difference and the weights of each temperature. One popular way to obtain the weights is to adopt the free energy of each canonical ensemble, which achieves uniform sampling among temperature space. However, this uniform distribution in temperature space may not be optimal since high temperatures do not always speed up the conformational transitions of interest, as anti-Arrhenius kinetics are prevalent in protein and RNA folding. Here, we propose a new method: Enhancing Pairwise State-transition Weights (EPSW), to obtain the optimal weights by minimizing the round-trip time for transitions among different metastable states at the temperature of interest in ST. The novelty of the EPSW algorithm lies in explicitly considering the kinetics of conformation transitions when optimizing the weights of different temperatures. We further demonstrate the power of EPSW in three different systems: a simple two-temperature model, a two-dimensional model for protein folding with anti-Arrhenius kinetics, and the alanine dipeptide. The results from these three systems showed that the new algorithm can substantially accelerate the transitions between conformational states of interest in the ST expanded ensemble and further facilitate the convergence of thermodynamics compared to the widely used free energy weights. We anticipate that this algorithm is particularly useful for studying functional conformational changes of biological systems where the initial and final states are often known from structural biology experiments.

  13. Enhancing pairwise state-transition weights: A new weighting scheme in simulated tempering that can minimize transition time between a pair of conformational states

    Science.gov (United States)

    Qiao, Qin; Zhang, Hou-Dao; Huang, Xuhui

    2016-04-01

    Simulated tempering (ST) is a widely used enhancing sampling method for Molecular Dynamics simulations. As one expanded ensemble method, ST is a combination of canonical ensembles at different temperatures and the acceptance probability of cross-temperature transitions is determined by both the temperature difference and the weights of each temperature. One popular way to obtain the weights is to adopt the free energy of each canonical ensemble, which achieves uniform sampling among temperature space. However, this uniform distribution in temperature space may not be optimal since high temperatures do not always speed up the conformational transitions of interest, as anti-Arrhenius kinetics are prevalent in protein and RNA folding. Here, we propose a new method: Enhancing Pairwise State-transition Weights (EPSW), to obtain the optimal weights by minimizing the round-trip time for transitions among different metastable states at the temperature of interest in ST. The novelty of the EPSW algorithm lies in explicitly considering the kinetics of conformation transitions when optimizing the weights of different temperatures. We further demonstrate the power of EPSW in three different systems: a simple two-temperature model, a two-dimensional model for protein folding with anti-Arrhenius kinetics, and the alanine dipeptide. The results from these three systems showed that the new algorithm can substantially accelerate the transitions between conformational states of interest in the ST expanded ensemble and further facilitate the convergence of thermodynamics compared to the widely used free energy weights. We anticipate that this algorithm is particularly useful for studying functional conformational changes of biological systems where the initial and final states are often known from structural biology experiments.

  14. Enhancing pairwise state-transition weights: A new weighting scheme in simulated tempering that can minimize transition time between a pair of conformational states

    International Nuclear Information System (INIS)

    Qiao, Qin; Zhang, Hou-Dao; Huang, Xuhui

    2016-01-01

    Simulated tempering (ST) is a widely used enhancing sampling method for Molecular Dynamics simulations. As one expanded ensemble method, ST is a combination of canonical ensembles at different temperatures and the acceptance probability of cross-temperature transitions is determined by both the temperature difference and the weights of each temperature. One popular way to obtain the weights is to adopt the free energy of each canonical ensemble, which achieves uniform sampling among temperature space. However, this uniform distribution in temperature space may not be optimal since high temperatures do not always speed up the conformational transitions of interest, as anti-Arrhenius kinetics are prevalent in protein and RNA folding. Here, we propose a new method: Enhancing Pairwise State-transition Weights (EPSW), to obtain the optimal weights by minimizing the round-trip time for transitions among different metastable states at the temperature of interest in ST. The novelty of the EPSW algorithm lies in explicitly considering the kinetics of conformation transitions when optimizing the weights of different temperatures. We further demonstrate the power of EPSW in three different systems: a simple two-temperature model, a two-dimensional model for protein folding with anti-Arrhenius kinetics, and the alanine dipeptide. The results from these three systems showed that the new algorithm can substantially accelerate the transitions between conformational states of interest in the ST expanded ensemble and further facilitate the convergence of thermodynamics compared to the widely used free energy weights. We anticipate that this algorithm is particularly useful for studying functional conformational changes of biological systems where the initial and final states are often known from structural biology experiments.

  15. Multiple-event probability in general-relativistic quantum mechanics. II. A discrete model

    International Nuclear Information System (INIS)

    Mondragon, Mauricio; Perez, Alejandro; Rovelli, Carlo

    2007-01-01

    We introduce a simple quantum mechanical model in which time and space are discrete and periodic. These features avoid the complications related to continuous-spectrum operators and infinite-norm states. The model provides a tool for discussing the probabilistic interpretation of generally covariant quantum systems, without the confusion generated by spurious infinities. We use the model to illustrate the formalism of general-relativistic quantum mechanics, and to test the definition of multiple-event probability introduced in a companion paper [Phys. Rev. D 75, 084033 (2007)]. We consider a version of the model with unitary time evolution and a version without unitary time evolution

  16. Preference learning with evolutionary Multivariate Adaptive Regression Spline model

    DEFF Research Database (Denmark)

    Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll

    2015-01-01

    This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...

  17. Historical demography of common carp estimated from individuals collected from various parts of the world using the pairwise sequentially markovian coalescent approach.

    Science.gov (United States)

    Yuan, Zihao; Huang, Wei; Liu, Shikai; Xu, Peng; Dunham, Rex; Liu, Zhanjiang

    2018-04-01

    The inference of historical demography of a species is helpful for understanding species' differentiation and its population dynamics. However, such inference has been previously difficult due to the lack of proper analytical methods and availability of genetic data. A recently developed method called Pairwise Sequentially Markovian Coalescent (PSMC) offers the capability for estimation of the trajectories of historical populations over considerable time periods using genomic sequences. In this study, we applied this approach to infer the historical demography of the common carp using samples collected from Europe, Asia and the Americas. Comparison between Asian and European common carp populations showed that the last glacial period starting 100 ka BP likely caused a significant decline in population size of the wild common carp in Europe, while it did not have much of an impact on its counterparts in Asia. This was probably caused by differences in glacial activities in East Asia and Europe, and suggesting a separation of the European and Asian clades before the last glacial maximum. The North American clade which is an invasive population shared a similar demographic history as those from Europe, consistent with the idea that the North American common carp probably had European ancestral origins. Our analysis represents the first reconstruction of the historical population demography of the common carp, which is important to elucidate the separation of European and Asian common carp clades during the Quaternary glaciation, as well as the dispersal of common carp across the world.

  18. The ruin probability of a discrete time risk model under constant interest rate with heavy tails

    NARCIS (Netherlands)

    Tang, Q.

    2004-01-01

    This paper investigates the ultimate ruin probability of a discrete time risk model with a positive constant interest rate. Under the assumption that the gross loss of the company within one year is subexponentially distributed, a simple asymptotic relation for the ruin probability is derived and

  19. Three-dimensional analytic probabilities of coupled vibrational-rotational-translational energy transfer for DSMC modeling of nonequilibrium flows

    International Nuclear Information System (INIS)

    Adamovich, Igor V.

    2014-01-01

    A three-dimensional, nonperturbative, semiclassical analytic model of vibrational energy transfer in collisions between a rotating diatomic molecule and an atom, and between two rotating diatomic molecules (Forced Harmonic Oscillator–Free Rotation model) has been extended to incorporate rotational relaxation and coupling between vibrational, translational, and rotational energy transfer. The model is based on analysis of semiclassical trajectories of rotating molecules interacting by a repulsive exponential atom-to-atom potential. The model predictions are compared with the results of three-dimensional close-coupled semiclassical trajectory calculations using the same potential energy surface. The comparison demonstrates good agreement between analytic and numerical probabilities of rotational and vibrational energy transfer processes, over a wide range of total collision energies, rotational energies, and impact parameter. The model predicts probabilities of single-quantum and multi-quantum vibrational-rotational transitions and is applicable up to very high collision energies and quantum numbers. Closed-form analytic expressions for these transition probabilities lend themselves to straightforward incorporation into DSMC nonequilibrium flow codes

  20. Probability Theory Plus Noise: Descriptive Estimation and Inferential Judgment.

    Science.gov (United States)

    Costello, Fintan; Watts, Paul

    2018-01-01

    We describe a computational model of two central aspects of people's probabilistic reasoning: descriptive probability estimation and inferential probability judgment. This model assumes that people's reasoning follows standard frequentist probability theory, but it is subject to random noise. This random noise has a regressive effect in descriptive probability estimation, moving probability estimates away from normative probabilities and toward the center of the probability scale. This random noise has an anti-regressive effect in inferential judgement, however. These regressive and anti-regressive effects explain various reliable and systematic biases seen in people's descriptive probability estimation and inferential probability judgment. This model predicts that these contrary effects will tend to cancel out in tasks that involve both descriptive estimation and inferential judgement, leading to unbiased responses in those tasks. We test this model by applying it to one such task, described by Gallistel et al. ). Participants' median responses in this task were unbiased, agreeing with normative probability theory over the full range of responses. Our model captures the pattern of unbiased responses in this task, while simultaneously explaining systematic biases away from normatively correct probabilities seen in other tasks. Copyright © 2018 Cognitive Science Society, Inc.

  1. The pairwise disconnectivity index as a new metric for the topological analysis of regulatory networks

    Directory of Open Access Journals (Sweden)

    Wingender Edgar

    2008-05-01

    Full Text Available Abstract Background Currently, there is a gap between purely theoretical studies of the topology of large bioregulatory networks and the practical traditions and interests of experimentalists. While the theoretical approaches emphasize the global characterization of regulatory systems, the practical approaches focus on the role of distinct molecules and genes in regulation. To bridge the gap between these opposite approaches, one needs to combine 'general' with 'particular' properties and translate abstract topological features of large systems into testable functional characteristics of individual components. Here, we propose a new topological parameter – the pairwise disconnectivity index of a network's element – that is capable of such bridging. Results The pairwise disconnectivity index quantifies how crucial an individual element is for sustaining the communication ability between connected pairs of vertices in a network that is displayed as a directed graph. Such an element might be a vertex (i.e., molecules, genes, an edge (i.e., reactions, interactions, as well as a group of vertices and/or edges. The index can be viewed as a measure of topological redundancy of regulatory paths which connect different parts of a given network and as a measure of sensitivity (robustness of this network to the presence (absence of each individual element. Accordingly, we introduce the notion of a path-degree of a vertex in terms of its corresponding incoming, outgoing and mediated paths, respectively. The pairwise disconnectivity index has been applied to the analysis of several regulatory networks from various organisms. The importance of an individual vertex or edge for the coherence of the network is determined by the particular position of the given element in the whole network. Conclusion Our approach enables to evaluate the effect of removing each element (i.e., vertex, edge, or their combinations from a network. The greatest potential value of

  2. Risk estimation using probability machines

    Science.gov (United States)

    2014-01-01

    Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

  3. Two-slit experiment: quantum and classical probabilities

    International Nuclear Information System (INIS)

    Khrennikov, Andrei

    2015-01-01

    Inter-relation between quantum and classical probability models is one of the most fundamental problems of quantum foundations. Nowadays this problem also plays an important role in quantum technologies, in quantum cryptography and the theory of quantum random generators. In this letter, we compare the viewpoint of Richard Feynman that the behavior of quantum particles cannot be described by classical probability theory with the viewpoint that quantum–classical inter-relation is more complicated (cf, in particular, with the tomographic model of quantum mechanics developed in detail by Vladimir Man'ko). As a basic example, we consider the two-slit experiment, which played a crucial role in quantum foundational debates at the beginning of quantum mechanics (QM). In particular, its analysis led Niels Bohr to the formulation of the principle of complementarity. First, we demonstrate that in complete accordance with Feynman's viewpoint, the probabilities for the two-slit experiment have the non-Kolmogorovian structure, since they violate one of basic laws of classical probability theory, the law of total probability (the heart of the Bayesian analysis). However, then we show that these probabilities can be embedded in a natural way into the classical (Kolmogorov, 1933) probability model. To do this, one has to take into account the randomness of selection of different experimental contexts, the joint consideration of which led Feynman to a conclusion about the non-classicality of quantum probability. We compare this embedding of non-Kolmogorovian quantum probabilities into the Kolmogorov model with well-known embeddings of non-Euclidean geometries into Euclidean space (e.g., the Poincaré disk model for the Lobachvesky plane). (paper)

  4. Calibration of Smartphone-Based Weather Measurements Using Pairwise Gossip

    Directory of Open Access Journals (Sweden)

    Jane Louie Fresco Zamora

    2015-01-01

    Full Text Available Accurate and reliable daily global weather reports are necessary for weather forecasting and climate analysis. However, the availability of these reports continues to decline due to the lack of economic support and policies in maintaining ground weather measurement systems from where these reports are obtained. Thus, to mitigate data scarcity, it is required to utilize weather information from existing sensors and built-in smartphone sensors. However, as smartphone usage often varies according to human activity, it is difficult to obtain accurate measurement data. In this paper, we present a heuristic-based pairwise gossip algorithm that will calibrate smartphone-based pressure sensors with respect to fixed weather stations as our referential ground truth. Based on actual measurements, we have verified that smartphone-based readings are unstable when observed during movement. Using our calibration algorithm on actual smartphone-based pressure readings, the updated values were significantly closer to the ground truth values.

  5. Alternative probability theories for cognitive psychology.

    Science.gov (United States)

    Narens, Louis

    2014-01-01

    Various proposals for generalizing event spaces for probability functions have been put forth in the mathematical, scientific, and philosophic literatures. In cognitive psychology such generalizations are used for explaining puzzling results in decision theory and for modeling the influence of context effects. This commentary discusses proposals for generalizing probability theory to event spaces that are not necessarily boolean algebras. Two prominent examples are quantum probability theory, which is based on the set of closed subspaces of a Hilbert space, and topological probability theory, which is based on the set of open sets of a topology. Both have been applied to a variety of cognitive situations. This commentary focuses on how event space properties can influence probability concepts and impact cognitive modeling. Copyright © 2013 Cognitive Science Society, Inc.

  6. Criteria for the singularity of a pairwise l1-distance matrix and their generalizations

    International Nuclear Information System (INIS)

    D'yakonov, Alexander G

    2012-01-01

    We study the singularity problem for the pairwise distance matrix of a system of points, as well as generalizations of this problem that are connected with applications to interpolation theory and with an algebraic approach to recognition problems. We obtain necessary and sufficient conditions on a system under which the dimension of the range space of polynomials of bounded degree over the columns of the distance matrix is less than the number of points in the system.

  7. The FOLDALIGN web server for pairwise structural RNA alignment and mutual motif search

    DEFF Research Database (Denmark)

    Havgaard, Jakob Hull; Lyngsø, Rune B.; Gorodkin, Jan

    2005-01-01

    FOLDALIGN is a Sankoff-based algorithm for making structural alignments of RNA sequences. Here, we present a web server for making pairwise alignments between two RNA sequences, using the recently updated version of FOLDALIGN. The server can be used to scan two sequences for a common structural RNA...... motif of limited size, or the entire sequences can be aligned locally or globally. The web server offers a graphical interface, which makes it simple to make alignments and manually browse the results. the web server can be accessed at http://foldalign.kvl.dk...

  8. Transition probability spaces in loop quantum gravity

    Science.gov (United States)

    Guo, Xiao-Kan

    2018-03-01

    We study the (generalized) transition probability spaces, in the sense of Mielnik and Cantoni, for spacetime quantum states in loop quantum gravity. First, we show that loop quantum gravity admits the structures of transition probability spaces. This is exemplified by first checking such structures in covariant quantum mechanics and then identifying the transition probability spaces in spin foam models via a simplified version of general boundary formulation. The transition probability space thus defined gives a simple way to reconstruct the discrete analog of the Hilbert space of the canonical theory and the relevant quantum logical structures. Second, we show that the transition probability space and in particular the spin foam model are 2-categories. Then we discuss how to realize in spin foam models two proposals by Crane about the mathematical structures of quantum gravity, namely, the quantum topos and causal sites. We conclude that transition probability spaces provide us with an alternative framework to understand various foundational questions of loop quantum gravity.

  9. OL-DEC-MDP Model for Multiagent Online Scheduling with a Time-Dependent Probability of Success

    Directory of Open Access Journals (Sweden)

    Cheng Zhu

    2014-01-01

    Full Text Available Focusing on the on-line multiagent scheduling problem, this paper considers the time-dependent probability of success and processing duration and proposes an OL-DEC-MDP (opportunity loss-decentralized Markov Decision Processes model to include opportunity loss into scheduling decision to improve overall performance. The success probability of job processing as well as the process duration is dependent on the time at which the processing is started. The probability of completing the assigned job by an agent would be higher when the process is started earlier, but the opportunity loss could also be high due to the longer engaging duration. As a result, OL-DEC-MDP model introduces a reward function considering the opportunity loss, which is estimated based on the prediction of the upcoming jobs by a sampling method on the job arrival. Heuristic strategies are introduced in computing the best starting time for an incoming job by each agent, and an incoming job will always be scheduled to the agent with the highest reward among all agents with their best starting policies. The simulation experiments show that the OL-DEC-MDP model will improve the overall scheduling performance compared with models not considering opportunity loss in heavy-loading environment.

  10. Interpreting and Understanding Logits, Probits, and other Non-Linear Probability Models

    DEFF Research Database (Denmark)

    Breen, Richard; Karlson, Kristian Bernt; Holm, Anders

    2018-01-01

    Methods textbooks in sociology and other social sciences routinely recommend the use of the logit or probit model when an outcome variable is binary, an ordered logit or ordered probit when it is ordinal, and a multinomial logit when it has more than two categories. But these methodological...... guidelines take little or no account of a body of work that, over the past 30 years, has pointed to problematic aspects of these nonlinear probability models and, particularly, to difficulties in interpreting their parameters. In this chapterreview, we draw on that literature to explain the problems, show...

  11. Estimating Subjective Probabilities

    DEFF Research Database (Denmark)

    Andersen, Steffen; Fountain, John; Harrison, Glenn W.

    2014-01-01

    either construct elicitation mechanisms that control for risk aversion, or construct elicitation mechanisms which undertake 'calibrating adjustments' to elicited reports. We illustrate how the joint estimation of risk attitudes and subjective probabilities can provide the calibration adjustments...... that theory calls for. We illustrate this approach using data from a controlled experiment with real monetary consequences to the subjects. This allows the observer to make inferences about the latent subjective probability, under virtually any well-specified model of choice under subjective risk, while still...

  12. Predicting Cumulative Incidence Probability by Direct Binomial Regression

    DEFF Research Database (Denmark)

    Scheike, Thomas H.; Zhang, Mei-Jie

    Binomial modelling; cumulative incidence probability; cause-specific hazards; subdistribution hazard......Binomial modelling; cumulative incidence probability; cause-specific hazards; subdistribution hazard...

  13. Blind Students' Learning of Probability through the Use of a Tactile Model

    Science.gov (United States)

    Vita, Aida Carvalho; Kataoka, Verônica Yumi

    2014-01-01

    The objective of this paper is to discuss how blind students learn basic concepts of probability using the tactile model proposed by Vita (2012). Among the activities were part of the teaching sequence "Jefferson's Random Walk", in which students built a tree diagram (using plastic trays, foam cards, and toys), and pictograms in 3D…

  14. Effect of pairwise additivity on finite-temperature behavior of classical ideal gas

    Science.gov (United States)

    Shekaari, Ashkan; Jafari, Mahmoud

    2018-05-01

    Finite-temperature molecular dynamics simulations have been applied to inquire into the effect of pairwise additivity on the behavior of classical ideal gas within the temperature range of T = 250-4000 K via applying a variety of pair potentials and then examining the temperature dependence of a number of thermodynamical properties. Examining the compressibility factor reveals the most deviation from ideal-gas behavior for the Lennard-Jones system mainly due to the presence of both the attractive and repulsive terms. The systems with either attractive or repulsive intermolecular potentials are found to present no resemblance to real gases, but the most similarity to the ideal one as temperature rises.

  15. Probability Distributome: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions.

    Science.gov (United States)

    Dinov, Ivo D; Siegrist, Kyle; Pearl, Dennis K; Kalinin, Alexandr; Christou, Nicolas

    2016-06-01

    Probability distributions are useful for modeling, simulation, analysis, and inference on varieties of natural processes and physical phenomena. There are uncountably many probability distributions. However, a few dozen families of distributions are commonly defined and are frequently used in practice for problem solving, experimental applications, and theoretical studies. In this paper, we present a new computational and graphical infrastructure, the Distributome , which facilitates the discovery, exploration and application of diverse spectra of probability distributions. The extensible Distributome infrastructure provides interfaces for (human and machine) traversal, search, and navigation of all common probability distributions. It also enables distribution modeling, applications, investigation of inter-distribution relations, as well as their analytical representations and computational utilization. The entire Distributome framework is designed and implemented as an open-source, community-built, and Internet-accessible infrastructure. It is portable, extensible and compatible with HTML5 and Web2.0 standards (http://Distributome.org). We demonstrate two types of applications of the probability Distributome resources: computational research and science education. The Distributome tools may be employed to address five complementary computational modeling applications (simulation, data-analysis and inference, model-fitting, examination of the analytical, mathematical and computational properties of specific probability distributions, and exploration of the inter-distributional relations). Many high school and college science, technology, engineering and mathematics (STEM) courses may be enriched by the use of modern pedagogical approaches and technology-enhanced methods. The Distributome resources provide enhancements for blended STEM education by improving student motivation, augmenting the classical curriculum with interactive webapps, and overhauling the

  16. [Biometric bases: basic concepts of probability calculation].

    Science.gov (United States)

    Dinya, E

    1998-04-26

    The author gives or outline of the basic concepts of probability theory. The bases of the event algebra, definition of the probability, the classical probability model and the random variable are presented.

  17. Path Loss, Shadow Fading, and Line-Of-Sight Probability Models for 5G Urban Macro-Cellular Scenarios

    DEFF Research Database (Denmark)

    Sun, Shu; Thomas, Timothy; Rappaport, Theodore S.

    2015-01-01

    This paper presents key parameters including the line-of-sight (LOS) probability, large-scale path loss, and shadow fading models for the design of future fifth generation (5G) wireless communication systems in urban macro-cellular (UMa) scenarios, using the data obtained from propagation...... measurements in Austin, US, and Aalborg, Denmark, at 2, 10, 18, and 38 GHz. A comparison of different LOS probability models is performed for the Aalborg environment. Both single-slope and dual-slope omnidirectional path loss models are investigated to analyze and contrast their root-mean-square (RMS) errors...

  18. Predicting binary choices from probability phrase meanings.

    Science.gov (United States)

    Wallsten, Thomas S; Jang, Yoonhee

    2008-08-01

    The issues of how individuals decide which of two events is more likely and of how they understand probability phrases both involve judging relative likelihoods. In this study, we investigated whether derived scales representing probability phrase meanings could be used within a choice model to predict independently observed binary choices. If they can, this simultaneously provides support for our model and suggests that the phrase meanings are measured meaningfully. The model assumes that, when deciding which of two events is more likely, judges take a single sample from memory regarding each event and respond accordingly. The model predicts choice probabilities by using the scaled meanings of individually selected probability phrases as proxies for confidence distributions associated with sampling from memory. Predictions are sustained for 34 of 41 participants but, nevertheless, are biased slightly low. Sequential sampling models improve the fit. The results have both theoretical and applied implications.

  19. Atomic pairwise distribution function analysis of the amorphous phase prepared by different manufacturing routes

    DEFF Research Database (Denmark)

    Boetker, Johan P.; Koradia, Vishal; Rades, Thomas

    2012-01-01

    was subjected to quench cooling thereby creating an amorphous form of the drug from both starting materials. The milled and quench cooled samples were, together with the crystalline starting materials, analyzed with X-ray powder diffraction (XRPD), Raman spectroscopy and atomic pair-wise distribution function...... (PDF) analysis of the XRPD pattern. When compared to XRPD and Raman spectroscopy, the PDF analysis was superior in displaying the difference between the amorphous samples prepared by milling and quench cooling approaches of the two starting materials....

  20. Modeling Stress Strain Relationships and Predicting Failure Probabilities For Graphite Core Components

    Energy Technology Data Exchange (ETDEWEB)

    Duffy, Stephen [Cleveland State Univ., Cleveland, OH (United States)

    2013-09-09

    This project will implement inelastic constitutive models that will yield the requisite stress-strain information necessary for graphite component design. Accurate knowledge of stress states (both elastic and inelastic) is required to assess how close a nuclear core component is to failure. Strain states are needed to assess deformations in order to ascertain serviceability issues relating to failure, e.g., whether too much shrinkage has taken place for the core to function properly. Failure probabilities, as opposed to safety factors, are required in order to capture the bariability in failure strength in tensile regimes. The current stress state is used to predict the probability of failure. Stochastic failure models will be developed that can accommodate possible material anisotropy. This work will also model material damage (i.e., degradation of mechanical properties) due to radiation exposure. The team will design tools for components fabricated from nuclear graphite. These tools must readily interact with finite element software--in particular, COMSOL, the software algorithm currently being utilized by the Idaho National Laboratory. For the eleastic response of graphite, the team will adopt anisotropic stress-strain relationships available in COMSO. Data from the literature will be utilized to characterize the appropriate elastic material constants.

  1. Modeling Stress Strain Relationships and Predicting Failure Probabilities For Graphite Core Components

    International Nuclear Information System (INIS)

    Duffy, Stephen

    2013-01-01

    This project will implement inelastic constitutive models that will yield the requisite stress-strain information necessary for graphite component design. Accurate knowledge of stress states (both elastic and inelastic) is required to assess how close a nuclear core component is to failure. Strain states are needed to assess deformations in order to ascertain serviceability issues relating to failure, e.g., whether too much shrinkage has taken place for the core to function properly. Failure probabilities, as opposed to safety factors, are required in order to capture the bariability in failure strength in tensile regimes. The current stress state is used to predict the probability of failure. Stochastic failure models will be developed that can accommodate possible material anisotropy. This work will also model material damage (i.e., degradation of mechanical properties) due to radiation exposure. The team will design tools for components fabricated from nuclear graphite. These tools must readily interact with finite element software--in particular, COMSOL, the software algorithm currently being utilized by the Idaho National Laboratory. For the eleastic response of graphite, the team will adopt anisotropic stress-strain relationships available in COMSO. Data from the literature will be utilized to characterize the appropriate elastic material constants.

  2. Naming game with learning errors in communications

    OpenAIRE

    Lou, Yang; Chen, Guanrong

    2014-01-01

    Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network topology. By pair-wise iterative interactions, the population reaches a consensus state asymptotically. In this paper, we study naming game with communication errors during pair-wise conversations, where errors are represented by error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed....

  3. Analysis of the “naming game” with learning errors in communications

    OpenAIRE

    Yang Lou; Guanrong Chen

    2015-01-01

    Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is ...

  4. A transmission/escape probabilities model for neutral particle transport in the outer regions of a diverted tokamak

    International Nuclear Information System (INIS)

    Stacey, W.M.

    1992-12-01

    A new computational model for neutral particle transport in the outer regions of a diverted tokamak plasma chamber is presented. The model is based on the calculation of transmission and escape probabilities using first-flight integral transport theory and the balancing of fluxes across the surfaces bounding the various regions. The geometrical complexity of the problem is included in precomputed probabilities which depend only on the mean free path of the region

  5. Exact method for the simulation of Coulombic systems by spherically truncated, pairwise r-1 summation

    International Nuclear Information System (INIS)

    Wolf, D.; Keblinski, P.; Phillpot, S.R.; Eggebrecht, J.

    1999-01-01

    Based on a recent result showing that the net Coulomb potential in condensed ionic systems is rather short ranged, an exact and physically transparent method permitting the evaluation of the Coulomb potential by direct summation over the r -1 Coulomb pair potential is presented. The key observation is that the problems encountered in determining the Coulomb energy by pairwise, spherically truncated r -1 summation are a direct consequence of the fact that the system summed over is practically never neutral. A simple method is developed that achieves charge neutralization wherever the r -1 pair potential is truncated. This enables the extraction of the Coulomb energy, forces, and stresses from a spherically truncated, usually charged environment in a manner that is independent of the grouping of the pair terms. The close connection of our approach with the Ewald method is demonstrated and exploited, providing an efficient method for the simulation of even highly disordered ionic systems by direct, pairwise r -1 summation with spherical truncation at rather short range, i.e., a method which fully exploits the short-ranged nature of the interactions in ionic systems. The method is validated by simulations of crystals, liquids, and interfacial systems, such as free surfaces and grain boundaries. copyright 1999 American Institute of Physics

  6. Expected utility with lower probabilities

    DEFF Research Database (Denmark)

    Hendon, Ebbe; Jacobsen, Hans Jørgen; Sloth, Birgitte

    1994-01-01

    An uncertain and not just risky situation may be modeled using so-called belief functions assigning lower probabilities to subsets of outcomes. In this article we extend the von Neumann-Morgenstern expected utility theory from probability measures to belief functions. We use this theory...

  7. High-resolution urban flood modelling - a joint probability approach

    Science.gov (United States)

    Hartnett, Michael; Olbert, Agnieszka; Nash, Stephen

    2017-04-01

    (Divoky et al., 2005). Nevertheless, such events occur and in Ireland alone there are several cases of serious damage due to flooding resulting from a combination of high sea water levels and river flows driven by the same meteorological conditions (e.g. Olbert et al. 2015). A November 2009 fluvial-coastal flooding of Cork City bringing €100m loss was one such incident. This event was used by Olbert et al. (2015) to determine processes controlling urban flooding and is further explored in this study to elaborate on coastal and fluvial flood mechanisms and their roles in controlling water levels. The objective of this research is to develop a methodology to assess combined effect of multiple source flooding on flood probability and severity in urban areas and to establish a set of conditions that dictate urban flooding due to extreme climatic events. These conditions broadly combine physical flood drivers (such as coastal and fluvial processes), their mechanisms and thresholds defining flood severity. The two main physical processes controlling urban flooding: high sea water levels (coastal flooding) and high river flows (fluvial flooding), and their threshold values for which flood is likely to occur, are considered in this study. Contribution of coastal and fluvial drivers to flooding and their impacts are assessed in a two-step process. The first step involves frequency analysis and extreme value statistical modelling of storm surges, tides and river flows and ultimately the application of joint probability method to estimate joint exceedence return periods for combination of surges, tide and river flows. In the second step, a numerical model of Cork Harbour MSN_Flood comprising a cascade of four nested high-resolution models is used to perform simulation of flood inundation under numerous hypothetical coastal and fluvial flood scenarios. The risk of flooding is quantified based on a range of physical aspects such as the extent and depth of inundation (Apel et al

  8. Time series modeling of pathogen-specific disease probabilities with subsampled data.

    Science.gov (United States)

    Fisher, Leigh; Wakefield, Jon; Bauer, Cici; Self, Steve

    2017-03-01

    Many diseases arise due to exposure to one of multiple possible pathogens. We consider the situation in which disease counts are available over time from a study region, along with a measure of clinical disease severity, for example, mild or severe. In addition, we suppose a subset of the cases are lab tested in order to determine the pathogen responsible for disease. In such a context, we focus interest on modeling the probabilities of disease incidence given pathogen type. The time course of these probabilities is of great interest as is the association with time-varying covariates such as meteorological variables. In this set up, a natural Bayesian approach would be based on imputation of the unsampled pathogen information using Markov Chain Monte Carlo but this is computationally challenging. We describe a practical approach to inference that is easy to implement. We use an empirical Bayes procedure in a first step to estimate summary statistics. We then treat these summary statistics as the observed data and develop a Bayesian generalized additive model. We analyze data on hand, foot, and mouth disease (HFMD) in China in which there are two pathogens of primary interest, enterovirus 71 (EV71) and Coxackie A16 (CA16). We find that both EV71 and CA16 are associated with temperature, relative humidity, and wind speed, with reasonably similar functional forms for both pathogens. The important issue of confounding by time is modeled using a penalized B-spline model with a random effects representation. The level of smoothing is addressed by a careful choice of the prior on the tuning variance. © 2016, The International Biometric Society.

  9. Various models for pion probability distributions from heavy-ion collisions

    International Nuclear Information System (INIS)

    Mekjian, A.Z.; Mekjian, A.Z.; Schlei, B.R.; Strottman, D.; Schlei, B.R.

    1998-01-01

    Various models for pion multiplicity distributions produced in relativistic heavy ion collisions are discussed. The models include a relativistic hydrodynamic model, a thermodynamic description, an emitting source pion laser model, and a description which generates a negative binomial description. The approach developed can be used to discuss other cases which will be mentioned. The pion probability distributions for these various cases are compared. Comparison of the pion laser model and Bose-Einstein condensation in a laser trap and with the thermal model are made. The thermal model and hydrodynamic model are also used to illustrate why the number of pions never diverges and why the Bose-Einstein correction effects are relatively small. The pion emission strength η of a Poisson emitter and a critical density η c are connected in a thermal model by η/n c =e -m/T <1, and this fact reduces any Bose-Einstein correction effects in the number and number fluctuation of pions. Fluctuations can be much larger than Poisson in the pion laser model and for a negative binomial description. The clan representation of the negative binomial distribution due to Van Hove and Giovannini is discussed using the present description. Applications to CERN/NA44 and CERN/NA49 data are discussed in terms of the relativistic hydrodynamic model. copyright 1998 The American Physical Society

  10. Influences of variables on ship collision probability in a Bayesian belief network model

    International Nuclear Information System (INIS)

    Hänninen, Maria; Kujala, Pentti

    2012-01-01

    The influences of the variables in a Bayesian belief network model for estimating the role of human factors on ship collision probability in the Gulf of Finland are studied for discovering the variables with the largest influences and for examining the validity of the network. The change in the so-called causation probability is examined while observing each state of the network variables and by utilizing sensitivity and mutual information analyses. Changing course in an encounter situation is the most influential variable in the model, followed by variables such as the Officer of the Watch's action, situation assessment, danger detection, personal condition and incapacitation. The least influential variables are the other distractions on bridge, the bridge view, maintenance routines and the officer's fatigue. In general, the methods are found to agree on the order of the model variables although some disagreements arise due to slightly dissimilar approaches to the concept of variable influence. The relative values and the ranking of variables based on the values are discovered to be more valuable than the actual numerical values themselves. Although the most influential variables seem to be plausible, there are some discrepancies between the indicated influences in the model and literature. Thus, improvements are suggested to the network.

  11. Pair-Wise and Many-Body Dispersive Interactions Coupled to an Optimally Tuned Range-Separated Hybrid Functional.

    Science.gov (United States)

    Agrawal, Piyush; Tkatchenko, Alexandre; Kronik, Leeor

    2013-08-13

    We propose a nonempirical, pair-wise or many-body dispersion-corrected, optimally tuned range-separated hybrid functional. This functional retains the advantages of the optimal-tuning approach in the prediction of the electronic structure. At the same time, it gains accuracy in the prediction of binding energies for dispersively bound systems, as demonstrated on the S22 and S66 benchmark sets of weakly bound dimers.

  12. Quantification of a decision-making failure probability of the accident management using cognitive analysis model

    Energy Technology Data Exchange (ETDEWEB)

    Yoshida, Yoshitaka; Ohtani, Masanori [Institute of Nuclear Safety System, Inc., Mihama, Fukui (Japan); Fujita, Yushi [TECNOVA Corp., Tokyo (Japan)

    2002-09-01

    In the nuclear power plant, much knowledge is acquired through probabilistic safety assessment (PSA) of a severe accident, and accident management (AM) is prepared. It is necessary to evaluate the effectiveness of AM using the decision-making failure probability of an emergency organization, operation failure probability of operators, success criteria of AM and reliability of AM equipments in PSA. However, there has been no suitable qualification method for PSA so far to obtain the decision-making failure probability, because the decision-making failure of an emergency organization treats the knowledge based error. In this work, we developed a new method for quantification of the decision-making failure probability of an emergency organization using cognitive analysis model, which decided an AM strategy, in a nuclear power plant at the severe accident, and tried to apply it to a typical pressurized water reactor (PWR) plant. As a result: (1) It could quantify the decision-making failure probability adjusted to PSA for general analysts, who do not necessarily possess professional human factors knowledge, by choosing the suitable value of a basic failure probability and an error-factor. (2) The decision-making failure probabilities of six AMs were in the range of 0.23 to 0.41 using the screening evaluation method and in the range of 0.10 to 0.19 using the detailed evaluation method as the result of trial evaluation based on severe accident analysis of a typical PWR plant, and a result of sensitivity analysis of the conservative assumption, failure probability decreased about 50%. (3) The failure probability using the screening evaluation method exceeded that using detailed evaluation method by 99% of probability theoretically, and the failure probability of AM in this study exceeded 100%. From this result, it was shown that the decision-making failure probability was more conservative than the detailed evaluation method, and the screening evaluation method satisfied

  13. Quantification of a decision-making failure probability of the accident management using cognitive analysis model

    International Nuclear Information System (INIS)

    Yoshida, Yoshitaka; Ohtani, Masanori; Fujita, Yushi

    2002-01-01

    In the nuclear power plant, much knowledge is acquired through probabilistic safety assessment (PSA) of a severe accident, and accident management (AM) is prepared. It is necessary to evaluate the effectiveness of AM using the decision-making failure probability of an emergency organization, operation failure probability of operators, success criteria of AM and reliability of AM equipments in PSA. However, there has been no suitable qualification method for PSA so far to obtain the decision-making failure probability, because the decision-making failure of an emergency organization treats the knowledge based error. In this work, we developed a new method for quantification of the decision-making failure probability of an emergency organization using cognitive analysis model, which decided an AM strategy, in a nuclear power plant at the severe accident, and tried to apply it to a typical pressurized water reactor (PWR) plant. As a result: (1) It could quantify the decision-making failure probability adjusted to PSA for general analysts, who do not necessarily possess professional human factors knowledge, by choosing the suitable value of a basic failure probability and an error-factor. (2) The decision-making failure probabilities of six AMs were in the range of 0.23 to 0.41 using the screening evaluation method and in the range of 0.10 to 0.19 using the detailed evaluation method as the result of trial evaluation based on severe accident analysis of a typical PWR plant, and a result of sensitivity analysis of the conservative assumption, failure probability decreased about 50%. (3) The failure probability using the screening evaluation method exceeded that using detailed evaluation method by 99% of probability theoretically, and the failure probability of AM in this study exceeded 100%. From this result, it was shown that the decision-making failure probability was more conservative than the detailed evaluation method, and the screening evaluation method satisfied

  14. Computing the Skewness of the Phylogenetic Mean Pairwise Distance in Linear Time

    DEFF Research Database (Denmark)

    Tsirogiannis, Constantinos; Sandel, Brody Steven

    2014-01-01

    The phylogenetic Mean Pairwise Distance (MPD) is one of the most popular measures for computing the phylogenetic distance between a given group of species. More specifically, for a phylogenetic tree and for a set of species R represented by a subset of the leaf nodes of , the MPD of R is equal...... to the average cost of all possible simple paths in that connect pairs of nodes in R. Among other phylogenetic measures, the MPD is used as a tool for deciding if the species of a given group R are closely related. To do this, it is important to compute not only the value of the MPD for this group but also...

  15. Classification of forest-based ecotourism areas in Pocahontas County of West Virginia using GIS and pairwise comparison method

    Science.gov (United States)

    Ishwar Dhami; Jinyang. Deng

    2012-01-01

    Many previous studies have examined ecotourism primarily from the perspective of tourists while largely ignoring ecotourism destinations. This study used geographical information system (GIS) and pairwise comparison to identify forest-based ecotourism areas in Pocahontas County, West Virginia. The study adopted the criteria and scores developed by Boyd and Butler (1994...

  16. A cellular automata model of traffic flow with variable probability of randomization

    International Nuclear Information System (INIS)

    Zheng Wei-Fan; Zhang Ji-Ye

    2015-01-01

    Research on the stochastic behavior of traffic flow is important to understand the intrinsic evolution rules of a traffic system. By introducing an interactional potential of vehicles into the randomization step, an improved cellular automata traffic flow model with variable probability of randomization is proposed in this paper. In the proposed model, the driver is affected by the interactional potential of vehicles before him, and his decision-making process is related to the interactional potential. Compared with the traditional cellular automata model, the modeling is more suitable for the driver’s random decision-making process based on the vehicle and traffic situations in front of him in actual traffic. From the improved model, the fundamental diagram (flow–density relationship) is obtained, and the detailed high-density traffic phenomenon is reproduced through numerical simulation. (paper)

  17. PTM Along Track Algorithm to Maintain Spacing During Same Direction Pair-Wise Trajectory Management Operations

    Science.gov (United States)

    Carreno, Victor A.

    2015-01-01

    Pair-wise Trajectory Management (PTM) is a cockpit based delegated responsibility separation standard. When an air traffic service provider gives a PTM clearance to an aircraft and the flight crew accepts the clearance, the flight crew will maintain spacing and separation from a designated aircraft. A PTM along track algorithm will receive state information from the designated aircraft and from the own ship to produce speed guidance for the flight crew to maintain spacing and separation

  18. Criteria for the singularity of a pairwise l{sub 1}-distance matrix and their generalizations

    Energy Technology Data Exchange (ETDEWEB)

    D' yakonov, Alexander G [M. V. Lomonosov Moscow State University, Faculty of Computational Mathematics and Cybernetics, Moscow (Russian Federation)

    2012-06-30

    We study the singularity problem for the pairwise distance matrix of a system of points, as well as generalizations of this problem that are connected with applications to interpolation theory and with an algebraic approach to recognition problems. We obtain necessary and sufficient conditions on a system under which the dimension of the range space of polynomials of bounded degree over the columns of the distance matrix is less than the number of points in the system.

  19. Collision Probability Analysis

    DEFF Research Database (Denmark)

    Hansen, Peter Friis; Pedersen, Preben Terndrup

    1998-01-01

    It is the purpose of this report to apply a rational model for prediction of ship-ship collision probabilities as function of the ship and the crew characteristics and the navigational environment for MS Dextra sailing on a route between Cadiz and the Canary Islands.The most important ship and crew...... characteristics are: ship speed, ship manoeuvrability, the layout of the navigational bridge, the radar system, the number and the training of navigators, the presence of a look out etc. The main parameters affecting the navigational environment are ship traffic density, probability distributions of wind speeds...... probability, i.e. a study of the navigator's role in resolving critical situations, a causation factor is derived as a second step.The report documents the first step in a probabilistic collision damage analysis. Future work will inlcude calculation of energy released for crushing of structures giving...

  20. Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment scores.

    Science.gov (United States)

    Bastien, Olivier; Maréchal, Eric

    2008-08-07

    Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic data. Two statistical models have been proposed. In the asymptotic limit of long sequences, the Karlin-Altschul model is based on the computation of a P-value, assuming that the number of high scoring matching regions above a threshold is Poisson distributed. Alternatively, the Lipman-Pearson model is based on the computation of a Z-value from a random score distribution obtained by a Monte-Carlo simulation. Z-values allow the deduction of an upper bound of the P-value (1/Z-value2) following the TULIP theorem. Simulations of Z-value distribution is known to fit with a Gumbel law. This remarkable property was not demonstrated and had no obvious biological support. We built a model of evolution of sequences based on aging, as meant in Reliability Theory, using the fact that the amount of information shared between an initial sequence and the sequences in its lineage (i.e., mutual information in Information Theory) is a decreasing function of time. This quantity is simply measured by a sequence alignment score. In systems aging, the failure rate is related to the systems longevity. The system can be a machine with structured components, or a living entity or population. "Reliability" refers to the ability to operate properly according to a standard. Here, the "reliability" of a sequence refers to the ability to conserve a sufficient functional level at the folded and maturated protein level (positive selection pressure). Homologous sequences were considered as systems 1) having a high redundancy of information reflected by the magnitude of their alignment scores, 2) which components are the amino acids that can independently be damaged by random DNA mutations. From these assumptions, we deduced that information shared at each amino acid position evolved with a constant rate, corresponding to the

  1. Generalized Probability-Probability Plots

    NARCIS (Netherlands)

    Mushkudiani, N.A.; Einmahl, J.H.J.

    2004-01-01

    We introduce generalized Probability-Probability (P-P) plots in order to study the one-sample goodness-of-fit problem and the two-sample problem, for real valued data.These plots, that are constructed by indexing with the class of closed intervals, globally preserve the properties of classical P-P

  2. Quantum Probabilities as Behavioral Probabilities

    Directory of Open Access Journals (Sweden)

    Vyacheslav I. Yukalov

    2017-03-01

    Full Text Available We demonstrate that behavioral probabilities of human decision makers share many common features with quantum probabilities. This does not imply that humans are some quantum objects, but just shows that the mathematics of quantum theory is applicable to the description of human decision making. The applicability of quantum rules for describing decision making is connected with the nontrivial process of making decisions in the case of composite prospects under uncertainty. Such a process involves deliberations of a decision maker when making a choice. In addition to the evaluation of the utilities of considered prospects, real decision makers also appreciate their respective attractiveness. Therefore, human choice is not based solely on the utility of prospects, but includes the necessity of resolving the utility-attraction duality. In order to justify that human consciousness really functions similarly to the rules of quantum theory, we develop an approach defining human behavioral probabilities as the probabilities determined by quantum rules. We show that quantum behavioral probabilities of humans do not merely explain qualitatively how human decisions are made, but they predict quantitative values of the behavioral probabilities. Analyzing a large set of empirical data, we find good quantitative agreement between theoretical predictions and observed experimental data.

  3. Classical probability model for Bell inequality

    International Nuclear Information System (INIS)

    Khrennikov, Andrei

    2014-01-01

    We show that by taking into account randomness of realization of experimental contexts it is possible to construct common Kolmogorov space for data collected for these contexts, although they can be incompatible. We call such a construction 'Kolmogorovization' of contextuality. This construction of common probability space is applied to Bell's inequality. It is well known that its violation is a consequence of collecting statistical data in a few incompatible experiments. In experiments performed in quantum optics contexts are determined by selections of pairs of angles (θ i ,θ ' j ) fixing orientations of polarization beam splitters. Opposite to the common opinion, we show that statistical data corresponding to measurements of polarizations of photons in the singlet state, e.g., in the form of correlations, can be described in the classical probabilistic framework. The crucial point is that in constructing the common probability space one has to take into account not only randomness of the source (as Bell did), but also randomness of context-realizations (in particular, realizations of pairs of angles (θ i , θ ' j )). One may (but need not) say that randomness of 'free will' has to be accounted for.

  4. Modeling spatial variability of sand-lenses in clay till settings using transition probability and multiple-point geostatistics

    DEFF Research Database (Denmark)

    Kessler, Timo Christian; Nilsson, Bertel; Klint, Knud Erik

    2010-01-01

    (TPROGS) of alternating geological facies. The second method, multiple-point statistics, uses training images to estimate the conditional probability of sand-lenses at a certain location. Both methods respect field observations such as local stratigraphy, however, only the multiple-point statistics can...... of sand-lenses in clay till. Sand-lenses mainly account for horizontal transport and are prioritised in this study. Based on field observations, the distribution has been modeled using two different geostatistical approaches. One method uses a Markov chain model calculating the transition probabilities...

  5. Probability Aggregates in Probability Answer Set Programming

    OpenAIRE

    Saad, Emad

    2013-01-01

    Probability answer set programming is a declarative programming that has been shown effective for representing and reasoning about a variety of probability reasoning tasks. However, the lack of probability aggregates, e.g. {\\em expected values}, in the language of disjunctive hybrid probability logic programs (DHPP) disallows the natural and concise representation of many interesting problems. In this paper, we extend DHPP to allow arbitrary probability aggregates. We introduce two types of p...

  6. Probability modeling of high flow extremes in Yingluoxia watershed, the upper reaches of Heihe River basin

    Science.gov (United States)

    Li, Zhanling; Li, Zhanjie; Li, Chengcheng

    2014-05-01

    Probability modeling of hydrological extremes is one of the major research areas in hydrological science. Most basins in humid and semi-humid south and east of China are concerned for probability modeling analysis of high flow extremes. While, for the inland river basin which occupies about 35% of the country area, there is a limited presence of such studies partly due to the limited data availability and a relatively low mean annual flow. The objective of this study is to carry out probability modeling of high flow extremes in the upper reach of Heihe River basin, the second largest inland river basin in China, by using the peak over threshold (POT) method and Generalized Pareto Distribution (GPD), in which the selection of threshold and inherent assumptions for POT series are elaborated in details. For comparison, other widely used probability distributions including generalized extreme value (GEV), Lognormal, Log-logistic and Gamma are employed as well. Maximum likelihood estimate is used for parameter estimations. Daily flow data at Yingluoxia station from 1978 to 2008 are used. Results show that, synthesizing the approaches of mean excess plot, stability features of model parameters, return level plot and the inherent independence assumption of POT series, an optimum threshold of 340m3/s is finally determined for high flow extremes in Yingluoxia watershed. The resulting POT series is proved to be stationary and independent based on Mann-Kendall test, Pettitt test and autocorrelation test. In terms of Kolmogorov-Smirnov test, Anderson-Darling test and several graphical diagnostics such as quantile and cumulative density function plots, GPD provides the best fit to high flow extremes in the study area. The estimated high flows for long return periods demonstrate that, as the return period increasing, the return level estimates are probably more uncertain. The frequency of high flow extremes exhibits a very slight but not significant decreasing trend from 1978 to

  7. Determination of probability density functions for parameters in the Munson-Dawson model for creep behavior of salt

    International Nuclear Information System (INIS)

    Pfeifle, T.W.; Mellegard, K.D.; Munson, D.E.

    1992-10-01

    The modified Munson-Dawson (M-D) constitutive model that describes the creep behavior of salt will be used in performance assessment calculations to assess compliance of the Waste Isolation Pilot Plant (WIPP) facility with requirements governing the disposal of nuclear waste. One of these standards requires that the uncertainty of future states of the system, material model parameters, and data be addressed in the performance assessment models. This paper presents a method in which measurement uncertainty and the inherent variability of the material are characterized by treating the M-D model parameters as random variables. The random variables can be described by appropriate probability distribution functions which then can be used in Monte Carlo or structural reliability analyses. Estimates of three random variables in the M-D model were obtained by fitting a scalar form of the model to triaxial compression creep data generated from tests of WIPP salt. Candidate probability distribution functions for each of the variables were then fitted to the estimates and their relative goodness-of-fit tested using the Kolmogorov-Smirnov statistic. A sophisticated statistical software package obtained from BMDP Statistical Software, Inc. was used in the M-D model fitting. A separate software package, STATGRAPHICS, was used in fitting the candidate probability distribution functions to estimates of the variables. Skewed distributions, i.e., lognormal and Weibull, were found to be appropriate for the random variables analyzed

  8. Developing a Mathematical Model for Scheduling and Determining Success Probability of Research Projects Considering Complex-Fuzzy Networks

    Directory of Open Access Journals (Sweden)

    Gholamreza Norouzi

    2015-01-01

    Full Text Available In project management context, time management is one of the most important factors affecting project success. This paper proposes a new method to solve research project scheduling problems (RPSP containing Fuzzy Graphical Evaluation and Review Technique (FGERT networks. Through the deliverables of this method, a proper estimation of project completion time (PCT and success probability can be achieved. So algorithms were developed to cover all features of the problem based on three main parameters “duration, occurrence probability, and success probability.” These developed algorithms were known as PR-FGERT (Parallel and Reversible-Fuzzy GERT networks. The main provided framework includes simplifying the network of project and taking regular steps to determine PCT and success probability. Simplifications include (1 equivalent making of parallel and series branches in fuzzy network considering the concepts of probabilistic nodes, (2 equivalent making of delay or reversible-to-itself branches and impact of changing the parameters of time and probability based on removing related branches, (3 equivalent making of simple and complex loops, and (4 an algorithm that was provided to resolve no-loop fuzzy network, after equivalent making. Finally, the performance of models was compared with existing methods. The results showed proper and real performance of models in comparison with existing methods.

  9. A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models

    International Nuclear Information System (INIS)

    Echard, B.; Gayton, N.; Lemaire, M.; Relun, N.

    2013-01-01

    Applying reliability methods to a complex structure is often delicate for two main reasons. First, such a structure is fortunately designed with codified rules leading to a large safety margin which means that failure is a small probability event. Such a probability level is difficult to assess efficiently. Second, the structure mechanical behaviour is modelled numerically in an attempt to reproduce the real response and numerical model tends to be more and more time-demanding as its complexity is increased to improve accuracy and to consider particular mechanical behaviour. As a consequence, performing a large number of model computations cannot be considered in order to assess the failure probability. To overcome these issues, this paper proposes an original and easily implementable method called AK-IS for active learning and Kriging-based Importance Sampling. This new method is based on the AK-MCS algorithm previously published by Echard et al. [AK-MCS: an active learning reliability method combining Kriging and Monte Carlo simulation. Structural Safety 2011;33(2):145–54]. It associates the Kriging metamodel and its advantageous stochastic property with the Importance Sampling method to assess small failure probabilities. It enables the correction or validation of the FORM approximation with only a very few mechanical model computations. The efficiency of the method is, first, proved on two academic applications. It is then conducted for assessing the reliability of a challenging aerospace case study submitted to fatigue.

  10. Parasail: SIMD C library for global, semi-global, and local pairwise sequence alignments.

    Science.gov (United States)

    Daily, Jeff

    2016-02-10

    Sequence alignment algorithms are a key component of many bioinformatics applications. Though various fast Smith-Waterman local sequence alignment implementations have been developed for x86 CPUs, most are embedded into larger database search tools. In addition, fast implementations of Needleman-Wunsch global sequence alignment and its semi-global variants are not as widespread. This article presents the first software library for local, global, and semi-global pairwise intra-sequence alignments and improves the performance of previous intra-sequence implementations. A faster intra-sequence local pairwise alignment implementation is described and benchmarked, including new global and semi-global variants. Using a 375 residue query sequence a speed of 136 billion cell updates per second (GCUPS) was achieved on a dual Intel Xeon E5-2670 24-core processor system, the highest reported for an implementation based on Farrar's 'striped' approach. Rognes's SWIPE optimal database search application is still generally the fastest available at 1.2 to at best 2.4 times faster than Parasail for sequences shorter than 500 amino acids. However, Parasail was faster for longer sequences. For global alignments, Parasail's prefix scan implementation is generally the fastest, faster even than Farrar's 'striped' approach, however the opal library is faster for single-threaded applications. The software library is designed for 64 bit Linux, OS X, or Windows on processors with SSE2, SSE41, or AVX2. Source code is available from https://github.com/jeffdaily/parasail under the Battelle BSD-style license. Applications that require optimal alignment scores could benefit from the improved performance. For the first time, SIMD global, semi-global, and local alignments are available in a stand-alone C library.

  11. Identifying the Academic Rising Stars via Pairwise Citation Increment Ranking

    KAUST Repository

    Zhang, Chuxu

    2017-08-02

    Predicting the fast-rising young researchers (the Academic Rising Stars) in the future provides useful guidance to the research community, e.g., offering competitive candidates to university for young faculty hiring as they are expected to have success academic careers. In this work, given a set of young researchers who have published the first first-author paper recently, we solve the problem of how to effectively predict the top k% researchers who achieve the highest citation increment in Δt years. We explore a series of factors that can drive an author to be fast-rising and design a novel pairwise citation increment ranking (PCIR) method that leverages those factors to predict the academic rising stars. Experimental results on the large ArnetMiner dataset with over 1.7 million authors demonstrate the effectiveness of PCIR. Specifically, it outperforms all given benchmark methods, with over 8% average improvement. Further analysis demonstrates that temporal features are the best indicators for rising stars prediction, while venue features are less relevant.

  12. Fast Outage Probability Simulation for FSO Links with a Generalized Pointing Error Model

    KAUST Repository

    Ben Issaid, Chaouki

    2017-02-07

    Over the past few years, free-space optical (FSO) communication has gained significant attention. In fact, FSO can provide cost-effective and unlicensed links, with high-bandwidth capacity and low error rate, making it an exciting alternative to traditional wireless radio-frequency communication systems. However, the system performance is affected not only by the presence of atmospheric turbulences, which occur due to random fluctuations in the air refractive index but also by the existence of pointing errors. Metrics, such as the outage probability which quantifies the probability that the instantaneous signal-to-noise ratio is smaller than a given threshold, can be used to analyze the performance of this system. In this work, we consider weak and strong turbulence regimes, and we study the outage probability of an FSO communication system under a generalized pointing error model with both a nonzero boresight component and different horizontal and vertical jitter effects. More specifically, we use an importance sampling approach which is based on the exponential twisting technique to offer fast and accurate results.

  13. Fixation Probability in a Haploid-Diploid Population.

    Science.gov (United States)

    Bessho, Kazuhiro; Otto, Sarah P

    2017-01-01

    Classical population genetic theory generally assumes either a fully haploid or fully diploid life cycle. However, many organisms exhibit more complex life cycles, with both free-living haploid and diploid stages. Here we ask what the probability of fixation is for selected alleles in organisms with haploid-diploid life cycles. We develop a genetic model that considers the population dynamics using both the Moran model and Wright-Fisher model. Applying a branching process approximation, we obtain an accurate fixation probability assuming that the population is large and the net effect of the mutation is beneficial. We also find the diffusion approximation for the fixation probability, which is accurate even in small populations and for deleterious alleles, as long as selection is weak. These fixation probabilities from branching process and diffusion approximations are similar when selection is weak for beneficial mutations that are not fully recessive. In many cases, particularly when one phase predominates, the fixation probability differs substantially for haploid-diploid organisms compared to either fully haploid or diploid species. Copyright © 2017 by the Genetics Society of America.

  14. Uncovering the Best Skill Multimap by Constraining the Error Probabilities of the Gain-Loss Model

    Science.gov (United States)

    Anselmi, Pasquale; Robusto, Egidio; Stefanutti, Luca

    2012-01-01

    The Gain-Loss model is a probabilistic skill multimap model for assessing learning processes. In practical applications, more than one skill multimap could be plausible, while none corresponds to the true one. The article investigates whether constraining the error probabilities is a way of uncovering the best skill assignment among a number of…

  15. Finite element model updating of concrete structures based on imprecise probability

    Science.gov (United States)

    Biswal, S.; Ramaswamy, A.

    2017-09-01

    Imprecise probability based methods are developed in this study for the parameter estimation, in finite element model updating for concrete structures, when the measurements are imprecisely defined. Bayesian analysis using Metropolis Hastings algorithm for parameter estimation is generalized to incorporate the imprecision present in the prior distribution, in the likelihood function, and in the measured responses. Three different cases are considered (i) imprecision is present in the prior distribution and in the measurements only, (ii) imprecision is present in the parameters of the finite element model and in the measurement only, and (iii) imprecision is present in the prior distribution, in the parameters of the finite element model, and in the measurements. Procedures are also developed for integrating the imprecision in the parameters of the finite element model, in the finite element software Abaqus. The proposed methods are then verified against reinforced concrete beams and prestressed concrete beams tested in our laboratory as part of this study.

  16. Socio-economic scenario development for the assessment of climate change impacts on agricultural land use: a pairwise comparison approach

    DEFF Research Database (Denmark)

    Abildtrup, Jens; Audsley, E.; Fekete-Farkas, M.

    2006-01-01

    Assessment of the vulnerability of agriculture to climate change is strongly dependent on concurrent changes in socio-economic development pathways. This paper presents an integrated approach to the construction of socio-economic scenarios required for the analysis of climate change impacts...... on European agricultural land use. The scenarios are interpreted from the storylines described in the intergovernmental panel on climate change (IPCC) special report on emission scenarios (SRES), which ensures internal consistency between the evolution of socio-economics and climate change. A stepwise...... downscaling procedure based on expert-judgement and pairwise comparison is presented to obtain quantitative socio-economic parameters, e.g. prices and productivity estimates that are input to the ACCELERATES integrated land use model. In the first step, the global driving forces are identified and quantified...

  17. Probability of Detection (POD) as a statistical model for the validation of qualitative methods.

    Science.gov (United States)

    Wehling, Paul; LaBudde, Robert A; Brunelle, Sharon L; Nelson, Maria T

    2011-01-01

    A statistical model is presented for use in validation of qualitative methods. This model, termed Probability of Detection (POD), harmonizes the statistical concepts and parameters between quantitative and qualitative method validation. POD characterizes method response with respect to concentration as a continuous variable. The POD model provides a tool for graphical representation of response curves for qualitative methods. In addition, the model allows comparisons between candidate and reference methods, and provides calculations of repeatability, reproducibility, and laboratory effects from collaborative study data. Single laboratory study and collaborative study examples are given.

  18. Bayesian selection of misspecified models is overconfident and may cause spurious posterior probabilities for phylogenetic trees.

    Science.gov (United States)

    Yang, Ziheng; Zhu, Tianqi

    2018-02-20

    The Bayesian method is noted to produce spuriously high posterior probabilities for phylogenetic trees in analysis of large datasets, but the precise reasons for this overconfidence are unknown. In general, the performance of Bayesian selection of misspecified models is poorly understood, even though this is of great scientific interest since models are never true in real data analysis. Here we characterize the asymptotic behavior of Bayesian model selection and show that when the competing models are equally wrong, Bayesian model selection exhibits surprising and polarized behaviors in large datasets, supporting one model with full force while rejecting the others. If one model is slightly less wrong than the other, the less wrong model will eventually win when the amount of data increases, but the method may become overconfident before it becomes reliable. We suggest that this extreme behavior may be a major factor for the spuriously high posterior probabilities for evolutionary trees. The philosophical implications of our results to the application of Bayesian model selection to evaluate opposing scientific hypotheses are yet to be explored, as are the behaviors of non-Bayesian methods in similar situations.

  19. Model-assisted probability of detection of flaws in aluminum blocks using polynomial chaos expansions

    Science.gov (United States)

    Du, Xiaosong; Leifsson, Leifur; Grandin, Robert; Meeker, William; Roberts, Ronald; Song, Jiming

    2018-04-01

    Probability of detection (POD) is widely used for measuring reliability of nondestructive testing (NDT) systems. Typically, POD is determined experimentally, while it can be enhanced by utilizing physics-based computational models in combination with model-assisted POD (MAPOD) methods. With the development of advanced physics-based methods, such as ultrasonic NDT testing, the empirical information, needed for POD methods, can be reduced. However, performing accurate numerical simulations can be prohibitively time-consuming, especially as part of stochastic analysis. In this work, stochastic surrogate models for computational physics-based measurement simulations are developed for cost savings of MAPOD methods while simultaneously ensuring sufficient accuracy. The stochastic surrogate is used to propagate the random input variables through the physics-based simulation model to obtain the joint probability distribution of the output. The POD curves are then generated based on those results. Here, the stochastic surrogates are constructed using non-intrusive polynomial chaos (NIPC) expansions. In particular, the NIPC methods used are the quadrature, ordinary least-squares (OLS), and least-angle regression sparse (LARS) techniques. The proposed approach is demonstrated on the ultrasonic testing simulation of a flat bottom hole flaw in an aluminum block. The results show that the stochastic surrogates have at least two orders of magnitude faster convergence on the statistics than direct Monte Carlo sampling (MCS). Moreover, the evaluation of the stochastic surrogate models is over three orders of magnitude faster than the underlying simulation model for this case, which is the UTSim2 model.

  20. Bispectral pairwise interacting source analysis for identifying systems of cross-frequency interacting brain sources from electroencephalographic or magnetoencephalographic signals

    Science.gov (United States)

    Chella, Federico; Pizzella, Vittorio; Zappasodi, Filippo; Nolte, Guido; Marzetti, Laura

    2016-05-01

    Brain cognitive functions arise through the coordinated activity of several brain regions, which actually form complex dynamical systems operating at multiple frequencies. These systems often consist of interacting subsystems, whose characterization is of importance for a complete understanding of the brain interaction processes. To address this issue, we present a technique, namely the bispectral pairwise interacting source analysis (biPISA), for analyzing systems of cross-frequency interacting brain sources when multichannel electroencephalographic (EEG) or magnetoencephalographic (MEG) data are available. Specifically, the biPISA makes it possible to identify one or many subsystems of cross-frequency interacting sources by decomposing the antisymmetric components of the cross-bispectra between EEG or MEG signals, based on the assumption that interactions are pairwise. Thanks to the properties of the antisymmetric components of the cross-bispectra, biPISA is also robust to spurious interactions arising from mixing artifacts, i.e., volume conduction or field spread, which always affect EEG or MEG functional connectivity estimates. This method is an extension of the pairwise interacting source analysis (PISA), which was originally introduced for investigating interactions at the same frequency, to the study of cross-frequency interactions. The effectiveness of this approach is demonstrated in simulations for up to three interacting source pairs and for real MEG recordings of spontaneous brain activity. Simulations show that the performances of biPISA in estimating the phase difference between the interacting sources are affected by the increasing level of noise rather than by the number of the interacting subsystems. The analysis of real MEG data reveals an interaction between two pairs of sources of central mu and beta rhythms, localizing in the proximity of the left and right central sulci.

  1. Probability distributions in conservative energy exchange models of multiple interacting agents

    International Nuclear Information System (INIS)

    Scafetta, Nicola; West, Bruce J

    2007-01-01

    Herein we study energy exchange models of multiple interacting agents that conserve energy in each interaction. The models differ regarding the rules that regulate the energy exchange and boundary effects. We find a variety of stochastic behaviours that manifest energy equilibrium probability distributions of different types and interaction rules that yield not only the exponential distributions such as the familiar Maxwell-Boltzmann-Gibbs distribution of an elastically colliding ideal particle gas, but also uniform distributions, truncated exponential distributions, Gaussian distributions, Gamma distributions, inverse power law distributions, mixed exponential and inverse power law distributions, and evolving distributions. This wide variety of distributions should be of value in determining the underlying mechanisms generating the statistical properties of complex phenomena including those to be found in complex chemical reactions

  2. Logic, probability, and human reasoning.

    Science.gov (United States)

    Johnson-Laird, P N; Khemlani, Sangeet S; Goodwin, Geoffrey P

    2015-04-01

    This review addresses the long-standing puzzle of how logic and probability fit together in human reasoning. Many cognitive scientists argue that conventional logic cannot underlie deductions, because it never requires valid conclusions to be withdrawn - not even if they are false; it treats conditional assertions implausibly; and it yields many vapid, although valid, conclusions. A new paradigm of probability logic allows conclusions to be withdrawn and treats conditionals more plausibly, although it does not address the problem of vapidity. The theory of mental models solves all of these problems. It explains how people reason about probabilities and postulates that the machinery for reasoning is itself probabilistic. Recent investigations accordingly suggest a way to integrate probability and deduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

  3. Negative probability in the framework of combined probability

    OpenAIRE

    Burgin, Mark

    2013-01-01

    Negative probability has found diverse applications in theoretical physics. Thus, construction of sound and rigorous mathematical foundations for negative probability is important for physics. There are different axiomatizations of conventional probability. So, it is natural that negative probability also has different axiomatic frameworks. In the previous publications (Burgin, 2009; 2010), negative probability was mathematically formalized and rigorously interpreted in the context of extende...

  4. The limiting conditional probability distribution in a stochastic model of T cell repertoire maintenance.

    Science.gov (United States)

    Stirk, Emily R; Lythe, Grant; van den Berg, Hugo A; Hurst, Gareth A D; Molina-París, Carmen

    2010-04-01

    The limiting conditional probability distribution (LCD) has been much studied in the field of mathematical biology, particularly in the context of epidemiology and the persistence of epidemics. However, it has not yet been applied to the immune system. One of the characteristic features of the T cell repertoire is its diversity. This diversity declines in old age, whence the concepts of extinction and persistence are also relevant to the immune system. In this paper we model T cell repertoire maintenance by means of a continuous-time birth and death process on the positive integers, where the origin is an absorbing state. We show that eventual extinction is guaranteed. The late-time behaviour of the process before extinction takes place is modelled by the LCD, which we prove always exists for the process studied here. In most cases, analytic expressions for the LCD cannot be computed but the probability distribution may be approximated by means of the stationary probability distributions of two related processes. We show how these approximations are related to the LCD of the original process and use them to study the LCD in two special cases. We also make use of the large N expansion to derive a further approximation to the LCD. The accuracy of the various approximations is then analysed. (c) 2009 Elsevier Inc. All rights reserved.

  5. Fundamentals of applied probability and random processes

    CERN Document Server

    Ibe, Oliver

    2014-01-01

    The long-awaited revision of Fundamentals of Applied Probability and Random Processes expands on the central components that made the first edition a classic. The title is based on the premise that engineers use probability as a modeling tool, and that probability can be applied to the solution of engineering problems. Engineers and students studying probability and random processes also need to analyze data, and thus need some knowledge of statistics. This book is designed to provide students with a thorough grounding in probability and stochastic processes, demonstrate their applicability t

  6. The HYDROMED model and its application to semi-arid Mediterranean catchments with hill reservoirs 3: Reservoir storage capacity and probability of failure model

    Directory of Open Access Journals (Sweden)

    R. Ragab

    2001-01-01

    Full Text Available This paper addresses the issue of "what reservoir storage capacity is required to maintain a yield with a given probability of failure?". It is an important issue in terms of construction and cost. HYDROMED offers a solution based on the modified Gould probability matrix method. This method has the advantage of sampling all years data without reference to the sequence and is therefore particularly suitable for catchments with patchy data. In the HYDROMED model, the probability of failure is calculated on a monthly basis. The model has been applied to the El-Gouazine catchment in Tunisia using a long rainfall record from Kairouan together with the estimated Hortonian runoff, class A pan evaporation data and estimated abstraction data. Generally, the probability of failure differed from winter to summer. Generally, the probability of failure approaches zero when the reservoir capacity is 500,000 m3. The 25% probability of failure (75% success is achieved with a reservoir capacity of 58,000 m3 in June and 95,000 m3 in January. The probability of failure for a 240,000 m3 capacity reservoir (closer to storage capacity of El-Gouazine 233,000 m3, is approximately 5% in November, December and January, 3% in March, and 1.1% in May and June. Consequently there is no high risk of El-Gouazine being unable to meet its requirements at a capacity of 233,000 m3. Subsequently the benefit, in terms of probability of failure, by increasing the reservoir volume of El-Gouazine to greater than the 250,000 m3 is not high. This is important for the design engineers and the funding organizations. However, the analysis is based on the existing water abstraction policy, absence of siltation rate data and on the assumption that the present climate will prevail during the lifetime of the reservoir. Should these conditions change, a new analysis should be carried out. Keywords: HYDROMED, reservoir, storage capacity, probability of failure, Mediterranean

  7. Probabilities in quantum cosmological models: A decoherent histories analysis using a complex potential

    International Nuclear Information System (INIS)

    Halliwell, J. J.

    2009-01-01

    In the quantization of simple cosmological models (minisuperspace models) described by the Wheeler-DeWitt equation, an important step is the construction, from the wave function, of a probability distribution answering various questions of physical interest, such as the probability of the system entering a given region of configuration space at any stage in its entire history. A standard but heuristic procedure is to use the flux of (components of) the wave function in a WKB approximation. This gives sensible semiclassical results but lacks an underlying operator formalism. In this paper, we address the issue of constructing probability distributions linked to the Wheeler-DeWitt equation using the decoherent histories approach to quantum theory. The key step is the construction of class operators characterizing questions of physical interest. Taking advantage of a recent decoherent histories analysis of the arrival time problem in nonrelativistic quantum mechanics, we show that the appropriate class operators in quantum cosmology are readily constructed using a complex potential. The class operator for not entering a region of configuration space is given by the S matrix for scattering off a complex potential localized in that region. We thus derive the class operators for entering one or more regions in configuration space. The class operators commute with the Hamiltonian, have a sensible classical limit, and are closely related to an intersection number operator. The definitions of class operators given here handle the key case in which the underlying classical system has multiple crossings of the boundaries of the regions of interest. We show that oscillatory WKB solutions to the Wheeler-DeWitt equation give approximate decoherence of histories, as do superpositions of WKB solutions, as long as the regions of configuration space are sufficiently large. The corresponding probabilities coincide, in a semiclassical approximation, with standard heuristic procedures

  8. Characteristic length of the knotting probability revisited

    International Nuclear Information System (INIS)

    Uehara, Erica; Deguchi, Tetsuo

    2015-01-01

    We present a self-avoiding polygon (SAP) model for circular DNA in which the radius of impermeable cylindrical segments corresponds to the screening length of double-stranded DNA surrounded by counter ions. For the model we evaluate the probability for a generated SAP with N segments having a given knot K through simulation. We call it the knotting probability of a knot K with N segments for the SAP model. We show that when N is large the most significant factor in the knotting probability is given by the exponentially decaying part exp(−N/N K ), where the estimates of parameter N K are consistent with the same value for all the different knots we investigated. We thus call it the characteristic length of the knotting probability. We give formulae expressing the characteristic length as a function of the cylindrical radius r ex , i.e. the screening length of double-stranded DNA. (paper)

  9. Precipitation intensity probability distribution modelling for hydrological and construction design purposes

    International Nuclear Information System (INIS)

    Koshinchanov, Georgy; Dimitrov, Dobri

    2008-01-01

    The characteristics of rainfall intensity are important for many purposes, including design of sewage and drainage systems, tuning flood warning procedures, etc. Those estimates are usually statistical estimates of the intensity of precipitation realized for certain period of time (e.g. 5, 10 min., etc) with different return period (e.g. 20, 100 years, etc). The traditional approach in evaluating the mentioned precipitation intensities is to process the pluviometer's records and fit probability distribution to samples of intensities valid for certain locations ore regions. Those estimates further become part of the state regulations to be used for various economic activities. Two problems occur using the mentioned approach: 1. Due to various factors the climate conditions are changed and the precipitation intensity estimates need regular update; 2. As far as the extremes of the probability distribution are of particular importance for the practice, the methodology of the distribution fitting needs specific attention to those parts of the distribution. The aim of this paper is to make review of the existing methodologies for processing the intensive rainfalls and to refresh some of the statistical estimates for the studied areas. The methodologies used in Bulgaria for analyzing the intensive rainfalls and produce relevant statistical estimates: - The method of the maximum intensity, used in the National Institute of Meteorology and Hydrology to process and decode the pluviometer's records, followed by distribution fitting for each precipitation duration period; - As the above, but with separate modeling of probability distribution for the middle and high probability quantiles. - Method is similar to the first one, but with a threshold of 0,36 mm/min of intensity; - Another method proposed by the Russian hydrologist G. A. Aleksiev for regionalization of estimates over some territory, improved and adapted by S. Gerasimov for Bulgaria; - Next method is considering only

  10. Probability density function modeling of scalar mixing from concentrated sources in turbulent channel flow

    OpenAIRE

    Bakosi, J.; Franzese, P.; Boybeyi, Z.

    2010-01-01

    Dispersion of a passive scalar from concentrated sources in fully developed turbulent channel flow is studied with the probability density function (PDF) method. The joint PDF of velocity, turbulent frequency and scalar concentration is represented by a large number of Lagrangian particles. A stochastic near-wall PDF model combines the generalized Langevin model of Haworth & Pope with Durbin's method of elliptic relaxation to provide a mathematically exact treatment of convective and viscous ...

  11. How to model a negligible probability under the WTO sanitary and phytosanitary agreement?

    Science.gov (United States)

    Powell, Mark R

    2013-06-01

    Since the 1997 EC--Hormones decision, World Trade Organization (WTO) Dispute Settlement Panels have wrestled with the question of what constitutes a negligible risk under the Sanitary and Phytosanitary Agreement. More recently, the 2010 WTO Australia--Apples Panel focused considerable attention on the appropriate quantitative model for a negligible probability in a risk assessment. The 2006 Australian Import Risk Analysis for Apples from New Zealand translated narrative probability statements into quantitative ranges. The uncertainty about a "negligible" probability was characterized as a uniform distribution with a minimum value of zero and a maximum value of 10(-6) . The Australia - Apples Panel found that the use of this distribution would tend to overestimate the likelihood of "negligible" events and indicated that a triangular distribution with a most probable value of zero and a maximum value of 10⁻⁶ would correct the bias. The Panel observed that the midpoint of the uniform distribution is 5 × 10⁻⁷ but did not consider that the triangular distribution has an expected value of 3.3 × 10⁻⁷. Therefore, if this triangular distribution is the appropriate correction, the magnitude of the bias found by the Panel appears modest. The Panel's detailed critique of the Australian risk assessment, and the conclusions of the WTO Appellate Body about the materiality of flaws found by the Panel, may have important implications for the standard of review for risk assessments under the WTO SPS Agreement. © 2012 Society for Risk Analysis.

  12. Factors influencing reporting and harvest probabilities in North American geese

    Science.gov (United States)

    Zimmerman, G.S.; Moser, T.J.; Kendall, W.L.; Doherty, P.F.; White, Gary C.; Caswell, D.F.

    2009-01-01

    We assessed variation in reporting probabilities of standard bands among species, populations, harvest locations, and size classes of North American geese to enable estimation of unbiased harvest probabilities. We included reward (US10,20,30,50, or100) and control (0) banded geese from 16 recognized goose populations of 4 species: Canada (Branta canadensis), cackling (B. hutchinsii), Ross's (Chen rossii), and snow geese (C. caerulescens). We incorporated spatially explicit direct recoveries and live recaptures into a multinomial model to estimate reporting, harvest, and band-retention probabilities. We compared various models for estimating harvest probabilities at country (United States vs. Canada), flyway (5 administrative regions), and harvest area (i.e., flyways divided into northern and southern sections) scales. Mean reporting probability of standard bands was 0.73 (95 CI 0.690.77). Point estimates of reporting probabilities for goose populations or spatial units varied from 0.52 to 0.93, but confidence intervals for individual estimates overlapped and model selection indicated that models with species, population, or spatial effects were less parsimonious than those without these effects. Our estimates were similar to recently reported estimates for mallards (Anas platyrhynchos). We provide current harvest probability estimates for these populations using our direct measures of reporting probability, improving the accuracy of previous estimates obtained from recovery probabilities alone. Goose managers and researchers throughout North America can use our reporting probabilities to correct recovery probabilities estimated from standard banding operations for deriving spatially explicit harvest probabilities.

  13. Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

    Science.gov (United States)

    Haberlandt, U.; Radtke, I.

    2014-01-01

    Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the

  14. The creation and evaluation of a model predicting the probability of conception in seasonal-calving, pasture-based dairy cows.

    Science.gov (United States)

    Fenlon, Caroline; O'Grady, Luke; Doherty, Michael L; Dunnion, John; Shalloo, Laurence; Butler, Stephen T

    2017-07-01

    Reproductive performance in pasture-based production systems has a fundamentally important effect on economic efficiency. The individual factors affecting the probability of submission and conception are multifaceted and have been extensively researched. The present study analyzed some of these factors in relation to service-level probability of conception in seasonal-calving pasture-based dairy cows to develop a predictive model of conception. Data relating to 2,966 services from 737 cows on 2 research farms were used for model development and data from 9 commercial dairy farms were used for model testing, comprising 4,212 services from 1,471 cows. The data spanned a 15-yr period and originated from seasonal-calving pasture-based dairy herds in Ireland. The calving season for the study herds extended from January to June, with peak calving in February and March. A base mixed-effects logistic regression model was created using a stepwise model-building strategy and incorporated parity, days in milk, interservice interval, calving difficulty, and predicted transmitting abilities for calving interval and milk production traits. To attempt to further improve the predictive capability of the model, the addition of effects that were not statistically significant was considered, resulting in a final model composed of the base model with the inclusion of BCS at service. The models' predictions were evaluated using discrimination to measure their ability to correctly classify positive and negative cases. Precision, recall, F-score, and area under the receiver operating characteristic curve (AUC) were calculated. Calibration tests measured the accuracy of the predicted probabilities. These included tests of overall goodness-of-fit, bias, and calibration error. Both models performed better than using the population average probability of conception. Neither of the models showed high levels of discrimination (base model AUC 0.61, final model AUC 0.62), possibly because of the

  15. Mathematical models of tumor growth: translating absorbed dose to tumor control probability

    International Nuclear Information System (INIS)

    Sgouros, G.

    1996-01-01

    cell loss due to irradiation, the log-kill model, therefore, predicts that incomplete treatment of a kinetically heterogeneous tumor will yield a more proliferative tumor. The probability of tumor control in such a simulation may be obtained from the nadir in tumor cell number. If the nadir is not sufficiently low to yield a high probability of tumor control, then the tumor will re-grow. Since tumors in each sub-population are assumed lost at the same rate, cells comprising the sub-population with the shortest potential doubling time will re-grow the fastest, yielding a recurrent tumor that is more proliferative. A number of assumptions and simplifications are both implicitly and explicitly made in converting absorbed dose to tumor control probability. The modeling analyses described above must, therefore, be viewed in terms of understanding and evaluating different treatment approaches with the goal of treatment optimization rather than outcome prediction

  16. Probability Weighting and Loss Aversion in Futures Hedging

    NARCIS (Netherlands)

    Mattos, F.; Garcia, P.; Pennings, J.M.E.

    2008-01-01

    We analyze how the introduction of probability weighting and loss aversion in a futures hedging model affects decision making. Analytical findings indicate that probability weighting alone always affects optimal hedge ratios, while loss and risk aversion only have an impact when probability

  17. Probability for Weather and Climate

    Science.gov (United States)

    Smith, L. A.

    2013-12-01

    Over the last 60 years, the availability of large-scale electronic computers has stimulated rapid and significant advances both in meteorology and in our understanding of the Earth System as a whole. The speed of these advances was due, in large part, to the sudden ability to explore nonlinear systems of equations. The computer allows the meteorologist to carry a physical argument to its conclusion; the time scales of weather phenomena then allow the refinement of physical theory, numerical approximation or both in light of new observations. Prior to this extension, as Charney noted, the practicing meteorologist could ignore the results of theory with good conscience. Today, neither the practicing meteorologist nor the practicing climatologist can do so, but to what extent, and in what contexts, should they place the insights of theory above quantitative simulation? And in what circumstances can one confidently estimate the probability of events in the world from model-based simulations? Despite solid advances of theory and insight made possible by the computer, the fidelity of our models of climate differs in kind from the fidelity of models of weather. While all prediction is extrapolation in time, weather resembles interpolation in state space, while climate change is fundamentally an extrapolation. The trichotomy of simulation, observation and theory which has proven essential in meteorology will remain incomplete in climate science. Operationally, the roles of probability, indeed the kinds of probability one has access too, are different in operational weather forecasting and climate services. Significant barriers to forming probability forecasts (which can be used rationally as probabilities) are identified. Monte Carlo ensembles can explore sensitivity, diversity, and (sometimes) the likely impact of measurement uncertainty and structural model error. The aims of different ensemble strategies, and fundamental differences in ensemble design to support of

  18. Entropy-optimal weight constraint elicitation with additive multi-attribute utility models

    NARCIS (Netherlands)

    Valkenhoef , van Gert; Tervonen, Tommi

    2016-01-01

    We consider the elicitation of incomplete preference information for the additive utility model in terms of linear constraints on the weights. Eliciting incomplete preferences using holistic pair-wise judgments is convenient for the decision maker, but selecting the best pair-wise comparison is

  19. An efficient genetic algorithm for structural RNA pairwise alignment and its application to non-coding RNA discovery in yeast

    Directory of Open Access Journals (Sweden)

    Taneda Akito

    2008-12-01

    Full Text Available Abstract Background Aligning RNA sequences with low sequence identity has been a challenging problem since such a computation essentially needs an algorithm with high complexities for taking structural conservation into account. Although many sophisticated algorithms for the purpose have been proposed to date, further improvement in efficiency is necessary to accelerate its large-scale applications including non-coding RNA (ncRNA discovery. Results We developed a new genetic algorithm, Cofolga2, for simultaneously computing pairwise RNA sequence alignment and consensus folding, and benchmarked it using BRAliBase 2.1. The benchmark results showed that our new algorithm is accurate and efficient in both time and memory usage. Then, combining with the originally trained SVM, we applied the new algorithm to novel ncRNA discovery where we compared S. cerevisiae genome with six related genomes in a pairwise manner. By focusing our search to the relatively short regions (50 bp to 2,000 bp sandwiched by conserved sequences, we successfully predict 714 intergenic and 1,311 sense or antisense ncRNA candidates, which were found in the pairwise alignments with stable consensus secondary structure and low sequence identity (≤ 50%. By comparing with the previous predictions, we found that > 92% of the candidates is novel candidates. The estimated rate of false positives in the predicted candidates is 51%. Twenty-five percent of the intergenic candidates has supports for expression in cell, i.e. their genomic positions overlap those of the experimentally determined transcripts in literature. By manual inspection of the results, moreover, we obtained four multiple alignments with low sequence identity which reveal consensus structures shared by three species/sequences. Conclusion The present method gives an efficient tool complementary to sequence-alignment-based ncRNA finders.

  20. Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment scores

    Directory of Open Access Journals (Sweden)

    Maréchal Eric

    2008-08-01

    Full Text Available Abstract Background Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic data. Two statistical models have been proposed. In the asymptotic limit of long sequences, the Karlin-Altschul model is based on the computation of a P-value, assuming that the number of high scoring matching regions above a threshold is Poisson distributed. Alternatively, the Lipman-Pearson model is based on the computation of a Z-value from a random score distribution obtained by a Monte-Carlo simulation. Z-values allow the deduction of an upper bound of the P-value (1/Z-value2 following the TULIP theorem. Simulations of Z-value distribution is known to fit with a Gumbel law. This remarkable property was not demonstrated and had no obvious biological support. Results We built a model of evolution of sequences based on aging, as meant in Reliability Theory, using the fact that the amount of information shared between an initial sequence and the sequences in its lineage (i.e., mutual information in Information Theory is a decreasing function of time. This quantity is simply measured by a sequence alignment score. In systems aging, the failure rate is related to the systems longevity. The system can be a machine with structured components, or a living entity or population. "Reliability" refers to the ability to operate properly according to a standard. Here, the "reliability" of a sequence refers to the ability to conserve a sufficient functional level at the folded and maturated protein level (positive selection pressure. Homologous sequences were considered as systems 1 having a high redundancy of information reflected by the magnitude of their alignment scores, 2 which components are the amino acids that can independently be damaged by random DNA mutations. From these assumptions, we deduced that information shared at each amino acid position evolved with a

  1. Quantum probability and cognitive modeling: some cautions and a promising direction in modeling physics learning.

    Science.gov (United States)

    Franceschetti, Donald R; Gire, Elizabeth

    2013-06-01

    Quantum probability theory offers a viable alternative to classical probability, although there are some ambiguities inherent in transferring the quantum formalism to a less determined realm. A number of physicists are now looking at the applicability of quantum ideas to the assessment of physics learning, an area particularly suited to quantum probability ideas.

  2. Probability of bystander effect induced by alpha-particles emitted by radon progeny using the analytical model of tracheobronchial tree

    International Nuclear Information System (INIS)

    Jovanovic, B.; Nikezic, D.

    2010-01-01

    Radiation-induced biological bystander effects have become a phenomenon associated with the interaction of radiation with cells. There is a need to include the influence of biological effects in the dosimetry of the human lung. With this aim, the purpose of this work is to calculate the probability of bystander effect induced by alpha-particle radiation on sensitive cells of the human lung. Probability was calculated by applying the analytical model cylinder bifurcation, which was created to simulate the geometry of the human lung with the geometric distribution of cell nuclei in the airway wall of the tracheobronchial tree. This analytical model of the human tracheobronchial tree represents the extension of the ICRP 66 model, and follows it as much as possible. Reported probabilities are calculated for various targets and alpha-particle energies. Probability of bystander effect has been calculated for alpha particles with 6 and 7.69 MeV energies, which are emitted in the 222 Rn chain. The application of these results may enhance current dose risk estimation approaches in the sense of the inclusion of the influence of the biological effects. (authors)

  3. Concepts of probability theory

    CERN Document Server

    Pfeiffer, Paul E

    1979-01-01

    Using the Kolmogorov model, this intermediate-level text discusses random variables, probability distributions, mathematical expectation, random processes, more. For advanced undergraduates students of science, engineering, or math. Includes problems with answers and six appendixes. 1965 edition.

  4. Pairwise and higher-order correlations among drug-resistance mutations in HIV-1 subtype B protease

    Directory of Open Access Journals (Sweden)

    Morozov Alexandre V

    2009-08-01

    individually smaller but may have a collective effect. Together they lead to correlations which could have an important impact on the dynamics of the evolution of cross-resistance, by allowing the virus to pass through otherwise unlikely mutational states. These findings also indicate that pairwise and possibly higher-order effects should be included in the models of protein evolution, instead of assuming that all residues mutate independently of one another.

  5. Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials

    Directory of Open Access Journals (Sweden)

    Nils Ternès

    2017-05-01

    Full Text Available Abstract Background Thanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions. Methods Based on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso, we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation; estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap; and visualize them graphically (pointwise or smoothed with spline. We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured. Results In our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4

  6. A pairwise residue contact area-based mean force potential for discrimination of native protein structure

    Directory of Open Access Journals (Sweden)

    Pezeshk Hamid

    2010-01-01

    Full Text Available Abstract Background Considering energy function to detect a correct protein fold from incorrect ones is very important for protein structure prediction and protein folding. Knowledge-based mean force potentials are certainly the most popular type of interaction function for protein threading. They are derived from statistical analyses of interacting groups in experimentally determined protein structures. These potentials are developed at the atom or the amino acid level. Based on orientation dependent contact area, a new type of knowledge-based mean force potential has been developed. Results We developed a new approach to calculate a knowledge-based potential of mean-force, using pairwise residue contact area. To test the performance of our approach, we performed it on several decoy sets to measure its ability to discriminate native structure from decoys. This potential has been able to distinguish native structures from the decoys in the most cases. Further, the calculated Z-scores were quite high for all protein datasets. Conclusions This knowledge-based potential of mean force can be used in protein structure prediction, fold recognition, comparative modelling and molecular recognition. The program is available at http://www.bioinf.cs.ipm.ac.ir/softwares/surfield

  7. Swedish earthquakes and acceleration probabilities

    International Nuclear Information System (INIS)

    Slunga, R.

    1979-03-01

    A method to assign probabilities to ground accelerations for Swedish sites is described. As hardly any nearfield instrumental data is available we are left with the problem of interpreting macroseismic data in terms of acceleration. By theoretical wave propagation computations the relation between seismic strength of the earthquake, focal depth, distance and ground accelerations are calculated. We found that most Swedish earthquake of the area, the 1904 earthquake 100 km south of Oslo, is an exception and probably had a focal depth exceeding 25 km. For the nuclear power plant sites an annual probability of 10 -5 has been proposed as interesting. This probability gives ground accelerations in the range 5-20 % for the sites. This acceleration is for a free bedrock site. For consistency all acceleration results in this study are given for bedrock sites. When applicating our model to the 1904 earthquake and assuming the focal zone to be in the lower crust we get the epicentral acceleration of this earthquake to be 5-15 % g. The results above are based on an analyses of macrosismic data as relevant instrumental data is lacking. However, the macroseismic acceleration model deduced in this study gives epicentral ground acceleration of small Swedish earthquakes in agreement with existent distant instrumental data. (author)

  8. A statistical model for deriving probability distributions of contamination for accidental releases

    International Nuclear Information System (INIS)

    ApSimon, H.M.; Davison, A.C.

    1986-01-01

    Results generated from a detailed long-range transport model, MESOS, simulating dispersal of a large number of hypothetical releases of radionuclides in a variety of meteorological situations over Western Europe have been used to derive a simpler statistical model, MESOSTAT. This model may be used to generate probability distributions of different levels of contamination at a receptor point 100-1000 km or so from the source (for example, across a frontier in another country) without considering individual release and dispersal scenarios. The model is embodied in a series of equations involving parameters which are determined from such factors as distance between source and receptor, nuclide decay and deposition characteristics, release duration, and geostrophic windrose at the source. Suitable geostrophic windrose data have been derived for source locations covering Western Europe. Special attention has been paid to the relatively improbable extreme values of contamination at the top end of the distribution. The MESOSTAT model and its development are described, with illustrations of its use and comparison with the original more detailed modelling techniques. (author)

  9. Current recommendations on the estimation of transition probabilities in Markov cohort models for use in health care decision-making: a targeted literature review

    Directory of Open Access Journals (Sweden)

    Olariu E

    2017-09-01

    Full Text Available Elena Olariu,1 Kevin K Cadwell,1 Elizabeth Hancock,1 David Trueman,1 Helene Chevrou-Severac2 1PHMR Ltd, London, UK; 2Takeda Pharmaceuticals International AG, Zurich, Switzerland Objective: Although Markov cohort models represent one of the most common forms of decision-analytic models used in health care decision-making, correct implementation of such models requires reliable estimation of transition probabilities. This study sought to identify consensus statements or guidelines that detail how such transition probability matrices should be estimated. Methods: A literature review was performed to identify relevant publications in the following databases: Medline, Embase, the Cochrane Library, and PubMed. Electronic searches were supplemented by manual-searches of health technology assessment (HTA websites in Australia, Belgium, Canada, France, Germany, Ireland, Norway, Portugal, Sweden, and the UK. One reviewer assessed studies for eligibility. Results: Of the 1,931 citations identified in the electronic searches, no studies met the inclusion criteria for full-text review, and no guidelines on transition probabilities in Markov models were identified. Manual-searching of the websites of HTA agencies identified ten guidelines on economic evaluations (Australia, Belgium, Canada, France, Germany, Ireland, Norway, Portugal, Sweden, and UK. All identified guidelines provided general guidance on how to develop economic models, but none provided guidance on the calculation of transition probabilities. One relevant publication was identified following review of the reference lists of HTA agency guidelines: the International Society for Pharmacoeconomics and Outcomes Research taskforce guidance. This provided limited guidance on the use of rates and probabilities. Conclusions: There is limited formal guidance available on the estimation of transition probabilities for use in decision-analytic models. Given the increasing importance of cost

  10. A new formulation of the probability density function in random walk models for atmospheric dispersion

    DEFF Research Database (Denmark)

    Falk, Anne Katrine Vinther; Gryning, Sven-Erik

    1997-01-01

    In this model for atmospheric dispersion particles are simulated by the Langevin Equation, which is a stochastic differential equation. It uses the probability density function (PDF) of the vertical velocity fluctuations as input. The PDF is constructed as an expansion after Hermite polynomials...

  11. A Quantum Theoretical Explanation for Probability Judgment Errors

    Science.gov (United States)

    Busemeyer, Jerome R.; Pothos, Emmanuel M.; Franco, Riccardo; Trueblood, Jennifer S.

    2011-01-01

    A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction and disjunction fallacies, averaging effects, unpacking effects, and order effects on inference. On the one hand, quantum theory is similar to other categorization and memory models of cognition in that it relies on vector…

  12. Impact of Age on the Risk of Advanced Colorectal Neoplasia in a Young Population: An Analysis Using the Predicted Probability Model.

    Science.gov (United States)

    Jung, Yoon Suk; Park, Chan Hyuk; Kim, Nam Hee; Lee, Mi Yeon; Park, Dong Il

    2017-09-01

    The incidence of colorectal cancer is decreasing in adults aged ≥50 years and increasing in those aged probability models for ACRN in a population aged 30-49 years. Of 96,235 participants, 57,635 and 38,600 were included in the derivation and validation cohorts, respectively. The predicted probability model considered age, sex, body mass index, family history of colorectal cancer, and smoking habits, as follows: Y ACRN  = -8.755 + 0.080·X age  - 0.055·X male  + 0.041·X BMI  + 0.200·X family_history_of_CRC  + 0.218·X former_smoker  + 0.644·X current_smoker . The optimal cutoff value for the predicted probability of ACRN by Youden index was 1.14%. The area under the receiver-operating characteristic curve (AUROC) values of our model for ACRN were higher than those of the previously established Asia-Pacific Colorectal Screening (APCS), Korean Colorectal Screening (KCS), and Kaminski's scoring models [AUROC (95% confidence interval): model in the current study, 0.673 (0.648-0.697); vs. APCS, 0.588 (0.564-0.611), P probability model can assess the risk of ACRN more accurately than existing models, including the APCS, KCS, and Kaminski's scoring models.

  13. Linear positivity and virtual probability

    International Nuclear Information System (INIS)

    Hartle, James B.

    2004-01-01

    We investigate the quantum theory of closed systems based on the linear positivity decoherence condition of Goldstein and Page. The objective of any quantum theory of a closed system, most generally the universe, is the prediction of probabilities for the individual members of sets of alternative coarse-grained histories of the system. Quantum interference between members of a set of alternative histories is an obstacle to assigning probabilities that are consistent with the rules of probability theory. A quantum theory of closed systems therefore requires two elements: (1) a condition specifying which sets of histories may be assigned probabilities and (2) a rule for those probabilities. The linear positivity condition of Goldstein and Page is the weakest of the general conditions proposed so far. Its general properties relating to exact probability sum rules, time neutrality, and conservation laws are explored. Its inconsistency with the usual notion of independent subsystems in quantum mechanics is reviewed. Its relation to the stronger condition of medium decoherence necessary for classicality is discussed. The linear positivity of histories in a number of simple model systems is investigated with the aim of exhibiting linearly positive sets of histories that are not decoherent. The utility of extending the notion of probability to include values outside the range of 0-1 is described. Alternatives with such virtual probabilities cannot be measured or recorded, but can be used in the intermediate steps of calculations of real probabilities. Extended probabilities give a simple and general way of formulating quantum theory. The various decoherence conditions are compared in terms of their utility for characterizing classicality and the role they might play in further generalizations of quantum mechanics

  14. Application of a weighted spatial probability model in GIS to analyse landslides in Penang Island, Malaysia

    Directory of Open Access Journals (Sweden)

    Samy Ismail Elmahdy

    2016-01-01

    Full Text Available In the current study, Penang Island, which is one of the several mountainous areas in Malaysia that is often subjected to landslide hazard, was chosen for further investigation. A multi-criteria Evaluation and the spatial probability weighted approach and model builder was applied to map and analyse landslides in Penang Island. A set of automated algorithms was used to construct new essential geological and morphometric thematic maps from remote sensing data. The maps were ranked using the weighted probability spatial model based on their contribution to the landslide hazard. Results obtained showed that sites at an elevation of 100–300 m, with steep slopes of 10°–37° and slope direction (aspect in the E and SE directions were areas of very high and high probability for the landslide occurrence; the total areas were 21.393 km2 (11.84% and 58.690 km2 (32.48%, respectively. The obtained map was verified by comparing variogram models of the mapped and the occurred landslide locations and showed a strong correlation with the locations of occurred landslides, indicating that the proposed method can successfully predict the unpredictable landslide hazard. The method is time and cost effective and can be used as a reference for geological and geotechnical engineers.

  15. Joint probabilities and quantum cognition

    International Nuclear Information System (INIS)

    Acacio de Barros, J.

    2012-01-01

    In this paper we discuss the existence of joint probability distributions for quantumlike response computations in the brain. We do so by focusing on a contextual neural-oscillator model shown to reproduce the main features of behavioral stimulus-response theory. We then exhibit a simple example of contextual random variables not having a joint probability distribution, and describe how such variables can be obtained from neural oscillators, but not from a quantum observable algebra.

  16. Joint probabilities and quantum cognition

    Energy Technology Data Exchange (ETDEWEB)

    Acacio de Barros, J. [Liberal Studies, 1600 Holloway Ave., San Francisco State University, San Francisco, CA 94132 (United States)

    2012-12-18

    In this paper we discuss the existence of joint probability distributions for quantumlike response computations in the brain. We do so by focusing on a contextual neural-oscillator model shown to reproduce the main features of behavioral stimulus-response theory. We then exhibit a simple example of contextual random variables not having a joint probability distribution, and describe how such variables can be obtained from neural oscillators, but not from a quantum observable algebra.

  17. Probability

    CERN Document Server

    Shiryaev, A N

    1996-01-01

    This book contains a systematic treatment of probability from the ground up, starting with intuitive ideas and gradually developing more sophisticated subjects, such as random walks, martingales, Markov chains, ergodic theory, weak convergence of probability measures, stationary stochastic processes, and the Kalman-Bucy filter Many examples are discussed in detail, and there are a large number of exercises The book is accessible to advanced undergraduates and can be used as a text for self-study This new edition contains substantial revisions and updated references The reader will find a deeper study of topics such as the distance between probability measures, metrization of weak convergence, and contiguity of probability measures Proofs for a number of some important results which were merely stated in the first edition have been added The author included new material on the probability of large deviations, and on the central limit theorem for sums of dependent random variables

  18. Development of a methodology for probable maximum precipitation estimation over the American River watershed using the WRF model

    Science.gov (United States)

    Tan, Elcin

    A new physically-based methodology for probable maximum precipitation (PMP) estimation is developed over the American River Watershed (ARW) using the Weather Research and Forecast (WRF-ARW) model. A persistent moisture flux convergence pattern, called Pineapple Express, is analyzed for 42 historical extreme precipitation events, and it is found that Pineapple Express causes extreme precipitation over the basin of interest. An average correlation between moisture flux convergence and maximum precipitation is estimated as 0.71 for 42 events. The performance of the WRF model is verified for precipitation by means of calibration and independent validation of the model. The calibration procedure is performed only for the first ranked flood event 1997 case, whereas the WRF model is validated for 42 historical cases. Three nested model domains are set up with horizontal resolutions of 27 km, 9 km, and 3 km over the basin of interest. As a result of Chi-square goodness-of-fit tests, the hypothesis that "the WRF model can be used in the determination of PMP over the ARW for both areal average and point estimates" is accepted at the 5% level of significance. The sensitivities of model physics options on precipitation are determined using 28 microphysics, atmospheric boundary layer, and cumulus parameterization schemes combinations. It is concluded that the best triplet option is Thompson microphysics, Grell 3D ensemble cumulus, and YSU boundary layer (TGY), based on 42 historical cases, and this TGY triplet is used for all analyses of this research. Four techniques are proposed to evaluate physically possible maximum precipitation using the WRF: 1. Perturbations of atmospheric conditions; 2. Shift in atmospheric conditions; 3. Replacement of atmospheric conditions among historical events; and 4. Thermodynamically possible worst-case scenario creation. Moreover, climate change effect on precipitation is discussed by emphasizing temperature increase in order to determine the

  19. Improved bounds on the epidemic threshold of exact SIS models on complex networks

    KAUST Repository

    Ruhi, Navid Azizan; Thrampoulidis, Christos; Hassibi, Babak

    2017-01-01

    The SIS (susceptible-infected-susceptible) epidemic model on an arbitrary network, without making approximations, is a 2n-state Markov chain with a unique absorbing state (the all-healthy state). This makes analysis of the SIS model and, in particular, determining the threshold of epidemic spread quite challenging. It has been shown that the exact marginal probabilities of infection can be upper bounded by an n-dimensional linear time-invariant system, a consequence of which is that the Markov chain is “fast-mixing” when the LTI system is stable, i.e. when equation (where β is the infection rate per link, δ is the recovery rate, and λmax(A) is the largest eigenvalue of the network's adjacency matrix). This well-known threshold has been recently shown not to be tight in several cases, such as in a star network. In this paper, we provide tighter upper bounds on the exact marginal probabilities of infection, by also taking pairwise infection probabilities into account. Based on this improved bound, we derive tighter eigenvalue conditions that guarantee fast mixing (i.e., logarithmic mixing time) of the chain. We demonstrate the improvement of the threshold condition by comparing the new bound with the known one on various networks with various epidemic parameters.

  20. Improved bounds on the epidemic threshold of exact SIS models on complex networks

    KAUST Repository

    Ruhi, Navid Azizan

    2017-01-05

    The SIS (susceptible-infected-susceptible) epidemic model on an arbitrary network, without making approximations, is a 2n-state Markov chain with a unique absorbing state (the all-healthy state). This makes analysis of the SIS model and, in particular, determining the threshold of epidemic spread quite challenging. It has been shown that the exact marginal probabilities of infection can be upper bounded by an n-dimensional linear time-invariant system, a consequence of which is that the Markov chain is “fast-mixing” when the LTI system is stable, i.e. when equation (where β is the infection rate per link, δ is the recovery rate, and λmax(A) is the largest eigenvalue of the network\\'s adjacency matrix). This well-known threshold has been recently shown not to be tight in several cases, such as in a star network. In this paper, we provide tighter upper bounds on the exact marginal probabilities of infection, by also taking pairwise infection probabilities into account. Based on this improved bound, we derive tighter eigenvalue conditions that guarantee fast mixing (i.e., logarithmic mixing time) of the chain. We demonstrate the improvement of the threshold condition by comparing the new bound with the known one on various networks with various epidemic parameters.

  1. Modeling tumor control probability for spatially inhomogeneous risk of failure based on clinical outcome data

    DEFF Research Database (Denmark)

    Lühr, Armin; Löck, Steffen; Jakobi, Annika

    2017-01-01

    PURPOSE: Objectives of this work are (1) to derive a general clinically relevant approach to model tumor control probability (TCP) for spatially variable risk of failure and (2) to demonstrate its applicability by estimating TCP for patients planned for photon and proton irradiation. METHODS AND ...

  2. Hypothyroidism after primary radiotherapy for head and neck squamous cell carcinoma: Normal tissue complication probability modeling with latent time correction

    DEFF Research Database (Denmark)

    Rønjom, Marianne Feen; Brink, Carsten; Bentzen, Søren

    2013-01-01

    To develop a normal tissue complication probability (NTCP) model of radiation-induced biochemical hypothyroidism (HT) after primary radiotherapy for head and neck squamous cell carcinoma (HNSCC) with adjustment for latency and clinical risk factors.......To develop a normal tissue complication probability (NTCP) model of radiation-induced biochemical hypothyroidism (HT) after primary radiotherapy for head and neck squamous cell carcinoma (HNSCC) with adjustment for latency and clinical risk factors....

  3. PHOTOMETRIC REDSHIFTS AND QUASAR PROBABILITIES FROM A SINGLE, DATA-DRIVEN GENERATIVE MODEL

    International Nuclear Information System (INIS)

    Bovy, Jo; Hogg, David W.; Weaver, Benjamin A.; Myers, Adam D.; Hennawi, Joseph F.; McMahon, Richard G.; Schiminovich, David; Sheldon, Erin S.; Brinkmann, Jon; Schneider, Donald P.

    2012-01-01

    We describe a technique for simultaneously classifying and estimating the redshift of quasars. It can separate quasars from stars in arbitrary redshift ranges, estimate full posterior distribution functions for the redshift, and naturally incorporate flux uncertainties, missing data, and multi-wavelength photometry. We build models of quasars in flux-redshift space by applying the extreme deconvolution technique to estimate the underlying density. By integrating this density over redshift, one can obtain quasar flux densities in different redshift ranges. This approach allows for efficient, consistent, and fast classification and photometric redshift estimation. This is achieved by combining the speed obtained by choosing simple analytical forms as the basis of our density model with the flexibility of non-parametric models through the use of many simple components with many parameters. We show that this technique is competitive with the best photometric quasar classification techniques—which are limited to fixed, broad redshift ranges and high signal-to-noise ratio data—and with the best photometric redshift techniques when applied to broadband optical data. We demonstrate that the inclusion of UV and NIR data significantly improves photometric quasar-star separation and essentially resolves all of the redshift degeneracies for quasars inherent to the ugriz filter system, even when included data have a low signal-to-noise ratio. For quasars spectroscopically confirmed by the SDSS 84% and 97% of the objects with Galaxy Evolution Explorer UV and UKIDSS NIR data have photometric redshifts within 0.1 and 0.3, respectively, of the spectroscopic redshift; this amounts to about a factor of three improvement over ugriz-only photometric redshifts. Our code to calculate quasar probabilities and redshift probability distributions is publicly available.

  4. A general transformation to canonical form for potentials in pairwise interatomic interactions.

    Science.gov (United States)

    Walton, Jay R; Rivera-Rivera, Luis A; Lucchese, Robert R; Bevan, John W

    2015-06-14

    A generalized formulation of explicit force-based transformations is introduced to investigate the concept of a canonical potential in both fundamental chemical and intermolecular bonding. Different classes of representative ground electronic state pairwise interatomic interactions are referenced to a chosen canonical potential illustrating application of such transformations. Specifically, accurately determined potentials of the diatomic molecules H2, H2(+), HF, LiH, argon dimer, and one-dimensional dissociative coordinates in Ar-HBr, OC-HF, and OC-Cl2 are investigated throughout their bound potentials. Advantages of the current formulation for accurately evaluating equilibrium dissociation energies and a fundamentally different unified perspective on nature of intermolecular interactions will be emphasized. In particular, this canonical approach has significance to previous assertions that there is no very fundamental distinction between van der Waals bonding and covalent bonding or for that matter hydrogen and halogen bonds.

  5. Linear VSS and Distributed Commitments Based on Secret Sharing and Pairwise Checks

    DEFF Research Database (Denmark)

    Fehr, Serge; Maurer, Ueli M.

    2002-01-01

    . VSS and DC are main building blocks for unconditional secure multi-party computation protocols. This general approach covers all known linear VSS and DC schemes. The main theorem states that the security of a scheme is equivalent to a pure linear-algebra condition on the linear mappings (e.......g. described as matrices and vectors) describing the scheme. The security of all known schemes follows as corollaries whose proofs are pure linear-algebra arguments, in contrast to some hybrid arguments used in the literature. Our approach is demonstrated for the CDM DC scheme, which we generalize to be secure......We present a general treatment of all non-cryptographic (i.e., information-theoretically secure) linear veriable-secret-sharing (VSS) and distributed-commitment (DC) schemes, based on an underlying secret sharing scheme, pairwise checks between players, complaints, and accusations of the dealer...

  6. Subjective Probabilities for State-Dependent Continuous Utility

    NARCIS (Netherlands)

    P.P. Wakker (Peter)

    1987-01-01

    textabstractFor the expected utility model with state dependent utilities, Karni, Schmeidler and Vind (1983) have shown how to recover uniquely the involved subjective probabilities if the preferences, contingent on a hypothetical probability distribution over the state space, are known. This they

  7. Contributions to quantum probability

    International Nuclear Information System (INIS)

    Fritz, Tobias

    2010-01-01

    Chapter 1: On the existence of quantum representations for two dichotomic measurements. Under which conditions do outcome probabilities of measurements possess a quantum-mechanical model? This kind of problem is solved here for the case of two dichotomic von Neumann measurements which can be applied repeatedly to a quantum system with trivial dynamics. The solution uses methods from the theory of operator algebras and the theory of moment problems. The ensuing conditions reveal surprisingly simple relations between certain quantum-mechanical probabilities. It also shown that generally, none of these relations holds in general probabilistic models. This result might facilitate further experimental discrimination between quantum mechanics and other general probabilistic theories. Chapter 2: Possibilistic Physics. I try to outline a framework for fundamental physics where the concept of probability gets replaced by the concept of possibility. Whereas a probabilistic theory assigns a state-dependent probability value to each outcome of each measurement, a possibilistic theory merely assigns one of the state-dependent labels ''possible to occur'' or ''impossible to occur'' to each outcome of each measurement. It is argued that Spekkens' combinatorial toy theory of quantum mechanics is inconsistent in a probabilistic framework, but can be regarded as possibilistic. Then, I introduce the concept of possibilistic local hidden variable models and derive a class of possibilistic Bell inequalities which are violated for the possibilistic Popescu-Rohrlich boxes. The chapter ends with a philosophical discussion on possibilistic vs. probabilistic. It can be argued that, due to better falsifiability properties, a possibilistic theory has higher predictive power than a probabilistic one. Chapter 3: The quantum region for von Neumann measurements with postselection. It is determined under which conditions a probability distribution on a finite set can occur as the outcome

  8. Electrofishing capture probability of smallmouth bass in streams

    Science.gov (United States)

    Dauwalter, D.C.; Fisher, W.L.

    2007-01-01

    Abundance estimation is an integral part of understanding the ecology and advancing the management of fish populations and communities. Mark-recapture and removal methods are commonly used to estimate the abundance of stream fishes. Alternatively, abundance can be estimated by dividing the number of individuals sampled by the probability of capture. We conducted a mark-recapture study and used multiple repeated-measures logistic regression to determine the influence of fish size, sampling procedures, and stream habitat variables on the cumulative capture probability for smallmouth bass Micropterus dolomieu in two eastern Oklahoma streams. The predicted capture probability was used to adjust the number of individuals sampled to obtain abundance estimates. The observed capture probabilities were higher for larger fish and decreased with successive electrofishing passes for larger fish only. Model selection suggested that the number of electrofishing passes, fish length, and mean thalweg depth affected capture probabilities the most; there was little evidence for any effect of electrofishing power density and woody debris density on capture probability. Leave-one-out cross validation showed that the cumulative capture probability model predicts smallmouth abundance accurately. ?? Copyright by the American Fisheries Society 2007.

  9. Gene ontology analysis of pairwise genetic associations in two genome-wide studies of sporadic ALS

    Directory of Open Access Journals (Sweden)

    Kim Nora

    2012-07-01

    Full Text Available Abstract Background It is increasingly clear that common human diseases have a complex genetic architecture characterized by both additive and nonadditive genetic effects. The goal of the present study was to determine whether patterns of both additive and nonadditive genetic associations aggregate in specific functional groups as defined by the Gene Ontology (GO. Results We first estimated all pairwise additive and nonadditive genetic effects using the multifactor dimensionality reduction (MDR method that makes few assumptions about the underlying genetic model. Statistical significance was evaluated using permutation testing in two genome-wide association studies of ALS. The detection data consisted of 276 subjects with ALS and 271 healthy controls while the replication data consisted of 221 subjects with ALS and 211 healthy controls. Both studies included genotypes from approximately 550,000 single-nucleotide polymorphisms (SNPs. Each SNP was mapped to a gene if it was within 500 kb of the start or end. Each SNP was assigned a p-value based on its strongest joint effect with the other SNPs. We then used the Exploratory Visual Analysis (EVA method and software to assign a p-value to each gene based on the overabundance of significant SNPs at the α = 0.05 level in the gene. We also used EVA to assign p-values to each GO group based on the overabundance of significant genes at the α = 0.05 level. A GO category was determined to replicate if that category was significant at the α = 0.05 level in both studies. We found two GO categories that replicated in both studies. The first, ‘Regulation of Cellular Component Organization and Biogenesis’, a GO Biological Process, had p-values of 0.010 and 0.014 in the detection and replication studies, respectively. The second, ‘Actin Cytoskeleton’, a GO Cellular Component, had p-values of 0.040 and 0.046 in the detection and replication studies, respectively. Conclusions Pathway

  10. Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study.

    Science.gov (United States)

    de Uña-Álvarez, Jacobo; Meira-Machado, Luís

    2015-06-01

    Multi-state models are often used for modeling complex event history data. In these models the estimation of the transition probabilities is of particular interest, since they allow for long-term predictions of the process. These quantities have been traditionally estimated by the Aalen-Johansen estimator, which is consistent if the process is Markov. Several non-Markov estimators have been proposed in the recent literature, and their superiority with respect to the Aalen-Johansen estimator has been proved in situations in which the Markov condition is strongly violated. However, the existing estimators have the drawback of requiring that the support of the censoring distribution contains the support of the lifetime distribution, which is not often the case. In this article, we propose two new methods for estimating the transition probabilities in the progressive illness-death model. Some asymptotic results are derived. The proposed estimators are consistent regardless the Markov condition and the referred assumption about the censoring support. We explore the finite sample behavior of the estimators through simulations. The main conclusion of this piece of research is that the proposed estimators are much more efficient than the existing non-Markov estimators in most cases. An application to a clinical trial on colon cancer is included. Extensions to progressive processes beyond the three-state illness-death model are discussed. © 2015, The International Biometric Society.

  11. A hydroclimatological approach to predicting regional landslide probability using Landlab

    Directory of Open Access Journals (Sweden)

    R. Strauch

    2018-02-01

    Full Text Available We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km2. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at mid-elevations (1400 to 2400 m, and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

  12. A hydroclimatological approach to predicting regional landslide probability using Landlab

    Science.gov (United States)

    Strauch, Ronda; Istanbulluoglu, Erkan; Nudurupati, Sai Siddhartha; Bandaragoda, Christina; Gasparini, Nicole M.; Tucker, Gregory E.

    2018-02-01

    We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km2. The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at mid-elevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.

  13. Fundamentals of applied probability and random processes

    CERN Document Server

    Ibe, Oliver

    2005-01-01

    This book is based on the premise that engineers use probability as a modeling tool, and that probability can be applied to the solution of engineering problems. Engineers and students studying probability and random processes also need to analyze data, and thus need some knowledge of statistics. This book is designed to provide students with a thorough grounding in probability and stochastic processes, demonstrate their applicability to real-world problems, and introduce the basics of statistics. The book''s clear writing style and homework problems make it ideal for the classroom or for self-study.* Good and solid introduction to probability theory and stochastic processes * Logically organized; writing is presented in a clear manner * Choice of topics is comprehensive within the area of probability * Ample homework problems are organized into chapter sections

  14. Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context.

    Science.gov (United States)

    Karim, Mohammad Ehsanul; Platt, Robert W

    2017-06-15

    Correct specification of the inverse probability weighting (IPW) model is necessary for consistent inference from a marginal structural Cox model (MSCM). In practical applications, researchers are typically unaware of the true specification of the weight model. Nonetheless, IPWs are commonly estimated using parametric models, such as the main-effects logistic regression model. In practice, assumptions underlying such models may not hold and data-adaptive statistical learning methods may provide an alternative. Many candidate statistical learning approaches are available in the literature. However, the optimal approach for a given dataset is impossible to predict. Super learner (SL) has been proposed as a tool for selecting an optimal learner from a set of candidates using cross-validation. In this study, we evaluate the usefulness of a SL in estimating IPW in four different MSCM simulation scenarios, in which we varied the specification of the true weight model specification (linear and/or additive). Our simulations show that, in the presence of weight model misspecification, with a rich and diverse set of candidate algorithms, SL can generally offer a better alternative to the commonly used statistical learning approaches in terms of MSE as well as the coverage probabilities of the estimated effect in an MSCM. The findings from the simulation studies guided the application of the MSCM in a multiple sclerosis cohort from British Columbia, Canada (1995-2008), to estimate the impact of beta-interferon treatment in delaying disability progression. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  15. Upper Bounds for Ruin Probability with Stochastic Investment Return

    Institute of Scientific and Technical Information of China (English)

    ZHANG Lihong

    2005-01-01

    Risk models with stochastic investment return are widely held in practice, as well as in more challenging research fields. Risk theory is mainly concerned with ruin probability, and a tight bound for ruin probability is the best for practical use. This paper presents a discrete time risk model with stochastic investment return. Conditional expectation properties and martingale inequalities are used to obtain both exponential and non-exponential upper bounds for the ruin probability.

  16. Sensitivity of the probability of failure to probability of detection curve regions

    International Nuclear Information System (INIS)

    Garza, J.; Millwater, H.

    2016-01-01

    Non-destructive inspection (NDI) techniques have been shown to play a vital role in fracture control plans, structural health monitoring, and ensuring availability and reliability of piping, pressure vessels, mechanical and aerospace equipment. Probabilistic fatigue simulations are often used in order to determine the efficacy of an inspection procedure with the NDI method modeled as a probability of detection (POD) curve. These simulations can be used to determine the most advantageous NDI method for a given application. As an aid to this process, a first order sensitivity method of the probability-of-failure (POF) with respect to regions of the POD curve (lower tail, middle region, right tail) is developed and presented here. The sensitivity method computes the partial derivative of the POF with respect to a change in each region of a POD or multiple POD curves. The sensitivities are computed at no cost by reusing the samples from an existing Monte Carlo (MC) analysis. A numerical example is presented considering single and multiple inspections. - Highlights: • Sensitivities of probability-of-failure to a region of probability-of-detection curve. • The sensitivities are computed with negligible cost. • Sensitivities identify the important region of a POD curve. • Sensitivities can be used as a guide to selecting the optimal POD curve.

  17. An Empirical Model for Estimating the Probability of Electrical Short Circuits from Tin Whiskers. Part 2

    Science.gov (United States)

    Courey, Karim; Wright, Clara; Asfour, Shihab; Onar, Arzu; Bayliss, Jon; Ludwig, Larry

    2009-01-01

    In this experiment, an empirical model to quantify the probability of occurrence of an electrical short circuit from tin whiskers as a function of voltage was developed. This empirical model can be used to improve existing risk simulation models. FIB and TEM images of a tin whisker confirm the rare polycrystalline structure on one of the three whiskers studied. FIB cross-section of the card guides verified that the tin finish was bright tin.

  18. Human Error Probability Assessment During Maintenance Activities of Marine Systems

    Directory of Open Access Journals (Sweden)

    Rabiul Islam

    2018-03-01

    Full Text Available Background: Maintenance operations on-board ships are highly demanding. Maintenance operations are intensive activities requiring high man–machine interactions in challenging and evolving conditions. The evolving conditions are weather conditions, workplace temperature, ship motion, noise and vibration, and workload and stress. For example, extreme weather condition affects seafarers' performance, increasing the chances of error, and, consequently, can cause injuries or fatalities to personnel. An effective human error probability model is required to better manage maintenance on-board ships. The developed model would assist in developing and maintaining effective risk management protocols. Thus, the objective of this study is to develop a human error probability model considering various internal and external factors affecting seafarers' performance. Methods: The human error probability model is developed using probability theory applied to Bayesian network. The model is tested using the data received through the developed questionnaire survey of >200 experienced seafarers with >5 years of experience. The model developed in this study is used to find out the reliability of human performance on particular maintenance activities. Results: The developed methodology is tested on the maintenance of marine engine's cooling water pump for engine department and anchor windlass for deck department. In the considered case studies, human error probabilities are estimated in various scenarios and the results are compared between the scenarios and the different seafarer categories. The results of the case studies for both departments are also compared. Conclusion: The developed model is effective in assessing human error probabilities. These probabilities would get dynamically updated as and when new information is available on changes in either internal (i.e., training, experience, and fatigue or external (i.e., environmental and operational conditions

  19. Probability concepts in quality risk management.

    Science.gov (United States)

    Claycamp, H Gregg

    2012-01-01

    Essentially any concept of risk is built on fundamental concepts of chance, likelihood, or probability. Although risk is generally a probability of loss of something of value, given that a risk-generating event will occur or has occurred, it is ironic that the quality risk management literature and guidelines on quality risk management tools are relatively silent on the meaning and uses of "probability." The probability concept is typically applied by risk managers as a combination of frequency-based calculation and a "degree of belief" meaning of probability. Probability as a concept that is crucial for understanding and managing risk is discussed through examples from the most general, scenario-defining and ranking tools that use probability implicitly to more specific probabilistic tools in risk management. A rich history of probability in risk management applied to other fields suggests that high-quality risk management decisions benefit from the implementation of more thoughtful probability concepts in both risk modeling and risk management. Essentially any concept of risk is built on fundamental concepts of chance, likelihood, or probability. Although "risk" generally describes a probability of loss of something of value, given that a risk-generating event will occur or has occurred, it is ironic that the quality risk management literature and guidelines on quality risk management methodologies and respective tools focus on managing severity but are relatively silent on the in-depth meaning and uses of "probability." Pharmaceutical manufacturers are expanding their use of quality risk management to identify and manage risks to the patient that might occur in phases of the pharmaceutical life cycle from drug development to manufacture, marketing to product discontinuation. A probability concept is typically applied by risk managers as a combination of data-based measures of probability and a subjective "degree of belief" meaning of probability. Probability as

  20. MASTER: a model to improve and standardize clinical breakpoints for antimicrobial susceptibility testing using forecast probabilities.

    Science.gov (United States)

    Blöchliger, Nicolas; Keller, Peter M; Böttger, Erik C; Hombach, Michael

    2017-09-01

    The procedure for setting clinical breakpoints (CBPs) for antimicrobial susceptibility has been poorly standardized with respect to population data, pharmacokinetic parameters and clinical outcome. Tools to standardize CBP setting could result in improved antibiogram forecast probabilities. We propose a model to estimate probabilities for methodological categorization errors and defined zones of methodological uncertainty (ZMUs), i.e. ranges of zone diameters that cannot reliably be classified. The impact of ZMUs on methodological error rates was used for CBP optimization. The model distinguishes theoretical true inhibition zone diameters from observed diameters, which suffer from methodological variation. True diameter distributions are described with a normal mixture model. The model was fitted to observed inhibition zone diameters of clinical Escherichia coli strains. Repeated measurements for a quality control strain were used to quantify methodological variation. For 9 of 13 antibiotics analysed, our model predicted error rates of  0.1% for ampicillin, cefoxitin, cefuroxime and amoxicillin/clavulanic acid. Increasing the susceptible CBP (cefoxitin) and introducing ZMUs (ampicillin, cefuroxime, amoxicillin/clavulanic acid) decreased error rates to < 0.1%. ZMUs contained low numbers of isolates for ampicillin and cefuroxime (3% and 6%), whereas the ZMU for amoxicillin/clavulanic acid contained 41% of all isolates and was considered not practical. We demonstrate that CBPs can be improved and standardized by minimizing methodological categorization error rates. ZMUs may be introduced if an intermediate zone is not appropriate for pharmacokinetic/pharmacodynamic or drug dosing reasons. Optimized CBPs will provide a standardized antibiotic susceptibility testing interpretation at a defined level of probability. © The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For

  1. Hydrological model calibration for flood prediction in current and future climates using probability distributions of observed peak flows and model based rainfall

    Science.gov (United States)

    Haberlandt, Uwe; Wallner, Markus; Radtke, Imke

    2013-04-01

    Derived flood frequency analysis based on continuous hydrological modelling is very demanding regarding the required length and temporal resolution of precipitation input data. Often such flood predictions are obtained using long precipitation time series from stochastic approaches or from regional climate models as input. However, the calibration of the hydrological model is usually done using short time series of observed data. This inconsistent employment of different data types for calibration and application of a hydrological model increases its uncertainty. Here, it is proposed to calibrate a hydrological model directly on probability distributions of observed peak flows using model based rainfall in line with its later application. Two examples are given to illustrate the idea. The first one deals with classical derived flood frequency analysis using input data from an hourly stochastic rainfall model. The second one concerns a climate impact analysis using hourly precipitation from a regional climate model. The results show that: (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated on extreme conditions works quite well for average conditions but not vice versa, (III) the calibration of the hydrological model using regional climate model data works as an implicit bias correction method and (IV) the best performance for flood estimation is usually obtained when model based precipitation and observed probability distribution of peak flows are used for model calibration.

  2. Upgrading Probability via Fractions of Events

    Directory of Open Access Journals (Sweden)

    Frič Roman

    2016-08-01

    Full Text Available The influence of “Grundbegriffe” by A. N. Kolmogorov (published in 1933 on education in the area of probability and its impact on research in stochastics cannot be overestimated. We would like to point out three aspects of the classical probability theory “calling for” an upgrade: (i classical random events are black-and-white (Boolean; (ii classical random variables do not model quantum phenomena; (iii basic maps (probability measures and observables { dual maps to random variables have very different “mathematical nature”. Accordingly, we propose an upgraded probability theory based on Łukasiewicz operations (multivalued logic on events, elementary category theory, and covering the classical probability theory as a special case. The upgrade can be compared to replacing calculations with integers by calculations with rational (and real numbers. Namely, to avoid the three objections, we embed the classical (Boolean random events (represented by the f0; 1g-valued indicator functions of sets into upgraded random events (represented by measurable {0; 1}-valued functions, the minimal domain of probability containing “fractions” of classical random events, and we upgrade the notions of probability measure and random variable.

  3. p-adic probability interpretation of Bell's inequality

    International Nuclear Information System (INIS)

    Khrennikov, A.

    1995-01-01

    We study the violation of Bell's inequality using a p-adic generalization of the theory of probability. p-adic probability is introduced as a limit of relative frequencies but this limit exists with respect to a p-adic metric. In particular, negative probability distributions are well defined on the basis of the frequency definition. This new type of stochastics can be used to describe hidden-variables distributions of some quantum models. If the hidden variables have a p-adic probability distribution, Bell's inequality is not valid and it is not necessary to discuss the experimental violations of this inequality. ((orig.))

  4. VOLCANIC RISK ASSESSMENT - PROBABILITY AND CONSEQUENCES

    International Nuclear Information System (INIS)

    G.A. Valentine; F.V. Perry; S. Dartevelle

    2005-01-01

    Risk is the product of the probability and consequences of an event. Both of these must be based upon sound science that integrates field data, experiments, and modeling, but must also be useful to decision makers who likely do not understand all aspects of the underlying science. We review a decision framework used in many fields such as performance assessment for hazardous and/or radioactive waste disposal sites that can serve to guide the volcanological community towards integrated risk assessment. In this framework the underlying scientific understanding of processes that affect probability and consequences drive the decision-level results, but in turn these results can drive focused research in areas that cause the greatest level of uncertainty at the decision level. We review two examples of the determination of volcanic event probability: (1) probability of a new volcano forming at the proposed Yucca Mountain radioactive waste repository, and (2) probability that a subsurface repository in Japan would be affected by the nearby formation of a new stratovolcano. We also provide examples of work on consequences of explosive eruptions, within the framework mentioned above. These include field-based studies aimed at providing data for ''closure'' of wall rock erosion terms in a conduit flow model, predictions of dynamic pressure and other variables related to damage by pyroclastic flow into underground structures, and vulnerability criteria for structures subjected to conditions of explosive eruption. Process models (e.g., multiphase flow) are important for testing the validity or relative importance of possible scenarios in a volcanic risk assessment. We show how time-dependent multiphase modeling of explosive ''eruption'' of basaltic magma into an open tunnel (drift) at the Yucca Mountain repository provides insight into proposed scenarios that include the development of secondary pathways to the Earth's surface. Addressing volcanic risk within a decision

  5. Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models

    NARCIS (Netherlands)

    Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A.; van t Veld, Aart A.

    2012-01-01

    PURPOSE: To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator

  6. Application of wildfire spread and behavior models to assess fire probability and severity in the Mediterranean region

    Science.gov (United States)

    Salis, Michele; Arca, Bachisio; Bacciu, Valentina; Spano, Donatella; Duce, Pierpaolo; Santoni, Paul; Ager, Alan; Finney, Mark

    2010-05-01

    Characterizing the spatial pattern of large fire occurrence and severity is an important feature of the fire management planning in the Mediterranean region. The spatial characterization of fire probabilities, fire behavior distributions and value changes are key components for quantitative risk assessment and for prioritizing fire suppression resources, fuel treatments and law enforcement. Because of the growing wildfire severity and frequency in recent years (e.g.: Portugal, 2003 and 2005; Italy and Greece, 2007 and 2009), there is an increasing demand for models and tools that can aid in wildfire prediction and prevention. Newer wildfire simulation systems offer promise in this regard, and allow for fine scale modeling of wildfire severity and probability. Several new applications has resulted from the development of a minimum travel time (MTT) fire spread algorithm (Finney, 2002), that models the fire growth searching for the minimum time for fire to travel among nodes in a 2D network. The MTT approach makes computationally feasible to simulate thousands of fires and generate burn probability and fire severity maps over large areas. The MTT algorithm is imbedded in a number of research and fire modeling applications. High performance computers are typically used for MTT simulations, although the algorithm is also implemented in the FlamMap program (www.fire.org). In this work, we described the application of the MTT algorithm to estimate spatial patterns of burn probability and to analyze wildfire severity in three fire prone areas of the Mediterranean Basin, specifically Sardinia (Italy), Sicily (Italy) and Corsica (France) islands. We assembled fuels and topographic data for the simulations in 500 x 500 m grids for the study areas. The simulations were run using 100,000 ignitions under weather conditions that replicated severe and moderate weather conditions (97th and 70th percentile, July and August weather, 1995-2007). We used both random ignition locations

  7. Empirical probability model of cold plasma environment in the Jovian magnetosphere

    Science.gov (United States)

    Futaana, Yoshifumi; Wang, Xiao-Dong; Barabash, Stas; Roussos, Elias; Truscott, Pete

    2015-04-01

    We analyzed the Galileo PLS dataset to produce a new cold plasma environment model for the Jovian magneto- sphere. Although there exist many sophisticated radiation models, treating energetic plasma (e.g. JOSE, GIRE, or Salammbo), only a limited number of simple models has been utilized for cold plasma environment. By extend- ing the existing cold plasma models toward the probability domain, we can predict the extreme periods of Jovian environment by specifying the percentile of the environmental parameters. The new model was produced in the following procedure. We first referred to the existing cold plasma models of Divine and Garrett, 1983 (DG83) or Bagenal and Delamere 2011 (BD11). These models are scaled to fit the statistical median of the parameters obtained from Galileo PLS data. The scaled model (also called as "mean model") indicates the median environment of Jovian magnetosphere. Then, assuming that the deviations in the Galileo PLS parameters are purely due to variations in the environment, we extended the mean model toward the percentile domain. The input parameter of the model is simply the position of the spacecraft (distance, magnetic longitude and lati- tude) and the specific percentile (e.g. 0.5 for the mean model). All the parameters in the model are described in mathematical forms; therefore the needed computational resources are quite low. The new model can be used for assessing the JUICE mission profile. The spatial extent of the model covers the main phase of the JUICE mission; namely from the Europa orbit to 40 Rj (where Rj is the radius of Jupiter). In addition, theoretical extensions toward the latitudinal direction are also included in the model to support the high latitude orbit of the JUICE spacecraft.

  8. APOLLO: a quality assessment service for single and multiple protein models.

    Science.gov (United States)

    Wang, Zheng; Eickholt, Jesse; Cheng, Jianlin

    2011-06-15

    We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure. http://sysbio.rnet.missouri.edu/apollo/. Single and pair-wise global quality assessment software is also available at the site.

  9. Analytical models of probability distribution and excess noise factor of solid state photomultiplier signals with crosstalk

    International Nuclear Information System (INIS)

    Vinogradov, S.

    2012-01-01

    Silicon Photomultipliers (SiPM), also called Solid State Photomultipliers (SSPM), are based on Geiger mode avalanche breakdown that is limited by a strong negative feedback. An SSPM can detect and resolve single photons due to the high gain and ultra-low excess noise of avalanche multiplication in this mode. Crosstalk and afterpulsing processes associated with the high gain introduce specific excess noise and deteriorate the photon number resolution of the SSPM. The probabilistic features of these processes are widely studied because of its significance for the SSPM design, characterization, optimization and application, but the process modeling is mostly based on Monte Carlo simulations and numerical methods. In this study, crosstalk is considered to be a branching Poisson process, and analytical models of probability distribution and excess noise factor (ENF) of SSPM signals based on the Borel distribution as an advance on the geometric distribution models are presented and discussed. The models are found to be in a good agreement with the experimental probability distributions for dark counts and a few photon spectrums in a wide range of fired pixels number as well as with observed super-linear behavior of crosstalk ENF.

  10. Allelic drop-out probabilities estimated by logistic regression

    DEFF Research Database (Denmark)

    Tvedebrink, Torben; Eriksen, Poul Svante; Asplund, Maria

    2012-01-01

    We discuss the model for estimating drop-out probabilities presented by Tvedebrink et al. [7] and the concerns, that have been raised. The criticism of the model has demonstrated that the model is not perfect. However, the model is very useful for advanced forensic genetic work, where allelic drop-out...... is occurring. With this discussion, we hope to improve the drop-out model, so that it can be used for practical forensic genetics and stimulate further discussions. We discuss how to estimate drop-out probabilities when using a varying number of PCR cycles and other experimental conditions....

  11. Classical probabilities for Majorana and Weyl spinors

    International Nuclear Information System (INIS)

    Wetterich, C.

    2011-01-01

    Highlights: → Map of classical statistical Ising model to fermionic quantum field theory. → Lattice-regularized real Grassmann functional integral for single Weyl spinor. → Emerging complex structure characteristic for quantum physics. → A classical statistical ensemble describes a quantum theory. - Abstract: We construct a map between the quantum field theory of free Weyl or Majorana fermions and the probability distribution of a classical statistical ensemble for Ising spins or discrete bits. More precisely, a Grassmann functional integral based on a real Grassmann algebra specifies the time evolution of the real wave function q τ (t) for the Ising states τ. The time dependent probability distribution of a generalized Ising model obtains as p τ (t)=q τ 2 (t). The functional integral employs a lattice regularization for single Weyl or Majorana spinors. We further introduce the complex structure characteristic for quantum mechanics. Probability distributions of the Ising model which correspond to one or many propagating fermions are discussed explicitly. Expectation values of observables can be computed equivalently in the classical statistical Ising model or in the quantum field theory for fermions.

  12. Discrete probability models and methods probability on graphs and trees, Markov chains and random fields, entropy and coding

    CERN Document Server

    Brémaud, Pierre

    2017-01-01

    The emphasis in this book is placed on general models (Markov chains, random fields, random graphs), universal methods (the probabilistic method, the coupling method, the Stein-Chen method, martingale methods, the method of types) and versatile tools (Chernoff's bound, Hoeffding's inequality, Holley's inequality) whose domain of application extends far beyond the present text. Although the examples treated in the book relate to the possible applications, in the communication and computing sciences, in operations research and in physics, this book is in the first instance concerned with theory. The level of the book is that of a beginning graduate course. It is self-contained, the prerequisites consisting merely of basic calculus (series) and basic linear algebra (matrices). The reader is not assumed to be trained in probability since the first chapters give in considerable detail the background necessary to understand the rest of the book. .

  13. K-shell ionization probability in energetic nearly symmetric heavy-ion collisions

    International Nuclear Information System (INIS)

    Tserruya, I.; Schmidt-Boecking, H.; Schuch, R.

    1977-01-01

    Impact parameter dependent K-x-ray emission probabilities for the projectile and target atoms have been measured in 35 MeV Cl on Cl, Cl on Ti and Cl on Ni collisions. The sum of projectile plus target K-shell ionization probability is taken as a measure of the total 2psigma ionization probability. The 2pπ-2psigma totational coupling model is in clear disagreement with the present results. On the other hand the sum of probabilities is reproduced both in shape and absolute magnitude by the statistical model for inner-shell ionization. The K-shell ionization probability of the higher -Z collision partner is well described by this model including the 2psigma-1ssigma vacancy sharing probability calculated as a function of the impact parameter. (author)

  14. Contributions to quantum probability

    Energy Technology Data Exchange (ETDEWEB)

    Fritz, Tobias

    2010-06-25

    Chapter 1: On the existence of quantum representations for two dichotomic measurements. Under which conditions do outcome probabilities of measurements possess a quantum-mechanical model? This kind of problem is solved here for the case of two dichotomic von Neumann measurements which can be applied repeatedly to a quantum system with trivial dynamics. The solution uses methods from the theory of operator algebras and the theory of moment problems. The ensuing conditions reveal surprisingly simple relations between certain quantum-mechanical probabilities. It also shown that generally, none of these relations holds in general probabilistic models. This result might facilitate further experimental discrimination between quantum mechanics and other general probabilistic theories. Chapter 2: Possibilistic Physics. I try to outline a framework for fundamental physics where the concept of probability gets replaced by the concept of possibility. Whereas a probabilistic theory assigns a state-dependent probability value to each outcome of each measurement, a possibilistic theory merely assigns one of the state-dependent labels ''possible to occur'' or ''impossible to occur'' to each outcome of each measurement. It is argued that Spekkens' combinatorial toy theory of quantum mechanics is inconsistent in a probabilistic framework, but can be regarded as possibilistic. Then, I introduce the concept of possibilistic local hidden variable models and derive a class of possibilistic Bell inequalities which are violated for the possibilistic Popescu-Rohrlich boxes. The chapter ends with a philosophical discussion on possibilistic vs. probabilistic. It can be argued that, due to better falsifiability properties, a possibilistic theory has higher predictive power than a probabilistic one. Chapter 3: The quantum region for von Neumann measurements with postselection. It is determined under which conditions a probability distribution on a

  15. Converting dose distributions into tumour control probability

    International Nuclear Information System (INIS)

    Nahum, A.E.

    1996-01-01

    The endpoints in radiotherapy that are truly of relevance are not dose distributions but the probability of local control, sometimes known as the Tumour Control Probability (TCP) and the Probability of Normal Tissue Complications (NTCP). A model for the estimation of TCP based on simple radiobiological considerations is described. It is shown that incorporation of inter-patient heterogeneity into the radiosensitivity parameter a through s a can result in a clinically realistic slope for the dose-response curve. The model is applied to inhomogeneous target dose distributions in order to demonstrate the relationship between dose uniformity and s a . The consequences of varying clonogenic density are also explored. Finally the model is applied to the target-volume DVHs for patients in a clinical trial of conformal pelvic radiotherapy; the effect of dose inhomogeneities on distributions of TCP are shown as well as the potential benefits of customizing the target dose according to normal-tissue DVHs. (author). 37 refs, 9 figs

  16. Converting dose distributions into tumour control probability

    Energy Technology Data Exchange (ETDEWEB)

    Nahum, A E [The Royal Marsden Hospital, London (United Kingdom). Joint Dept. of Physics

    1996-08-01

    The endpoints in radiotherapy that are truly of relevance are not dose distributions but the probability of local control, sometimes known as the Tumour Control Probability (TCP) and the Probability of Normal Tissue Complications (NTCP). A model for the estimation of TCP based on simple radiobiological considerations is described. It is shown that incorporation of inter-patient heterogeneity into the radiosensitivity parameter a through s{sub a} can result in a clinically realistic slope for the dose-response curve. The model is applied to inhomogeneous target dose distributions in order to demonstrate the relationship between dose uniformity and s{sub a}. The consequences of varying clonogenic density are also explored. Finally the model is applied to the target-volume DVHs for patients in a clinical trial of conformal pelvic radiotherapy; the effect of dose inhomogeneities on distributions of TCP are shown as well as the potential benefits of customizing the target dose according to normal-tissue DVHs. (author). 37 refs, 9 figs.

  17. Spatial probability aids visual stimulus discrimination

    Directory of Open Access Journals (Sweden)

    Michael Druker

    2010-08-01

    Full Text Available We investigated whether the statistical predictability of a target's location would influence how quickly and accurately it was classified. Recent results have suggested that spatial probability can be a cue for the allocation of attention in visual search. One explanation for probability cuing is spatial repetition priming. In our two experiments we used probability distributions that were continuous across the display rather than relying on a few arbitrary screen locations. This produced fewer spatial repeats and allowed us to dissociate the effect of a high probability location from that of short-term spatial repetition. The task required participants to quickly judge the color of a single dot presented on a computer screen. In Experiment 1, targets were more probable in an off-center hotspot of high probability that gradually declined to a background rate. Targets garnered faster responses if they were near earlier target locations (priming and if they were near the high probability hotspot (probability cuing. In Experiment 2, target locations were chosen on three concentric circles around fixation. One circle contained 80% of targets. The value of this ring distribution is that it allowed for a spatially restricted high probability zone in which sequentially repeated trials were not likely to be physically close. Participant performance was sensitive to the high-probability circle in addition to the expected effects of eccentricity and the distance to recent targets. These two experiments suggest that inhomogeneities in spatial probability can be learned and used by participants on-line and without prompting as an aid for visual stimulus discrimination and that spatial repetition priming is not a sufficient explanation for this effect. Future models of attention should consider explicitly incorporating the probabilities of targets locations and features.

  18. A comparison of the probability distribution of observed substorm magnitude with that predicted by a minimal substorm model

    Directory of Open Access Journals (Sweden)

    S. K. Morley

    2007-11-01

    Full Text Available We compare the probability distributions of substorm magnetic bay magnitudes from observations and a minimal substorm model. The observed distribution was derived previously and independently using the IL index from the IMAGE magnetometer network. The model distribution is derived from a synthetic AL index time series created using real solar wind data and a minimal substorm model, which was previously shown to reproduce observed substorm waiting times. There are two free parameters in the model which scale the contributions to AL from the directly-driven DP2 electrojet and loading-unloading DP1 electrojet, respectively. In a limited region of the 2-D parameter space of the model, the probability distribution of modelled substorm bay magnitudes is not significantly different to the observed distribution. The ranges of the two parameters giving acceptable (95% confidence level agreement are consistent with expectations using results from other studies. The approximately linear relationship between the two free parameters over these ranges implies that the substorm magnitude simply scales linearly with the solar wind power input at the time of substorm onset.

  19. Establishment probability in newly founded populations

    Directory of Open Access Journals (Sweden)

    Gusset Markus

    2012-06-01

    Full Text Available Abstract Background Establishment success in newly founded populations relies on reaching the established phase, which is defined by characteristic fluctuations of the population’s state variables. Stochastic population models can be used to quantify the establishment probability of newly founded populations; however, so far no simple but robust method for doing so existed. To determine a critical initial number of individuals that need to be released to reach the established phase, we used a novel application of the “Wissel plot”, where –ln(1 – P0(t is plotted against time t. This plot is based on the equation P0t=1–c1e–ω1t, which relates the probability of extinction by time t, P0(t, to two constants: c1 describes the probability of a newly founded population to reach the established phase, whereas ω1 describes the population’s probability of extinction per short time interval once established. Results For illustration, we applied the method to a previously developed stochastic population model of the endangered African wild dog (Lycaon pictus. A newly founded population reaches the established phase if the intercept of the (extrapolated linear parts of the “Wissel plot” with the y-axis, which is –ln(c1, is negative. For wild dogs in our model, this is the case if a critical initial number of four packs, consisting of eight individuals each, are released. Conclusions The method we present to quantify the establishment probability of newly founded populations is generic and inferences thus are transferable to other systems across the field of conservation biology. In contrast to other methods, our approach disaggregates the components of a population’s viability by distinguishing establishment from persistence.

  20. Bayesian Probability Theory

    Science.gov (United States)

    von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo

    2014-06-01

    Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.

  1. Grading the probabilities of credit default risk for Malaysian listed companies by using the KMV-Merton model

    Science.gov (United States)

    Anuwar, Muhammad Hafidz; Jaffar, Maheran Mohd

    2017-08-01

    This paper provides an overview for the assessment of credit risk specific to the banks. In finance, risk is a term to reflect the potential of financial loss. The risk of default on loan may increase when a company does not make a payment on that loan when the time comes. Hence, this framework analyses the KMV-Merton model to estimate the probabilities of default for Malaysian listed companies. In this way, banks can verify the ability of companies to meet their loan commitments in order to overcome bad investments and financial losses. This model has been applied to all Malaysian listed companies in Bursa Malaysia for estimating the credit default probabilities of companies and compare with the rating given by the rating agency, which is RAM Holdings Berhad to conform to reality. Then, the significance of this study is a credit risk grade is proposed by using the KMV-Merton model for the Malaysian listed companies.

  2. Theory of pairwise coupling embedded in more general local dispersion relations

    International Nuclear Information System (INIS)

    Fuchs, V.; Bers, A.; Harten, L.

    1985-01-01

    Earlier work on the mode conversion theory by Fuchs, Ko, and Bers is detailed and expanded upon, and its relation to energy conservation is discussed. Given a local dispersion relation, D(ω; k, z) = 0, describing stable waves excited at an externally imposed frequency ω, a pairwise mode-coupling event embedded therein is extracted by expanding D(k, z) around a contour k = k/sub c/(z) given by partialD/partialk = 0. The branch points of D(k, z) = 0 are the turning points of a second-order differential-equation representation. In obtaining the fraction of mode-converted energy, the connection formula and conservation of energy must be used together. Also, proper attention must be given to distinguish cases for which the coupling disappears or persists upon confluence of the branches, a property which is shown to depend on the forward (v/sub g/v/sub ph/>0) or backward (v/sub g/v/sub ph/<0) nature of the waves. Examples occurring in ion-cyclotron and lower-hybrid heating are presented, illustrating the use of the theory

  3. School and conference on probability theory

    International Nuclear Information System (INIS)

    Lawler, G.F.

    2004-01-01

    This volume includes expanded lecture notes from the School and Conference in Probability Theory held at ICTP in May, 2001. Probability theory is a very large area, too large for a single school and conference. The organizers, G. Lawler, C. Newman, and S. Varadhan chose to focus on a number of active research areas that have their roots in statistical physics. The pervasive theme in these lectures is trying to find the large time or large space behaviour of models defined on discrete lattices. Usually the definition of the model is relatively simple: either assigning a particular weight to each possible configuration (equilibrium statistical mechanics) or specifying the rules under which the system evolves (nonequilibrium statistical mechanics). Interacting particle systems is the area of probability that studies the evolution of particles (either finite or infinite in number) under random motions. The evolution of particles depends on the positions of the other particles; often one assumes that it depends only on the particles that are close to the particular particle. Thomas Liggett's lectures give an introduction to this very large area. Claudio Landim's follows up by discussing hydrodynamic limits of particle systems. The goal of this area is to describe the long time, large system size dynamics in terms of partial differential equations. The area of random media is concerned with the properties of materials or environments that are not homogeneous. Percolation theory studies one of the simplest stated models for impurities - taking a lattice and removing some of the vertices or bonds. Luiz Renato G. Fontes and Vladas Sidoravicius give a detailed introduction to this area. Random walk in random environment combines two sources of randomness - a particle performing stochastic motion in which the transition probabilities depend on position and have been chosen from some probability distribution. Alain-Sol Sznitman gives a survey of recent developments in this

  4. Cladding failure probability modeling for risk evaluations of fast reactors

    International Nuclear Information System (INIS)

    Mueller, C.J.; Kramer, J.M.

    1987-01-01

    This paper develops the methodology to incorporate cladding failure data and associated modeling into risk evaluations of liquid metal-cooled fast reactors (LMRs). Current US innovative designs for metal-fueled pool-type LMRs take advantage of inherent reactivity feedback mechanisms to limit reactor temperature increases in response to classic anticipated-transient-without-scram (ATWS) initiators. Final shutdown without reliance on engineered safety features can then be accomplished if sufficient time is available for operator intervention to terminate fission power production and/or provide auxiliary cooling prior to significant core disruption. Coherent cladding failure under the sustained elevated temperatures of ATWS events serves as one indicator of core disruption. In this paper we combine uncertainties in cladding failure data with uncertainties in calculations of ATWS cladding temperature conditions to calculate probabilities of cladding failure as a function of the time for accident recovery

  5. Normal tissue complication probability modeling of radiation-induced hypothyroidism after head-and-neck radiation therapy.

    Science.gov (United States)

    Bakhshandeh, Mohsen; Hashemi, Bijan; Mahdavi, Seied Rabi Mehdi; Nikoofar, Alireza; Vasheghani, Maryam; Kazemnejad, Anoshirvan

    2013-02-01

    To determine the dose-response relationship of the thyroid for radiation-induced hypothyroidism in head-and-neck radiation therapy, according to 6 normal tissue complication probability models, and to find the best-fit parameters of the models. Sixty-five patients treated with primary or postoperative radiation therapy for various cancers in the head-and-neck region were prospectively evaluated. Patient serum samples (tri-iodothyronine, thyroxine, thyroid-stimulating hormone [TSH], free tri-iodothyronine, and free thyroxine) were measured before and at regular time intervals until 1 year after the completion of radiation therapy. Dose-volume histograms (DVHs) of the patients' thyroid gland were derived from their computed tomography (CT)-based treatment planning data. Hypothyroidism was defined as increased TSH (subclinical hypothyroidism) or increased TSH in combination with decreased free thyroxine and thyroxine (clinical hypothyroidism). Thyroid DVHs were converted to 2 Gy/fraction equivalent doses using the linear-quadratic formula with α/β = 3 Gy. The evaluated models included the following: Lyman with the DVH reduced to the equivalent uniform dose (EUD), known as LEUD; Logit-EUD; mean dose; relative seriality; individual critical volume; and population critical volume models. The parameters of the models were obtained by fitting the patients' data using a maximum likelihood analysis method. The goodness of fit of the models was determined by the 2-sample Kolmogorov-Smirnov test. Ranking of the models was made according to Akaike's information criterion. Twenty-nine patients (44.6%) experienced hypothyroidism. None of the models was rejected according to the evaluation of the goodness of fit. The mean dose model was ranked as the best model on the basis of its Akaike's information criterion value. The D(50) estimated from the models was approximately 44 Gy. The implemented normal tissue complication probability models showed a parallel architecture for the

  6. Normal Tissue Complication Probability Modeling of Radiation-Induced Hypothyroidism After Head-and-Neck Radiation Therapy

    Energy Technology Data Exchange (ETDEWEB)

    Bakhshandeh, Mohsen [Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Hashemi, Bijan, E-mail: bhashemi@modares.ac.ir [Department of Medical Physics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran (Iran, Islamic Republic of); Mahdavi, Seied Rabi Mehdi [Department of Medical Physics, Faculty of Medical Sciences, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Nikoofar, Alireza; Vasheghani, Maryam [Department of Radiation Oncology, Hafte-Tir Hospital, Tehran University of Medical Sciences, Tehran (Iran, Islamic Republic of); Kazemnejad, Anoshirvan [Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran (Iran, Islamic Republic of)

    2013-02-01

    Purpose: To determine the dose-response relationship of the thyroid for radiation-induced hypothyroidism in head-and-neck radiation therapy, according to 6 normal tissue complication probability models, and to find the best-fit parameters of the models. Methods and Materials: Sixty-five patients treated with primary or postoperative radiation therapy for various cancers in the head-and-neck region were prospectively evaluated. Patient serum samples (tri-iodothyronine, thyroxine, thyroid-stimulating hormone [TSH], free tri-iodothyronine, and free thyroxine) were measured before and at regular time intervals until 1 year after the completion of radiation therapy. Dose-volume histograms (DVHs) of the patients' thyroid gland were derived from their computed tomography (CT)-based treatment planning data. Hypothyroidism was defined as increased TSH (subclinical hypothyroidism) or increased TSH in combination with decreased free thyroxine and thyroxine (clinical hypothyroidism). Thyroid DVHs were converted to 2 Gy/fraction equivalent doses using the linear-quadratic formula with {alpha}/{beta} = 3 Gy. The evaluated models included the following: Lyman with the DVH reduced to the equivalent uniform dose (EUD), known as LEUD; Logit-EUD; mean dose; relative seriality; individual critical volume; and population critical volume models. The parameters of the models were obtained by fitting the patients' data using a maximum likelihood analysis method. The goodness of fit of the models was determined by the 2-sample Kolmogorov-Smirnov test. Ranking of the models was made according to Akaike's information criterion. Results: Twenty-nine patients (44.6%) experienced hypothyroidism. None of the models was rejected according to the evaluation of the goodness of fit. The mean dose model was ranked as the best model on the basis of its Akaike's information criterion value. The D{sub 50} estimated from the models was approximately 44 Gy. Conclusions: The implemented

  7. Ruin probability with claims modeled by a stationary ergodic stable process

    NARCIS (Netherlands)

    Mikosch, T.; Samorodnitsky, G.

    2000-01-01

    For a random walk with negative drift we study the exceedance probability (ruin probability) of a high threshold. The steps of this walk (claim sizes) constitute a stationary ergodic stable process. We study how ruin occurs in this situation and evaluate the asymptotic behavior of the ruin

  8. Discriminating Among Probability Weighting Functions Using Adaptive Design Optimization

    Science.gov (United States)

    Cavagnaro, Daniel R.; Pitt, Mark A.; Gonzalez, Richard; Myung, Jay I.

    2014-01-01

    Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within cumulative prospect theory. While several different parametric forms have been proposed, their qualitative similarities make it challenging to discriminate among them empirically. In this paper, we use both simulation and choice experiments to investigate the extent to which different parametric forms of the probability weighting function can be discriminated using adaptive design optimization, a computer-based methodology that identifies and exploits model differences for the purpose of model discrimination. The simulation experiments show that the correct (data-generating) form can be conclusively discriminated from its competitors. The results of an empirical experiment reveal heterogeneity between participants in terms of the functional form, with two models (Prelec-2, Linear in Log Odds) emerging as the most common best-fitting models. The findings shed light on assumptions underlying these models. PMID:24453406

  9. Use of the AIC with the EM algorithm: A demonstration of a probability model selection technique

    Energy Technology Data Exchange (ETDEWEB)

    Glosup, J.G.; Axelrod M.C. [Lawrence Livermore National Lab., CA (United States)

    1994-11-15

    The problem of discriminating between two potential probability models, a Gaussian distribution and a mixture of Gaussian distributions, is considered. The focus of our interest is a case where the models are potentially non-nested and the parameters of the mixture model are estimated through the EM algorithm. The AIC, which is frequently used as a criterion for discriminating between non-nested models, is modified to work with the EM algorithm and is shown to provide a model selection tool for this situation. A particular problem involving an infinite mixture distribution known as Middleton`s Class A model is used to demonstrate the effectiveness and limitations of this method.

  10. Consistent probabilities in loop quantum cosmology

    International Nuclear Information System (INIS)

    Craig, David A; Singh, Parampreet

    2013-01-01

    A fundamental issue for any quantum cosmological theory is to specify how probabilities can be assigned to various quantum events or sequences of events such as the occurrence of singularities or bounces. In previous work, we have demonstrated how this issue can be successfully addressed within the consistent histories approach to quantum theory for Wheeler–DeWitt-quantized cosmological models. In this work, we generalize that analysis to the exactly solvable loop quantization of a spatially flat, homogeneous and isotropic cosmology sourced with a massless, minimally coupled scalar field known as sLQC. We provide an explicit, rigorous and complete decoherent-histories formulation for this model and compute the probabilities for the occurrence of a quantum bounce versus a singularity. Using the scalar field as an emergent internal time, we show for generic states that the probability for a singularity to occur in this model is zero, and that of a bounce is unity, complementing earlier studies of the expectation values of the volume and matter density in this theory. We also show from the consistent histories point of view that all states in this model, whether quantum or classical, achieve arbitrarily large volume in the limit of infinite ‘past’ or ‘future’ scalar ‘time’, in the sense that the wave function evaluated at any arbitrary fixed value of the volume vanishes in that limit. Finally, we briefly discuss certain misconceptions concerning the utility of the consistent histories approach in these models. (paper)

  11. [Application of Bayes Probability Model in Differentiation of Yin and Yang Jaundice Syndromes in Neonates].

    Science.gov (United States)

    Mu, Chun-sun; Zhang, Ping; Kong, Chun-yan; Li, Yang-ning

    2015-09-01

    To study the application of Bayes probability model in differentiating yin and yang jaundice syndromes in neonates. Totally 107 jaundice neonates who admitted to hospital within 10 days after birth were assigned to two groups according to syndrome differentiation, 68 in the yang jaundice syndrome group and 39 in the yin jaundice syndrome group. Data collected for neonates were factors related to jaundice before, during and after birth. Blood routines, liver and renal functions, and myocardial enzymes were tested on the admission day or the next day. Logistic regression model and Bayes discriminating analysis were used to screen factors important for yin and yang jaundice syndrome differentiation. Finally, Bayes probability model for yin and yang jaundice syndromes was established and assessed. Factors important for yin and yang jaundice syndrome differentiation screened by Logistic regression model and Bayes discriminating analysis included mothers' age, mother with gestational diabetes mellitus (GDM), gestational age, asphyxia, or ABO hemolytic diseases, red blood cell distribution width (RDW-SD), platelet-large cell ratio (P-LCR), serum direct bilirubin (DBIL), alkaline phosphatase (ALP), cholinesterase (CHE). Bayes discriminating analysis was performed by SPSS to obtain Bayes discriminant function coefficient. Bayes discriminant function was established according to discriminant function coefficients. Yang jaundice syndrome: y1= -21. 701 +2. 589 x mother's age + 1. 037 x GDM-17. 175 x asphyxia + 13. 876 x gestational age + 6. 303 x ABO hemolytic disease + 2.116 x RDW-SD + 0. 831 x DBIL + 0. 012 x ALP + 1. 697 x LCR + 0. 001 x CHE; Yin jaundice syndrome: y2= -33. 511 + 2.991 x mother's age + 3.960 x GDM-12. 877 x asphyxia + 11. 848 x gestational age + 1. 820 x ABO hemolytic disease +2. 231 x RDW-SD +0. 999 x DBIL +0. 023 x ALP +1. 916 x LCR +0. 002 x CHE. Bayes discriminant function was hypothesis tested and got Wilks' λ =0. 393 (P =0. 000). So Bayes

  12. Fixation Probability in a Two-Locus Model by the Ancestral Recombination–Selection Graph

    Science.gov (United States)

    Lessard, Sabin; Kermany, Amir R.

    2012-01-01

    We use the ancestral influence graph (AIG) for a two-locus, two-allele selection model in the limit of a large population size to obtain an analytic approximation for the probability of ultimate fixation of a single mutant allele A. We assume that this new mutant is introduced at a given locus into a finite population in which a previous mutant allele B is already segregating with a wild type at another linked locus. We deduce that the fixation probability increases as the recombination rate increases if allele A is either in positive epistatic interaction with B and allele B is beneficial or in no epistatic interaction with B and then allele A itself is beneficial. This holds at least as long as the recombination fraction and the selection intensity are small enough and the population size is large enough. In particular this confirms the Hill–Robertson effect, which predicts that recombination renders more likely the ultimate fixation of beneficial mutants at different loci in a population in the presence of random genetic drift even in the absence of epistasis. More importantly, we show that this is true from weak negative epistasis to positive epistasis, at least under weak selection. In the case of deleterious mutants, the fixation probability decreases as the recombination rate increases. This supports Muller’s ratchet mechanism to explain the accumulation of deleterious mutants in a population lacking recombination. PMID:22095080

  13. On the quantification and efficient propagation of imprecise probabilities resulting from small datasets

    Science.gov (United States)

    Zhang, Jiaxin; Shields, Michael D.

    2018-01-01

    This paper addresses the problem of uncertainty quantification and propagation when data for characterizing probability distributions are scarce. We propose a methodology wherein the full uncertainty associated with probability model form and parameter estimation are retained and efficiently propagated. This is achieved by applying the information-theoretic multimodel inference method to identify plausible candidate probability densities and associated probabilities that each method is the best model in the Kullback-Leibler sense. The joint parameter densities for each plausible model are then estimated using Bayes' rule. We then propagate this full set of probability models by estimating an optimal importance sampling density that is representative of all plausible models, propagating this density, and reweighting the samples according to each of the candidate probability models. This is in contrast with conventional methods that try to identify a single probability model that encapsulates the full uncertainty caused by lack of data and consequently underestimate uncertainty. The result is a complete probabilistic description of both aleatory and epistemic uncertainty achieved with several orders of magnitude reduction in computational cost. It is shown how the model can be updated to adaptively accommodate added data and added candidate probability models. The method is applied for uncertainty analysis of plate buckling strength where it is demonstrated how dataset size affects the confidence (or lack thereof) we can place in statistical estimates of response when data are lacking.

  14. Pairwise local structural alignment of RNA sequences with sequence similarity less than 40%

    DEFF Research Database (Denmark)

    Havgaard, Jakob Hull; Lyngsø, Rune B.; Stormo, Gary D.

    2005-01-01

    detect two genes with low sequence similarity, where the genes are part of a larger genomic region. Results: Here we present such an approach for pairwise local alignment which is based on FILDALIGN and the Sankoff algorithm for simultaneous structural alignment of multiple sequences. We include...... the ability to conduct mutual scans of two sequences of arbitrary length while searching for common local structural motifs of some maximum length. This drastically reduces the complexity of the algorithm. The scoring scheme includes structural parameters corresponding to those available for free energy....... The structure prediction performance for a family is typically around 0.7 using Matthews correlation coefficient. In case (2), the algorithm is successful at locating RNA families with an average sensitivity of 0.8 and a positive predictive value of 0.9 using a BLAST-like hit selection scheme. Availability...

  15. Quantum probability measures and tomographic probability densities

    NARCIS (Netherlands)

    Amosov, GG; Man'ko, [No Value

    2004-01-01

    Using a simple relation of the Dirac delta-function to generalized the theta-function, the relationship between the tomographic probability approach and the quantum probability measure approach with the description of quantum states is discussed. The quantum state tomogram expressed in terms of the

  16. Stimulus-dependent maximum entropy models of neural population codes.

    Directory of Open Access Journals (Sweden)

    Einat Granot-Atedgi

    Full Text Available Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME model-a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.

  17. Methods for estimating drought streamflow probabilities for Virginia streams

    Science.gov (United States)

    Austin, Samuel H.

    2014-01-01

    Maximum likelihood logistic regression model equations used to estimate drought flow probabilities for Virginia streams are presented for 259 hydrologic basins in Virginia. Winter streamflows were used to estimate the likelihood of streamflows during the subsequent drought-prone summer months. The maximum likelihood logistic regression models identify probable streamflows from 5 to 8 months in advance. More than 5 million streamflow daily values collected over the period of record (January 1, 1900 through May 16, 2012) were compiled and analyzed over a minimum 10-year (maximum 112-year) period of record. The analysis yielded the 46,704 equations with statistically significant fit statistics and parameter ranges published in two tables in this report. These model equations produce summer month (July, August, and September) drought flow threshold probabilities as a function of streamflows during the previous winter months (November, December, January, and February). Example calculations are provided, demonstrating how to use the equations to estimate probable streamflows as much as 8 months in advance.

  18. Economic choices reveal probability distortion in macaque monkeys.

    Science.gov (United States)

    Stauffer, William R; Lak, Armin; Bossaerts, Peter; Schultz, Wolfram

    2015-02-18

    Economic choices are largely determined by two principal elements, reward value (utility) and probability. Although nonlinear utility functions have been acknowledged for centuries, nonlinear probability weighting (probability distortion) was only recently recognized as a ubiquitous aspect of real-world choice behavior. Even when outcome probabilities are known and acknowledged, human decision makers often overweight low probability outcomes and underweight high probability outcomes. Whereas recent studies measured utility functions and their corresponding neural correlates in monkeys, it is not known whether monkeys distort probability in a manner similar to humans. Therefore, we investigated economic choices in macaque monkeys for evidence of probability distortion. We trained two monkeys to predict reward from probabilistic gambles with constant outcome values (0.5 ml or nothing). The probability of winning was conveyed using explicit visual cues (sector stimuli). Choices between the gambles revealed that the monkeys used the explicit probability information to make meaningful decisions. Using these cues, we measured probability distortion from choices between the gambles and safe rewards. Parametric modeling of the choices revealed classic probability weighting functions with inverted-S shape. Therefore, the animals overweighted low probability rewards and underweighted high probability rewards. Empirical investigation of the behavior verified that the choices were best explained by a combination of nonlinear value and nonlinear probability distortion. Together, these results suggest that probability distortion may reflect evolutionarily preserved neuronal processing. Copyright © 2015 Stauffer et al.

  19. COVAL, Compound Probability Distribution for Function of Probability Distribution

    International Nuclear Information System (INIS)

    Astolfi, M.; Elbaz, J.

    1979-01-01

    1 - Nature of the physical problem solved: Computation of the probability distribution of a function of variables, given the probability distribution of the variables themselves. 'COVAL' has been applied to reliability analysis of a structure subject to random loads. 2 - Method of solution: Numerical transformation of probability distributions

  20. APPROXIMATION OF PROBABILITY DISTRIBUTIONS IN QUEUEING MODELS

    Directory of Open Access Journals (Sweden)

    T. I. Aliev

    2013-03-01

    Full Text Available For probability distributions with variation coefficient, not equal to unity, mathematical dependences for approximating distributions on the basis of first two moments are derived by making use of multi exponential distributions. It is proposed to approximate distributions with coefficient of variation less than unity by using hypoexponential distribution, which makes it possible to generate random variables with coefficient of variation, taking any value in a range (0; 1, as opposed to Erlang distribution, having only discrete values of coefficient of variation.

  1. Learning Preference Models from Data: On the Problem of Label Ranking and Its Variants

    Science.gov (United States)

    Hüllermeier, Eyke; Fürnkranz, Johannes

    The term “preference learning” refers to the application of machine learning methods for inducing preference models from empirical data. In the recent literature, corresponding problems appear in various guises. After a brief overview of the field, this work focuses on a particular learning scenario called label ranking where the problem is to learn a mapping from instances to rankings over a finite number of labels. Our approach for learning such a ranking function, called ranking by pairwise comparison (RPC), first induces a binary preference relation from suitable training data, using a natural extension of pairwise classification. A ranking is then derived from this relation by means of a ranking procedure. This paper elaborates on a key advantage of such an approach, namely the fact that our learner can be adapted to different loss functions by using different ranking procedures on the same underlying order relations. In particular, the Spearman rank correlation is minimized by using a simple weighted voting procedure. Moreover, we discuss a loss function suitable for settings where candidate labels must be tested successively until a target label is found. In this context, we propose the idea of “empirical conditioning” of class probabilities. A related ranking procedure, called “ranking through iterated choice”, is investigated experimentally.

  2. Cladding failure probability modeling for risk evaluations of fast reactors

    International Nuclear Information System (INIS)

    Mueller, C.J.; Kramer, J.M.

    1987-01-01

    This paper develops the methodology to incorporate cladding failure data and associated modeling into risk evaluations of liquid metal-cooled fast reactors (LMRs). Current U.S. innovative designs for metal-fueled pool-type LMRs take advantage of inherent reactivity feedback mechanisms to limit reactor temperature increases in response to classic anticipated-transient-without-scram (ATWS) initiators. Final shutdown without reliance on engineered safety features can then be accomplished if sufficient time is available for operator intervention to terminate fission power production and/or provide auxiliary cooling prior to significant core disruption. Coherent cladding failure under the sustained elevated temperatures of ATWS events serves as one indicator of core disruption. In this paper we combine uncertainties in cladding failure data with uncertainties in calculations of ATWS cladding temperature conditions to calculate probabilities of cladding failure as a function of the time for accident recovery. (orig.)

  3. Investigating the probability of detection of typical cavity shapes through modelling and comparison of geophysical techniques

    Science.gov (United States)

    James, P.

    2011-12-01

    With a growing need for housing in the U.K., the government has proposed increased development of brownfield sites. However, old mine workings and natural cavities represent a potential hazard before, during and after construction on such sites, and add further complication to subsurface parameters. Cavities are hence a limitation to certain redevelopment and their detection is an ever important consideration. The current standard technique for cavity detection is a borehole grid, which is intrusive, non-continuous, slow and expensive. A new robust investigation standard in the detection of cavities is sought and geophysical techniques offer an attractive alternative. Geophysical techniques have previously been utilised successfully in the detection of cavities in various geologies, but still has an uncertain reputation in the engineering industry. Engineers are unsure of the techniques and are inclined to rely on well known techniques than utilise new technologies. Bad experiences with geophysics are commonly due to the indiscriminate choice of particular techniques. It is imperative that a geophysical survey is designed with the specific site and target in mind at all times, and the ability and judgement to rule out some, or all, techniques. To this author's knowledge no comparative software exists to aid technique choice. Also, previous modelling software limit the shapes of bodies and hence typical cavity shapes are not represented. Here, we introduce 3D modelling software (Matlab) which computes and compares the response to various cavity targets from a range of techniques (gravity, gravity gradient, magnetic, magnetic gradient and GPR). Typical near surface cavity shapes are modelled including shafts, bellpits, various lining and capping materials, and migrating voids. The probability of cavity detection is assessed in typical subsurface and noise conditions across a range of survey parameters. Techniques can be compared and the limits of detection distance

  4. How fast can we learn maximum entropy models of neural populations?

    Energy Technology Data Exchange (ETDEWEB)

    Ganmor, Elad; Schneidman, Elad [Department of Neuroscience, Weizmann Institute of Science, Rehovot 76100 (Israel); Segev, Ronen, E-mail: elad.ganmor@weizmann.ac.i, E-mail: elad.schneidman@weizmann.ac.i [Department of Life Sciences and Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Beer-Sheva 84105 (Israel)

    2009-12-01

    Most of our knowledge about how the brain encodes information comes from recordings of single neurons. However, computations in the brain are carried out by large groups of neurons. Modelling the joint activity of many interacting elements is computationally hard because of the large number of possible activity patterns and limited experimental data. Recently it was shown in several different neural systems that maximum entropy pairwise models, which rely only on firing rates and pairwise correlations of neurons, are excellent models for the distribution of activity patterns of neural populations, and in particular, their responses to natural stimuli. Using simultaneous recordings of large groups of neurons in the vertebrate retina responding to naturalistic stimuli, we show here that the relevant statistics required for finding the pairwise model can be accurately estimated within seconds. Furthermore, while higher order statistics may, in theory, improve model accuracy, they are, in practice, harmful for times of up to 20 minutes due to sampling noise. Finally, we demonstrate that trading accuracy for entropy may actually improve model performance when data is limited, and suggest an optimization method that automatically adjusts model constraints in order to achieve good performance.

  5. How fast can we learn maximum entropy models of neural populations?

    International Nuclear Information System (INIS)

    Ganmor, Elad; Schneidman, Elad; Segev, Ronen

    2009-01-01

    Most of our knowledge about how the brain encodes information comes from recordings of single neurons. However, computations in the brain are carried out by large groups of neurons. Modelling the joint activity of many interacting elements is computationally hard because of the large number of possible activity patterns and limited experimental data. Recently it was shown in several different neural systems that maximum entropy pairwise models, which rely only on firing rates and pairwise correlations of neurons, are excellent models for the distribution of activity patterns of neural populations, and in particular, their responses to natural stimuli. Using simultaneous recordings of large groups of neurons in the vertebrate retina responding to naturalistic stimuli, we show here that the relevant statistics required for finding the pairwise model can be accurately estimated within seconds. Furthermore, while higher order statistics may, in theory, improve model accuracy, they are, in practice, harmful for times of up to 20 minutes due to sampling noise. Finally, we demonstrate that trading accuracy for entropy may actually improve model performance when data is limited, and suggest an optimization method that automatically adjusts model constraints in order to achieve good performance.

  6. Improved process for calculating the probability of being hit by crashing aircraft by the Balfanz-model

    International Nuclear Information System (INIS)

    Hennings, W.

    1988-01-01

    For calculating the probability of being hit by crashing military aircraft on different buildings, a model was introduced, which has already been used in the conventional fields. In the context of converting the research reactor BER II, this model was also used in the nuclear field. The report introduces this model and shows the application to a vertical cylinder as an example. Compared to the previous model, an exact and also simpler solution of the model attempt for determining the shade surface for different shapes of buildings is derived. The problems of the distribution of crashes given by the previous model is treated via the vertical angle and an attempt to solve these problems is given. (orig./HP) [de

  7. Quantitative non-monotonic modeling of economic uncertainty by probability and possibility distributions

    DEFF Research Database (Denmark)

    Schjær-Jacobsen, Hans

    2012-01-01

    uncertainty can be calculated. The possibility approach is particular well suited for representation of uncertainty of a non-statistical nature due to lack of knowledge and requires less information than the probability approach. Based on the kind of uncertainty and knowledge present, these aspects...... to the understanding of similarities and differences of the two approaches as well as practical applications. The probability approach offers a good framework for representation of randomness and variability. Once the probability distributions of uncertain parameters and their correlations are known the resulting...... are thoroughly discussed in the case of rectangular representation of uncertainty by the uniform probability distribution and the interval, respectively. Also triangular representations are dealt with and compared. Calculation of monotonic as well as non-monotonic functions of variables represented...

  8. The transition probability and the probability for the left-most particle's position of the q-totally asymmetric zero range process

    Energy Technology Data Exchange (ETDEWEB)

    Korhonen, Marko [Department of Mathematics and Statistics, University of Helsinki, FIN-00014 (Finland); Lee, Eunghyun [Centre de Recherches Mathématiques (CRM), Université de Montréal, Quebec H3C 3J7 (Canada)

    2014-01-15

    We treat the N-particle zero range process whose jumping rates satisfy a certain condition. This condition is required to use the Bethe ansatz and the resulting model is the q-boson model by Sasamoto and Wadati [“Exact results for one-dimensional totally asymmetric diffusion models,” J. Phys. A 31, 6057–6071 (1998)] or the q-totally asymmetric zero range process (TAZRP) by Borodin and Corwin [“Macdonald processes,” Probab. Theory Relat. Fields (to be published)]. We find the explicit formula of the transition probability of the q-TAZRP via the Bethe ansatz. By using the transition probability we find the probability distribution of the left-most particle's position at time t. To find the probability for the left-most particle's position we find a new identity corresponding to identity for the asymmetric simple exclusion process by Tracy and Widom [“Integral formulas for the asymmetric simple exclusion process,” Commun. Math. Phys. 279, 815–844 (2008)]. For the initial state that all particles occupy a single site, the probability distribution of the left-most particle's position at time t is represented by the contour integral of a determinant.

  9. Building a Model Using Bayesian Network for Assessment of Posterior Probabilities of Falling From Height at Workplaces

    Directory of Open Access Journals (Sweden)

    Seyed Shamseddin Alizadeh

    2014-12-01

    Full Text Available Background: Falls from height are one of the main causes of fatal occupational injuries. The objective of this study was to present a model for estimating occurrence probability of falling from height. Methods: In order to make a list of factors affecting falls, we used four expert group's judgment, literature review and an available database. Then the validity and reliability of designed questionnaire were determined and Bayesian networks were built. The built network, nodes and curves were quantified. For network sensitivity analysis, four types of analysis carried out. Results: A Bayesian network for assessment of posterior probabilities of falling from height proposed. The presented Bayesian network model shows the interrelationships among 37 causes affecting the falling from height and can calculate its posterior probabilities. The most important factors affecting falling were Non-compliance with safety instructions for work at height (0.127, Lack of safety equipment for work at height (0.094 and Lack of safety instructions for work at height (0.071 respectively. Conclusion: The proposed Bayesian network used to determine how different causes could affect the falling from height at work. The findings of this study can be used to decide on the falling accident prevention programs.

  10. Joint Probability-Based Neuronal Spike Train Classification

    Directory of Open Access Journals (Sweden)

    Yan Chen

    2009-01-01

    Full Text Available Neuronal spike trains are used by the nervous system to encode and transmit information. Euclidean distance-based methods (EDBMs have been applied to quantify the similarity between temporally-discretized spike trains and model responses. In this study, using the same discretization procedure, we developed and applied a joint probability-based method (JPBM to classify individual spike trains of slowly adapting pulmonary stretch receptors (SARs. The activity of individual SARs was recorded in anaesthetized, paralysed adult male rabbits, which were artificially-ventilated at constant rate and one of three different volumes. Two-thirds of the responses to the 600 stimuli presented at each volume were used to construct three response models (one for each stimulus volume consisting of a series of time bins, each with spike probabilities. The remaining one-third of the responses where used as test responses to be classified into one of the three model responses. This was done by computing the joint probability of observing the same series of events (spikes or no spikes, dictated by the test response in a given model and determining which probability of the three was highest. The JPBM generally produced better classification accuracy than the EDBM, and both performed well above chance. Both methods were similarly affected by variations in discretization parameters, response epoch duration, and two different response alignment strategies. Increasing bin widths increased classification accuracy, which also improved with increased observation time, but primarily during periods of increasing lung inflation. Thus, the JPBM is a simple and effective method performing spike train classification.

  11. Emergent behaviors of the Schrödinger-Lohe model on cooperative-competitive networks

    Science.gov (United States)

    Huh, Hyungjin; Ha, Seung-Yeal; Kim, Dohyun

    2017-12-01

    We present several sufficient frameworks leading to the emergent behaviors of the coupled Schrödinger-Lohe (S-L) model under the same one-body external potential on cooperative-competitive networks. The S-L model was first introduced as a possible phenomenological model exhibiting quantum synchronization and its emergent dynamics on all-to-all cooperative networks has been treated via two distinct approaches, Lyapunov functional approach and the finite-dimensional reduction based on pairwise correlations. In this paper, we further generalize the finite-dimensional dynamical systems approach for pairwise correlation functions on cooperative-competitive networks and provide several sufficient frameworks leading to the collective exponential synchronization. For small systems consisting of three and four quantum subsystem, we also show that the system for pairwise correlations can be reduced to the Lotka-Volterra model with cooperative and competitive interactions, in which lots of interesting dynamical patterns appear, e.g., existence of closed orbits and limit-cycles.

  12. Idealized models of the joint probability distribution of wind speeds

    Science.gov (United States)

    Monahan, Adam H.

    2018-05-01

    The joint probability distribution of wind speeds at two separate locations in space or points in time completely characterizes the statistical dependence of these two quantities, providing more information than linear measures such as correlation. In this study, we consider two models of the joint distribution of wind speeds obtained from idealized models of the dependence structure of the horizontal wind velocity components. The bivariate Rice distribution follows from assuming that the wind components have Gaussian and isotropic fluctuations. The bivariate Weibull distribution arises from power law transformations of wind speeds corresponding to vector components with Gaussian, isotropic, mean-zero variability. Maximum likelihood estimates of these distributions are compared using wind speed data from the mid-troposphere, from different altitudes at the Cabauw tower in the Netherlands, and from scatterometer observations over the sea surface. While the bivariate Rice distribution is more flexible and can represent a broader class of dependence structures, the bivariate Weibull distribution is mathematically simpler and may be more convenient in many applications. The complexity of the mathematical expressions obtained for the joint distributions suggests that the development of explicit functional forms for multivariate speed distributions from distributions of the components will not be practical for more complicated dependence structure or more than two speed variables.

  13. Reliability of structures by using probability and fatigue theories

    International Nuclear Information System (INIS)

    Lee, Ouk Sub; Kim, Dong Hyeok; Park, Yeon Chang

    2008-01-01

    Methodologies to calculate failure probability and to estimate the reliability of fatigue loaded structures are developed. The applicability of the methodologies is evaluated with the help of the fatigue crack growth models suggested by Paris and Walker. The probability theories such as the FORM (first order reliability method), the SORM (second order reliability method) and the MCS (Monte Carlo simulation) are utilized. It is found that the failure probability decreases with the increase of the design fatigue life and the applied minimum stress, the decrease of the initial edge crack size, the applied maximum stress and the slope of Paris equation. Furthermore, according to the sensitivity analysis of random variables, the slope of Pairs equation affects the failure probability dominantly among other random variables in the Paris and the Walker models

  14. Probability of identification: a statistical model for the validation of qualitative botanical identification methods.

    Science.gov (United States)

    LaBudde, Robert A; Harnly, James M

    2012-01-01

    A qualitative botanical identification method (BIM) is an analytical procedure that returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) material, or whether it contains excessive nontarget (undesirable) material. The report describes the development and validation of studies for a BIM based on the proportion of replicates identified, or probability of identification (POI), as the basic observed statistic. The statistical procedures proposed for data analysis follow closely those of the probability of detection, and harmonize the statistical concepts and parameters between quantitative and qualitative method validation. Use of POI statistics also harmonizes statistical concepts for botanical, microbiological, toxin, and other analyte identification methods that produce binary results. The POI statistical model provides a tool for graphical representation of response curves for qualitative methods, reporting of descriptive statistics, and application of performance requirements. Single collaborator and multicollaborative study examples are given.

  15. Development of a statistical model for the determination of the probability of riverbank erosion in a Meditteranean river basin

    Science.gov (United States)

    Varouchakis, Emmanouil; Kourgialas, Nektarios; Karatzas, George; Giannakis, Georgios; Lilli, Maria; Nikolaidis, Nikolaos

    2014-05-01

    Riverbank erosion affects the river morphology and the local habitat and results in riparian land loss, damage to property and infrastructures, ultimately weakening flood defences. An important issue concerning riverbank erosion is the identification of the areas vulnerable to erosion, as it allows for predicting changes and assists with stream management and restoration. One way to predict the vulnerable to erosion areas is to determine the erosion probability by identifying the underlying relations between riverbank erosion and the geomorphological and/or hydrological variables that prevent or stimulate erosion. A statistical model for evaluating the probability of erosion based on a series of independent local variables and by using logistic regression is developed in this work. The main variables affecting erosion are vegetation index (stability), the presence or absence of meanders, bank material (classification), stream power, bank height, river bank slope, riverbed slope, cross section width and water velocities (Luppi et al. 2009). In statistics, logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable, e.g. binary response, based on one or more predictor variables (continuous or categorical). The probabilities of the possible outcomes are modelled as a function of independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. 1 = "presence of erosion" and 0 = "no erosion") for any value of the independent variables. The regression coefficients are estimated by using maximum likelihood estimation. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding

  16. DECOFF Probabilities of Failed Operations

    DEFF Research Database (Denmark)

    Gintautas, Tomas

    2015-01-01

    A statistical procedure of estimation of Probabilities of Failed Operations is described and exemplified using ECMWF weather forecasts and SIMO output from Rotor Lift test case models. Also safety factor influence is investigated. DECOFF statistical method is benchmarked against standard Alpha-factor...

  17. Single, Complete, Probability Spaces Consistent With EPR-Bohm-Bell Experimental Data

    Science.gov (United States)

    Avis, David; Fischer, Paul; Hilbert, Astrid; Khrennikov, Andrei

    2009-03-01

    We show that paradoxical consequences of violations of Bell's inequality are induced by the use of an unsuitable probabilistic description for the EPR-Bohm-Bell experiment. The conventional description (due to Bell) is based on a combination of statistical data collected for different settings of polarization beam splitters (PBSs). In fact, such data consists of some conditional probabilities which only partially define a probability space. Ignoring this conditioning leads to apparent contradictions in the classical probabilistic model (due to Kolmogorov). We show how to make a completely consistent probabilistic model by taking into account the probabilities of selecting the settings of the PBSs. Our model matches both the experimental data and is consistent with classical probability theory.

  18. Screening synteny blocks in pairwise genome comparisons through integer programming.

    Science.gov (United States)

    Tang, Haibao; Lyons, Eric; Pedersen, Brent; Schnable, James C; Paterson, Andrew H; Freeling, Michael

    2011-04-18

    It is difficult to accurately interpret chromosomal correspondences such as true orthology and paralogy due to significant divergence of genomes from a common ancestor. Analyses are particularly problematic among lineages that have repeatedly experienced whole genome duplication (WGD) events. To compare multiple "subgenomes" derived from genome duplications, we need to relax the traditional requirements of "one-to-one" syntenic matchings of genomic regions in order to reflect "one-to-many" or more generally "many-to-many" matchings. However this relaxation may result in the identification of synteny blocks that are derived from ancient shared WGDs that are not of interest. For many downstream analyses, we need to eliminate weak, low scoring alignments from pairwise genome comparisons. Our goal is to objectively select subset of synteny blocks whose total scores are maximized while respecting the duplication history of the genomes in comparison. We call this "quota-based" screening of synteny blocks in order to appropriately fill a quota of syntenic relationships within one genome or between two genomes having WGD events. We have formulated the synteny block screening as an optimization problem known as "Binary Integer Programming" (BIP), which is solved using existing linear programming solvers. The computer program QUOTA-ALIGN performs this task by creating a clear objective function that maximizes the compatible set of synteny blocks under given constraints on overlaps and depths (corresponding to the duplication history in respective genomes). Such a procedure is useful for any pairwise synteny alignments, but is most useful in lineages affected by multiple WGDs, like plants or fish lineages. For example, there should be a 1:2 ploidy relationship between genome A and B if genome B had an independent WGD subsequent to the divergence of the two genomes. We show through simulations and real examples using plant genomes in the rosid superorder that the quota

  19. Sensitivity of probability-of-failure estimates with respect to probability of detection curve parameters

    Energy Technology Data Exchange (ETDEWEB)

    Garza, J. [University of Texas at San Antonio, Mechanical Engineering, 1 UTSA circle, EB 3.04.50, San Antonio, TX 78249 (United States); Millwater, H., E-mail: harry.millwater@utsa.edu [University of Texas at San Antonio, Mechanical Engineering, 1 UTSA circle, EB 3.04.50, San Antonio, TX 78249 (United States)

    2012-04-15

    A methodology has been developed and demonstrated that can be used to compute the sensitivity of the probability-of-failure (POF) with respect to the parameters of inspection processes that are simulated using probability of detection (POD) curves. The formulation is such that the probabilistic sensitivities can be obtained at negligible cost using sampling methods by reusing the samples used to compute the POF. As a result, the methodology can be implemented for negligible cost in a post-processing non-intrusive manner thereby facilitating implementation with existing or commercial codes. The formulation is generic and not limited to any specific random variables, fracture mechanics formulation, or any specific POD curve as long as the POD is modeled parametrically. Sensitivity estimates for the cases of different POD curves at multiple inspections, and the same POD curves at multiple inspections have been derived. Several numerical examples are presented and show excellent agreement with finite difference estimates with significant computational savings. - Highlights: Black-Right-Pointing-Pointer Sensitivity of the probability-of-failure with respect to the probability-of-detection curve. Black-Right-Pointing-Pointer The sensitivities are computed with negligible cost using Monte Carlo sampling. Black-Right-Pointing-Pointer The change in the POF due to a change in the POD curve parameters can be easily estimated.

  20. Sensitivity of probability-of-failure estimates with respect to probability of detection curve parameters

    International Nuclear Information System (INIS)

    Garza, J.; Millwater, H.

    2012-01-01

    A methodology has been developed and demonstrated that can be used to compute the sensitivity of the probability-of-failure (POF) with respect to the parameters of inspection processes that are simulated using probability of detection (POD) curves. The formulation is such that the probabilistic sensitivities can be obtained at negligible cost using sampling methods by reusing the samples used to compute the POF. As a result, the methodology can be implemented for negligible cost in a post-processing non-intrusive manner thereby facilitating implementation with existing or commercial codes. The formulation is generic and not limited to any specific random variables, fracture mechanics formulation, or any specific POD curve as long as the POD is modeled parametrically. Sensitivity estimates for the cases of different POD curves at multiple inspections, and the same POD curves at multiple inspections have been derived. Several numerical examples are presented and show excellent agreement with finite difference estimates with significant computational savings. - Highlights: ► Sensitivity of the probability-of-failure with respect to the probability-of-detection curve. ►The sensitivities are computed with negligible cost using Monte Carlo sampling. ► The change in the POF due to a change in the POD curve parameters can be easily estimated.

  1. Probability, statistics, and computational science.

    Science.gov (United States)

    Beerenwinkel, Niko; Siebourg, Juliane

    2012-01-01

    In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

  2. Quantum probabilities as Dempster-Shafer probabilities in the lattice of subspaces

    International Nuclear Information System (INIS)

    Vourdas, A.

    2014-01-01

    The orthocomplemented modular lattice of subspaces L[H(d)], of a quantum system with d-dimensional Hilbert space H(d), is considered. A generalized additivity relation which holds for Kolmogorov probabilities is violated by quantum probabilities in the full lattice L[H(d)] (it is only valid within the Boolean subalgebras of L[H(d)]). This suggests the use of more general (than Kolmogorov) probability theories, and here the Dempster-Shafer probability theory is adopted. An operator D(H 1 ,H 2 ), which quantifies deviations from Kolmogorov probability theory is introduced, and it is shown to be intimately related to the commutator of the projectors P(H 1 ),P(H 2 ), to the subspaces H 1 , H 2 . As an application, it is shown that the proof of the inequalities of Clauser, Horne, Shimony, and Holt for a system of two spin 1/2 particles is valid for Kolmogorov probabilities, but it is not valid for Dempster-Shafer probabilities. The violation of these inequalities in experiments supports the interpretation of quantum probabilities as Dempster-Shafer probabilities

  3. Prediction of radiation-induced liver disease by Lyman normal-tissue complication probability model in three-dimensional conformal radiation therapy for primary liver carcinoma

    International Nuclear Information System (INIS)

    Xu ZhiYong; Liang Shixiong; Zhu Ji; Zhu Xiaodong; Zhao Jiandong; Lu Haijie; Yang Yunli; Chen Long; Wang Anyu; Fu Xiaolong; Jiang Guoliang

    2006-01-01

    Purpose: To describe the probability of RILD by application of the Lyman-Kutcher-Burman normal-tissue complication (NTCP) model for primary liver carcinoma (PLC) treated with hypofractionated three-dimensional conformal radiotherapy (3D-CRT). Methods and Materials: A total of 109 PLC patients treated by 3D-CRT were followed for RILD. Of these patients, 93 were in liver cirrhosis of Child-Pugh Grade A, and 16 were in Child-Pugh Grade B. The Michigan NTCP model was used to predict the probability of RILD, and then the modified Lyman NTCP model was generated for Child-Pugh A and Child-Pugh B patients by maximum-likelihood analysis. Results: Of all patients, 17 developed RILD in which 8 were of Child-Pugh Grade A, and 9 were of Child-Pugh Grade B. The prediction of RILD by the Michigan model was underestimated for PLC patients. The modified n, m, TD 5 (1) were 1.1, 0.28, and 40.5 Gy and 0.7, 0.43, and 23 Gy for patients with Child-Pugh A and B, respectively, which yielded better estimations of RILD probability. The hepatic tolerable doses (TD 5 ) would be MDTNL of 21 Gy and 6 Gy, respectively, for Child-Pugh A and B patients. Conclusions: The Michigan model was probably not fit to predict RILD in PLC patients. A modified Lyman NTCP model for RILD was recommended

  4. Conditional probability of the tornado missile impact given a tornado occurrence

    International Nuclear Information System (INIS)

    Goodman, J.; Koch, J.E.

    1982-01-01

    Using an approach based on statistical mechanics, an expression for the probability of the first missile strike is developed. The expression depends on two generic parameters (injection probability eta(F) and height distribution psi(Z,F)), which are developed in this study, and one plant specific parameter (number of potential missiles N/sub p/). The expression for the joint probability of simultaneous impact of muitiple targets is also developed. This espression is applicable to calculation of the probability of common cause failure due to tornado missiles. It is shown that the probability of the first missile strike can be determined using a uniform missile distribution model. It is also shown that the conditional probability of the second strike, given the first, is underestimated by the uniform model. The probability of the second strike is greatly increased if the missiles are in clusters large enough to cover both targets

  5. Repopulation of interacting tumor cells during fractionated radiotherapy: Stochastic modeling of the tumor control probability

    International Nuclear Information System (INIS)

    Fakir, Hatim; Hlatky, Lynn; Li, Huamin; Sachs, Rainer

    2013-01-01

    Purpose: Optimal treatment planning for fractionated external beam radiation therapy requires inputs from radiobiology based on recent thinking about the “five Rs” (repopulation, radiosensitivity, reoxygenation, redistribution, and repair). The need is especially acute for the newer, often individualized, protocols made feasible by progress in image guided radiation therapy and dose conformity. Current stochastic tumor control probability (TCP) models incorporating tumor repopulation effects consider “stem-like cancer cells” (SLCC) to be independent, but the authors here propose that SLCC-SLCC interactions may be significant. The authors present a new stochastic TCP model for repopulating SLCC interacting within microenvironmental niches. Our approach is meant mainly for comparing similar protocols. It aims at practical generalizations of previous mathematical models. Methods: The authors consider protocols with complete sublethal damage repair between fractions. The authors use customized open-source software and recent mathematical approaches from stochastic process theory for calculating the time-dependent SLCC number and thereby estimating SLCC eradication probabilities. As specific numerical examples, the authors consider predicted TCP results for a 2 Gy per fraction, 60 Gy protocol compared to 64 Gy protocols involving early or late boosts in a limited volume to some fractions. Results: In sample calculations with linear quadratic parameters α = 0.3 per Gy, α/β = 10 Gy, boosting is predicted to raise TCP from a dismal 14.5% observed in some older protocols for advanced NSCLC to above 70%. This prediction is robust as regards: (a) the assumed values of parameters other than α and (b) the choice of models for intraniche SLCC-SLCC interactions. However, α = 0.03 per Gy leads to a prediction of almost no improvement when boosting. Conclusions: The predicted efficacy of moderate boosts depends sensitively on α. Presumably, the larger values of α are

  6. Repopulation of interacting tumor cells during fractionated radiotherapy: stochastic modeling of the tumor control probability.

    Science.gov (United States)

    Fakir, Hatim; Hlatky, Lynn; Li, Huamin; Sachs, Rainer

    2013-12-01

    Optimal treatment planning for fractionated external beam radiation therapy requires inputs from radiobiology based on recent thinking about the "five Rs" (repopulation, radiosensitivity, reoxygenation, redistribution, and repair). The need is especially acute for the newer, often individualized, protocols made feasible by progress in image guided radiation therapy and dose conformity. Current stochastic tumor control probability (TCP) models incorporating tumor repopulation effects consider "stem-like cancer cells" (SLCC) to be independent, but the authors here propose that SLCC-SLCC interactions may be significant. The authors present a new stochastic TCP model for repopulating SLCC interacting within microenvironmental niches. Our approach is meant mainly for comparing similar protocols. It aims at practical generalizations of previous mathematical models. The authors consider protocols with complete sublethal damage repair between fractions. The authors use customized open-source software and recent mathematical approaches from stochastic process theory for calculating the time-dependent SLCC number and thereby estimating SLCC eradication probabilities. As specific numerical examples, the authors consider predicted TCP results for a 2 Gy per fraction, 60 Gy protocol compared to 64 Gy protocols involving early or late boosts in a limited volume to some fractions. In sample calculations with linear quadratic parameters α = 0.3 per Gy, α∕β = 10 Gy, boosting is predicted to raise TCP from a dismal 14.5% observed in some older protocols for advanced NSCLC to above 70%. This prediction is robust as regards: (a) the assumed values of parameters other than α and (b) the choice of models for intraniche SLCC-SLCC interactions. However, α = 0.03 per Gy leads to a prediction of almost no improvement when boosting. The predicted efficacy of moderate boosts depends sensitively on α. Presumably, the larger values of α are the ones appropriate for individualized

  7. Nash equilibrium with lower probabilities

    DEFF Research Database (Denmark)

    Groes, Ebbe; Jacobsen, Hans Jørgen; Sloth, Birgitte

    1998-01-01

    We generalize the concept of Nash equilibrium in mixed strategies for strategic form games to allow for ambiguity in the players' expectations. In contrast to other contributions, we model ambiguity by means of so-called lower probability measures or belief functions, which makes it possible...

  8. Probabilistic sensitivity analysis on Markov models with uncertain transition probabilities: an application in evaluating treatment decisions for type 2 diabetes.

    Science.gov (United States)

    Zhang, Yuanhui; Wu, Haipeng; Denton, Brian T; Wilson, James R; Lobo, Jennifer M

    2017-10-27

    Markov models are commonly used for decision-making studies in many application domains; however, there are no widely adopted methods for performing sensitivity analysis on such models with uncertain transition probability matrices (TPMs). This article describes two simulation-based approaches for conducting probabilistic sensitivity analysis on a given discrete-time, finite-horizon, finite-state Markov model using TPMs that are sampled over a specified uncertainty set according to a relevant probability distribution. The first approach assumes no prior knowledge of the probability distribution, and each row of a TPM is independently sampled from the uniform distribution on the row's uncertainty set. The second approach involves random sampling from the (truncated) multivariate normal distribution of the TPM's maximum likelihood estimators for its rows subject to the condition that each row has nonnegative elements and sums to one. The two sampling methods are easily implemented and have reasonable computation times. A case study illustrates the application of these methods to a medical decision-making problem involving the evaluation of treatment guidelines for glycemic control of patients with type 2 diabetes, where natural variation in a patient's glycated hemoglobin (HbA1c) is modeled as a Markov chain, and the associated TPMs are subject to uncertainty.

  9. Modelling soft error probability in firmware: A case study

    African Journals Online (AJOL)

    The purpose is to estimate the probability that external disruptive events (such as ..... also changed the 16-bit magic variable to its unique 'magic' value. .... is mutually independent, not only over registers but over spikes, such that the above.

  10. PROBABILITY SURVEYS , CONDITIONAL PROBABILITIES AND ECOLOGICAL RISK ASSESSMENT

    Science.gov (United States)

    We show that probability-based environmental resource monitoring programs, such as the U.S. Environmental Protection Agency's (U.S. EPA) Environmental Monitoring and Assessment Program, and conditional probability analysis can serve as a basis for estimating ecological risk over ...

  11. Numerical modelling of local deposition patients, activity distributions and cellular hit probabilities of inhaled radon progenies in human airways

    International Nuclear Information System (INIS)

    Farkas, A.; Balashazy, I.; Szoeke, I.

    2003-01-01

    The general objective of our research is modelling the biophysical processes of the effects of inhaled radon progenies. This effort is related to the rejection or support of the linear no threshold (LNT) dose-effect hypothesis, which seems to be one of the most challenging tasks of current radiation protection. Our approximation and results may also serve as a useful tool for lung cancer models. In this study, deposition patterns, activity distributions and alpha-hit probabilities of inhaled radon progenies in the large airways of the human tracheobronchial tree are computed. The airflow fields and related particle deposition patterns strongly depend on the shape of airway geometry and breathing pattern. Computed deposition patterns of attached an unattached radon progenies are strongly inhomogeneous creating hot spots at the carinal regions and downstream of the inner sides of the daughter airways. The results suggest that in the vicinity of the carinal regions the multiple hit probabilities are quite high even at low average doses and increase exponentially in the low-dose range. Thus, even the so-called low doses may present high doses for large clusters of cells. The cell transformation probabilities are much higher in these regions and this phenomenon cannot be modeled with average burdens. (authors)

  12. Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

    Science.gov (United States)

    Calvert, Carol Elaine

    2014-01-01

    This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…

  13. A GRASS GIS Semi-Stochastic Model for Evaluating the Probability of Landslides Impacting Road Networks in Collazzone, Central Italy

    Science.gov (United States)

    Taylor, Faith E.; Santangelo, Michele; Marchesini, Ivan; Malamud, Bruce D.

    2013-04-01

    During a landslide triggering event, the tens to thousands of landslides resulting from the trigger (e.g., earthquake, heavy rainfall) may block a number of sections of the road network, posing a risk to rescue efforts, logistics and accessibility to a region. Here, we present initial results from a semi-stochastic model we are developing to evaluate the probability of landslides intersecting a road network and the network-accessibility implications of this across a region. This was performed in the open source GRASS GIS software, where we took 'model' landslides and dropped them on a 79 km2 test area region in Collazzone, Umbria, Central Italy, with a given road network (major and minor roads, 404 km in length) and already determined landslide susceptibilities. Landslide areas (AL) were randomly selected from a three-parameter inverse gamma probability density function, consisting of a power-law decay of about -2.4 for medium and large values of AL and an exponential rollover for small values of AL; the rollover (maximum probability) occurs at about AL = 400 m.2 The number of landslide areas selected for each triggered event iteration was chosen to have an average density of 1 landslide km-2, i.e. 79 landslide areas chosen randomly for each iteration. Landslides were then 'dropped' over the region semi-stochastically: (i) random points were generated across the study region; (ii) based on the landslide susceptibility map, points were accepted/rejected based on the probability of a landslide occurring at that location. After a point was accepted, it was assigned a landslide area (AL) and length to width ratio. Landslide intersections with roads were then assessed and indices such as the location, number and size of road blockage recorded. The GRASS-GIS model was performed 1000 times in a Monte-Carlo type simulation. Initial results show that for a landslide triggering event of 1 landslide km-2 over a 79 km2 region with 404 km of road, the number of road blockages

  14. Pointwise probability reinforcements for robust statistical inference.

    Science.gov (United States)

    Frénay, Benoît; Verleysen, Michel

    2014-02-01

    Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation. Copyright © 2013 Elsevier Ltd. All rights reserved.

  15. Fast pairwise structural RNA alignments by pruning of the dynamical programming matrix.

    Directory of Open Access Journals (Sweden)

    Jakob H Havgaard

    2007-10-01

    Full Text Available It has become clear that noncoding RNAs (ncRNA play important roles in cells, and emerging studies indicate that there might be a large number of unknown ncRNAs in mammalian genomes. There exist computational methods that can be used to search for ncRNAs by comparing sequences from different genomes. One main problem with these methods is their computational complexity, and heuristics are therefore employed. Two heuristics are currently very popular: pre-folding and pre-aligning. However, these heuristics are not ideal, as pre-aligning is dependent on sequence similarity that may not be present and pre-folding ignores the comparative information. Here, pruning of the dynamical programming matrix is presented as an alternative novel heuristic constraint. All subalignments that do not exceed a length-dependent minimum score are discarded as the matrix is filled out, thus giving the advantage of providing the constraints dynamically. This has been included in a new implementation of the FOLDALIGN algorithm for pairwise local or global structural alignment of RNA sequences. It is shown that time and memory requirements are dramatically lowered while overall performance is maintained. Furthermore, a new divide and conquer method is introduced to limit the memory requirement during global alignment and backtrack of local alignment. All branch points in the computed RNA structure are found and used to divide the structure into smaller unbranched segments. Each segment is then realigned and backtracked in a normal fashion. Finally, the FOLDALIGN algorithm has also been updated with a better memory implementation and an improved energy model. With these improvements in the algorithm, the FOLDALIGN software package provides the molecular biologist with an efficient and user-friendly tool for searching for new ncRNAs. The software package is available for download at http://foldalign.ku.dk.

  16. Probabilities and energies to obtain the counting efficiency of electron-capture nuclides, KLMN model

    International Nuclear Information System (INIS)

    Casas Galiano, G.; Grau Malonda, A.

    1994-01-01

    An intelligent computer program has been developed to obtain the mathematical formulae to compute the probabilities and reduced energies of the different atomic rearrangement pathways following electron-capture decay. Creation and annihilation operators for Auger and X processes have been introduced. Taking into account the symmetries associated with each process, 262 different pathways were obtained. This model allows us to obtain the influence of the M-electron-capture in the counting efficiency when the atomic number of the nuclide is high

  17. Probabilities and energies to obtain the counting efficiency of electron-capture nuclides. KLMN model

    International Nuclear Information System (INIS)

    Galiano, G.; Grau, A.

    1994-01-01

    An intelligent computer program has been developed to obtain the mathematical formulae to compute the probabilities and reduced energies of the different atomic rearrangement pathways following electron-capture decay. Creation and annihilation operators for Auger and X processes have been introduced. Taking into account the symmetries associated with each process, 262 different pathways were obtained. This model allows us to obtain the influence of the M-electro capture in the counting efficiency when the atomic number of the nuclide is high. (Author)

  18. Optimizing an objective function under a bivariate probability model

    NARCIS (Netherlands)

    X. Brusset; N.M. Temme (Nico)

    2007-01-01

    htmlabstractThe motivation of this paper is to obtain an analytical closed form of a quadratic objective function arising from a stochastic decision process with bivariate exponential probability distribution functions that may be dependent. This method is applicable when results need to be

  19. Closed Form Aliasing Probability For Q-ary Symmetric Errors

    Directory of Open Access Journals (Sweden)

    Geetani Edirisooriya

    1996-01-01

    Full Text Available In Built-In Self-Test (BIST techniques, test data reduction can be achieved using Linear Feedback Shift Registers (LFSRs. A faulty circuit may escape detection due to loss of information inherent to data compaction schemes. This is referred to as aliasing. The probability of aliasing in Multiple-Input Shift-Registers (MISRs has been studied under various bit error models. By modeling the signature analyzer as a Markov process we show that the closed form expression derived for aliasing probability previously, for MISRs with primitive polynomials under q-ary symmetric error model holds for all MISRs irrespective of their feedback polynomials and for group cellular automata signature analyzers as well. If the erroneous behaviour of a circuit can be modelled with q-ary symmetric errors, then the test circuit complexity and propagation delay associated with the signature analyzer can be minimized by using a set of m single bit LFSRs without increasing the probability of aliasing.

  20. Naive Probability: Model-based Estimates of Unique Events

    Science.gov (United States)

    2014-05-04

    of inference. Argument and Computation, 1–17, iFirst. Khemlani, S., & Johnson-Laird, P.N. (2012b). Theories of the syllogism: A meta -analysis...is the probability that… 1 space tourism will achieve widespread popularity in the next 50 years? advances in material science will lead to the... governments dedicate more resources to contacting extra-terrestrials? 8 the United States adopts an open border policy of universal acceptance? English is

  1. Searching for collective behavior in a large network of sensory neurons.

    Directory of Open Access Journals (Sweden)

    Gašper Tkačik

    2014-01-01

    Full Text Available Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1 estimating its entropy, which constrains the population's capacity to represent visual information; 2 classifying activity patterns into a small set of metastable collective modes; 3 showing that the neural codeword ensembles are extremely inhomogenous; 4 demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.

  2. Effects of Potential Lane-Changing Probability on Uniform Flow

    International Nuclear Information System (INIS)

    Tang Tieqiao; Huang Haijun; Shang Huayan

    2010-01-01

    In this paper, we use the car-following model with the anticipation effect of the potential lane-changing probability (Acta Mech. Sin. 24 (2008) 399) to investigate the effects of the potential lane-changing probability on uniform flow. The analytical and numerical results show that the potential lane-changing probability can enhance the speed and flow of uniform flow and that their increments are related to the density.

  3. Probability an introduction

    CERN Document Server

    Goldberg, Samuel

    1960-01-01

    Excellent basic text covers set theory, probability theory for finite sample spaces, binomial theorem, probability distributions, means, standard deviations, probability function of binomial distribution, more. Includes 360 problems with answers for half.

  4. Risk Probability Estimating Based on Clustering

    DEFF Research Database (Denmark)

    Chen, Yong; Jensen, Christian D.; Gray, Elizabeth

    2003-01-01

    of prior experiences, recommendations from a trusted entity or the reputation of the other entity. In this paper we propose a dynamic mechanism for estimating the risk probability of a certain interaction in a given environment using hybrid neural networks. We argue that traditional risk assessment models...... from the insurance industry do not directly apply to ubiquitous computing environments. Instead, we propose a dynamic mechanism for risk assessment, which is based on pattern matching, classification and prediction procedures. This mechanism uses an estimator of risk probability, which is based...

  5. Probability of misclassifying biological elements in surface waters.

    Science.gov (United States)

    Loga, Małgorzata; Wierzchołowska-Dziedzic, Anna

    2017-11-24

    Measurement uncertainties are inherent to assessment of biological indices of water bodies. The effect of these uncertainties on the probability of misclassification of ecological status is the subject of this paper. Four Monte-Carlo (M-C) models were applied to simulate the occurrence of random errors in the measurements of metrics corresponding to four biological elements of surface waters: macrophytes, phytoplankton, phytobenthos, and benthic macroinvertebrates. Long series of error-prone measurement values of these metrics, generated by M-C models, were used to identify cases in which values of any of the four biological indices lay outside of the "true" water body class, i.e., outside the class assigned from the actual physical measurements. Fraction of such cases in the M-C generated series was used to estimate the probability of misclassification. The method is particularly useful for estimating the probability of misclassification of the ecological status of surface water bodies in the case of short sequences of measurements of biological indices. The results of the Monte-Carlo simulations show a relatively high sensitivity of this probability to measurement errors of the river macrophyte index (MIR) and high robustness to measurement errors of the benthic macroinvertebrate index (MMI). The proposed method of using Monte-Carlo models to estimate the probability of misclassification has significant potential for assessing the uncertainty of water body status reported to the EC by the EU member countries according to WFD. The method can be readily applied also in risk assessment of water management decisions before adopting the status dependent corrective actions.

  6. pyRMSD: a Python package for efficient pairwise RMSD matrix calculation and handling.

    Science.gov (United States)

    Gil, Víctor A; Guallar, Víctor

    2013-09-15

    We introduce pyRMSD, an open source standalone Python package that aims at offering an integrative and efficient way of performing Root Mean Square Deviation (RMSD)-related calculations of large sets of structures. It is specially tuned to do fast collective RMSD calculations, as pairwise RMSD matrices, implementing up to three well-known superposition algorithms. pyRMSD provides its own symmetric distance matrix class that, besides the fact that it can be used as a regular matrix, helps to save memory and increases memory access speed. This last feature can dramatically improve the overall performance of any Python algorithm using it. In addition, its extensibility, testing suites and documentation make it a good choice to those in need of a workbench for developing or testing new algorithms. The source code (under MIT license), installer, test suites and benchmarks can be found at https://pele.bsc.es/ under the tools section. victor.guallar@bsc.es Supplementary data are available at Bioinformatics online.

  7. The pairwise phase consistency in cortical network and its relationship with neuronal activation

    Directory of Open Access Journals (Sweden)

    Wang Daming

    2017-01-01

    Full Text Available Gamma-band neuronal oscillation and synchronization with the range of 30-90 Hz are ubiquitous phenomenon across numerous brain areas and various species, and correlated with plenty of cognitive functions. The phase of the oscillation, as one aspect of CTC (Communication through Coherence hypothesis, underlies various functions for feature coding, memory processing and behaviour performing. The PPC (Pairwise Phase Consistency, an improved coherence measure, statistically quantifies the strength of phase synchronization. In order to evaluate the PPC and its relationships with input stimulus, neuronal activation and firing rate, a simplified spiking neuronal network is constructed to simulate orientation columns in primary visual cortex. If the input orientation stimulus is preferred for a certain orientation column, neurons within this corresponding column will obtain higher firing rate and stronger neuronal activation, which consequently engender higher PPC values, with higher PPC corresponding to higher firing rate. In addition, we investigate the PPC in time resolved analysis with a sliding window.

  8. Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes.

    Science.gov (United States)

    Pampouchidou, Anastasia; Marias, Kostas; Tsiknakis, Manolis; Simos, Panagiotis; Fan Yang; Lemaitre, Guillaume; Meriaudeau, Fabrice

    2016-08-01

    Depression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns. The main advantage of these methods is that they have significantly lower computational requirements, as the extracted features are of very low dimensionality. This is achieved by modifying the previously proposed algorithm which considers Three-Orthogonal-Planes, to only Pairwise-Orthogonal-Planes. Performance of the algorithms was tested on the benchmark dataset provided by the Audio/Visual Emotion Challenge 2014, with the person-specific system achieving 97.6% classification accuracy, and the person-independed one yielding promising preliminary results of 74.5% accuracy. The paper concludes with open issues, proposed solutions, and future plans.

  9. Normal tissue complication probabilities: dependence on choice of biological model and dose-volume histogram reduction scheme

    International Nuclear Information System (INIS)

    Moiseenko, Vitali; Battista, Jerry; Van Dyk, Jake

    2000-01-01

    Purpose: To evaluate the impact of dose-volume histogram (DVH) reduction schemes and models of normal tissue complication probability (NTCP) on ranking of radiation treatment plans. Methods and Materials: Data for liver complications in humans and for spinal cord in rats were used to derive input parameters of four different NTCP models. DVH reduction was performed using two schemes: 'effective volume' and 'preferred Lyman'. DVHs for competing treatment plans were derived from a sample DVH by varying dose uniformity in a high dose region so that the obtained cumulative DVHs intersected. Treatment plans were ranked according to the calculated NTCP values. Results: Whenever the preferred Lyman scheme was used to reduce the DVH, competing plans were indistinguishable as long as the mean dose was constant. The effective volume DVH reduction scheme did allow us to distinguish between these competing treatment plans. However, plan ranking depended on the radiobiological model used and its input parameters. Conclusions: Dose escalation will be a significant part of radiation treatment planning using new technologies, such as 3-D conformal radiotherapy and tomotherapy. Such dose escalation will depend on how the dose distributions in organs at risk are interpreted in terms of expected complication probabilities. The present study indicates considerable variability in predicted NTCP values because of the methods used for DVH reduction and radiobiological models and their input parameters. Animal studies and collection of standardized clinical data are needed to ascertain the effects of non-uniform dose distributions and to test the validity of the models currently in use

  10. Probabilistic models for neural populations that naturally capture global coupling and criticality.

    Science.gov (United States)

    Humplik, Jan; Tkačik, Gašper

    2017-09-01

    Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system's state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.

  11. Probability 1/e

    Science.gov (United States)

    Koo, Reginald; Jones, Martin L.

    2011-01-01

    Quite a number of interesting problems in probability feature an event with probability equal to 1/e. This article discusses three such problems and attempts to explain why this probability occurs with such frequency.

  12. Energies and transition probabilities from the full solution of nuclear quadrupole-octupole model

    International Nuclear Information System (INIS)

    Strecker, M.; Lenske, H.; Minkov, N.

    2013-01-01

    A collective model of nuclear quadrupole-octupole vibrations and rotations, originally restricted to a coherent interplay between quadrupole and octupole modes, is now developed for application beyond this restriction. The eigenvalue problem is solved by diagonalizing the unrestricted Hamiltonian in the basis of the analytic solution obtained in the case of the coherent-mode assumption. Within this scheme the yrast alternating-parity band is constructed by the lowest eigenvalues having the appropriate parity at given angular momentum. Additionally we include the calculation of transition probabilities which are fitted with the energies simultaneously. As a result we obtain a unique set of parameters. The obtained model parameters unambiguously determine the shape of the quadrupole-octupole potential. From the resulting wave functions quadrupole deformation expectation values are calculated which are found to be in agreement with experimental values. (author)

  13. Probability model of solid to liquid-like transition of a fluid suspension after a shear flow onset

    Czech Academy of Sciences Publication Activity Database

    Nouar, C.; Říha, Pavel

    2008-01-01

    Roč. 34, č. 5 (2008), s. 477-483 ISSN 0301-9322 R&D Projects: GA AV ČR IAA200600803 Institutional research plan: CEZ:AV0Z20600510 Keywords : laminar suspension flow * liquid-liquid interface * probability model Subject RIV: BK - Fluid Dynamics Impact factor: 1.497, year: 2008

  14. Effects of variability in probable maximum precipitation patterns on flood losses

    Science.gov (United States)

    Zischg, Andreas Paul; Felder, Guido; Weingartner, Rolf; Quinn, Niall; Coxon, Gemma; Neal, Jeffrey; Freer, Jim; Bates, Paul

    2018-05-01

    The assessment of the impacts of extreme floods is important for dealing with residual risk, particularly for critical infrastructure management and for insurance purposes. Thus, modelling of the probable maximum flood (PMF) from probable maximum precipitation (PMP) by coupling hydrological and hydraulic models has gained interest in recent years. Herein, we examine whether variability in precipitation patterns exceeds or is below selected uncertainty factors in flood loss estimation and if the flood losses within a river basin are related to the probable maximum discharge at the basin outlet. We developed a model experiment with an ensemble of probable maximum precipitation scenarios created by Monte Carlo simulations. For each rainfall pattern, we computed the flood losses with a model chain and benchmarked the effects of variability in rainfall distribution with other model uncertainties. The results show that flood losses vary considerably within the river basin and depend on the timing and superimposition of the flood peaks from the basin's sub-catchments. In addition to the flood hazard component, the other components of flood risk, exposure, and vulnerability contribute remarkably to the overall variability. This leads to the conclusion that the estimation of the probable maximum expectable flood losses in a river basin should not be based exclusively on the PMF. Consequently, the basin-specific sensitivities to different precipitation patterns and the spatial organization of the settlements within the river basin need to be considered in the analyses of probable maximum flood losses.

  15. Extinction probabilities and stationary distributions of mobile genetic elements in prokaryotes: The birth-death-diversification model.

    Science.gov (United States)

    Drakos, Nicole E; Wahl, Lindi M

    2015-12-01

    Theoretical approaches are essential to our understanding of the complex dynamics of mobile genetic elements (MGEs) within genomes. Recently, the birth-death-diversification model was developed to describe the dynamics of mobile promoters (MPs), a particular class of MGEs in prokaryotes. A unique feature of this model is that genetic diversification of elements was included. To explore the implications of diversification on the longterm fate of MGE lineages, in this contribution we analyze the extinction probabilities, extinction times and equilibrium solutions of the birth-death-diversification model. We find that diversification increases both the survival and growth rate of MGE families, but the strength of this effect depends on the rate of horizontal gene transfer (HGT). We also find that the distribution of MGE families per genome is not necessarily monotonically decreasing, as observed for MPs, but may have a peak in the distribution that is related to the HGT rate. For MPs specifically, we find that new families have a high extinction probability, and predict that the number of MPs is increasing, albeit at a very slow rate. Additionally, we develop an extension of the birth-death-diversification model which allows MGEs in different regions of the genome, for example coding and non-coding, to be described by different rates. This extension may offer a potential explanation as to why the majority of MPs are located in non-promoter regions of the genome. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Pharmacological treatments in asthma-affected horses: A pair-wise and network meta-analysis.

    Science.gov (United States)

    Calzetta, L; Roncada, P; di Cave, D; Bonizzi, L; Urbani, A; Pistocchini, E; Rogliani, P; Matera, M G

    2017-11-01

    Equine asthma is a disease characterised by reversible airflow obstruction, bronchial hyper-responsiveness and airway inflammation following exposure of susceptible horses to specific airborne agents. Although clinical remission can be achieved in a low-airborne dust environment, repeated exacerbations may lead to irreversible airway remodelling. The available data on the pharmacotherapy of equine asthma result from several small studies, and no head-to-head clinical trials have been conducted among the available medications. To assess the impact of the pharmacological interventions in equine asthma and compare the effect of different classes of drugs on lung function. Pair-wise and network meta-analysis. Literature searches for clinical trials on the pharmacotherapy of equine asthma were performed. The risk of publication bias was assessed by funnel plots and Egger's test. Changes in maximum transpulmonary or pleural pressure, pulmonary resistance and dynamic lung compliance vs. control were analysed via random-effects models and Bayesian networks. The results obtained from 319 equine asthma-affected horses were extracted from 32 studies. Bronchodilators, corticosteroids and chromones improved maximum transpulmonary or pleural pressure (range: -8.0 to -21.4 cmH 2 O; Ptherapies. Long-term treatments were more effective than short-term treatments. Weak publication bias was detected. This study demonstrates that long-term treatments with inhaled corticosteroids and long-acting β 2 -AR agonists may represent the first choice for treating equine asthma. Further high quality clinical trials are needed to clarify whether inhaled bronchodilators should be preferred to inhaled corticosteroids or vice versa, and to investigate the potential superiority of combination therapy in equine asthma. © 2017 EVJ Ltd.

  17. Decision making generalized by a cumulative probability weighting function

    Science.gov (United States)

    dos Santos, Lindomar Soares; Destefano, Natália; Martinez, Alexandre Souto

    2018-01-01

    Typical examples of intertemporal decision making involve situations in which individuals must choose between a smaller reward, but more immediate, and a larger one, delivered later. Analogously, probabilistic decision making involves choices between options whose consequences differ in relation to their probability of receiving. In Economics, the expected utility theory (EUT) and the discounted utility theory (DUT) are traditionally accepted normative models for describing, respectively, probabilistic and intertemporal decision making. A large number of experiments confirmed that the linearity assumed by the EUT does not explain some observed behaviors, as nonlinear preference, risk-seeking and loss aversion. That observation led to the development of new theoretical models, called non-expected utility theories (NEUT), which include a nonlinear transformation of the probability scale. An essential feature of the so-called preference function of these theories is that the probabilities are transformed by decision weights by means of a (cumulative) probability weighting function, w(p) . We obtain in this article a generalized function for the probabilistic discount process. This function has as particular cases mathematical forms already consecrated in the literature, including discount models that consider effects of psychophysical perception. We also propose a new generalized function for the functional form of w. The limiting cases of this function encompass some parametric forms already proposed in the literature. Far beyond a mere generalization, our function allows the interpretation of probabilistic decision making theories based on the assumption that individuals behave similarly in the face of probabilities and delays and is supported by phenomenological models.

  18. On the probability of cure for heavy-ion radiotherapy

    International Nuclear Information System (INIS)

    Hanin, Leonid; Zaider, Marco

    2014-01-01

    The probability of a cure in radiation therapy (RT)—viewed as the probability of eventual extinction of all cancer cells—is unobservable, and the only way to compute it is through modeling the dynamics of cancer cell population during and post-treatment. The conundrum at the heart of biophysical models aimed at such prospective calculations is the absence of information on the initial size of the subpopulation of clonogenic cancer cells (also called stem-like cancer cells), that largely determines the outcome of RT, both in an individual and population settings. Other relevant parameters (e.g. potential doubling time, cell loss factor and survival probability as a function of dose) are, at least in principle, amenable to empirical determination. In this article we demonstrate that, for heavy-ion RT, microdosimetric considerations (justifiably ignored in conventional RT) combined with an expression for the clone extinction probability obtained from a mechanistic model of radiation cell survival lead to useful upper bounds on the size of the pre-treatment population of clonogenic cancer cells as well as upper and lower bounds on the cure probability. The main practical impact of these limiting values is the ability to make predictions about the probability of a cure for a given population of patients treated to newer, still unexplored treatment modalities from the empirically determined probability of a cure for the same or similar population resulting from conventional low linear energy transfer (typically photon/electron) RT. We also propose that the current trend to deliver a lower total dose in a smaller number of fractions with larger-than-conventional doses per fraction has physical limits that must be understood before embarking on a particular treatment schedule. (paper)

  19. Gravity and count probabilities in an expanding universe

    Science.gov (United States)

    Bouchet, Francois R.; Hernquist, Lars

    1992-01-01

    The time evolution of nonlinear clustering on large scales in cold dark matter, hot dark matter, and white noise models of the universe is investigated using N-body simulations performed with a tree code. Count probabilities in cubic cells are determined as functions of the cell size and the clustering state (redshift), and comparisons are made with various theoretical models. We isolate the features that appear to be the result of gravitational instability, those that depend on the initial conditions, and those that are likely a consequence of numerical limitations. More specifically, we study the development of skewness, kurtosis, and the fifth moment in relation to variance, the dependence of the void probability on time as well as on sparseness of sampling, and the overall shape of the count probability distribution. Implications of our results for theoretical and observational studies are discussed.

  20. Finite-size scaling of survival probability in branching processes

    OpenAIRE

    Garcia-Millan, Rosalba; Font-Clos, Francesc; Corral, Alvaro

    2014-01-01

    Branching processes pervade many models in statistical physics. We investigate the survival probability of a Galton-Watson branching process after a finite number of generations. We reveal the finite-size scaling law of the survival probability for a given branching process ruled by a probability distribution of the number of offspring per element whose standard deviation is finite, obtaining the exact scaling function as well as the critical exponents. Our findings prove the universal behavi...

  1. Problems involved in calculating the probability of rare occurrences

    International Nuclear Information System (INIS)

    Tittes, E.

    1986-01-01

    Also with regard to the characteristics such as occurrence probability or occurrence rate, there are limits which have to be observed, or else probability data and thus the concept of determinable risk itself will lose its practical value. The mathematical models applied for probability assessment are based on data supplied by the insurance companies, reliability experts in the automobile industry, or by planning experts in the field of traffic or information supply. (DG) [de

  2. Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model.

    Directory of Open Access Journals (Sweden)

    Daniel Ting

    2010-04-01

    Full Text Available Distributions of the backbone dihedral angles of proteins have been studied for over 40 years. While many statistical analyses have been presented, only a handful of probability densities are publicly available for use in structure validation and structure prediction methods. The available distributions differ in a number of important ways, which determine their usefulness for various purposes. These include: 1 input data size and criteria for structure inclusion (resolution, R-factor, etc.; 2 filtering of suspect conformations and outliers using B-factors or other features; 3 secondary structure of input data (e.g., whether helix and sheet are included; whether beta turns are included; 4 the method used for determining probability densities ranging from simple histograms to modern nonparametric density estimation; and 5 whether they include nearest neighbor effects on the distribution of conformations in different regions of the Ramachandran map. In this work, Ramachandran probability distributions are presented for residues in protein loops from a high-resolution data set with filtering based on calculated electron densities. Distributions for all 20 amino acids (with cis and trans proline treated separately have been determined, as well as 420 left-neighbor and 420 right-neighbor dependent distributions. The neighbor-independent and neighbor-dependent probability densities have been accurately estimated using Bayesian nonparametric statistical analysis based on the Dirichlet process. In particular, we used hierarchical Dirichlet process priors, which allow sharing of information between densities for a particular residue type and different neighbor residue types. The resulting distributions are tested in a loop modeling benchmark with the program Rosetta, and are shown to improve protein loop conformation prediction significantly. The distributions are available at http://dunbrack.fccc.edu/hdp.

  3. UQ for Decision Making: How (at least five) Kinds of Probability Might Come Into Play

    Science.gov (United States)

    Smith, L. A.

    2013-12-01

    In 1959 IJ Good published the discussion "Kinds of Probability" in Science. Good identified (at least) five kinds. The need for (at least) a sixth kind of probability when quantifying uncertainty in the context of climate science is discussed. This discussion brings out the differences in weather-like forecasting tasks and climate-links tasks, with a focus on the effective use both of science and of modelling in support of decision making. Good also introduced the idea of a "Dynamic probability" a probability one expects to change without any additional empirical evidence; the probabilities assigned by a chess playing program when it is only half thorough its analysis being an example. This case is contrasted with the case of "Mature probabilities" where a forecast algorithm (or model) has converged on its asymptotic probabilities and the question hinges in whether or not those probabilities are expected to change significantly before the event in question occurs, even in the absence of new empirical evidence. If so, then how might one report and deploy such immature probabilities in scientific-support of decision-making rationally? Mature Probability is suggested as a useful sixth kind, although Good would doubtlessly argue that we can get by with just one, effective communication with decision makers may be enhanced by speaking as if the others existed. This again highlights the distinction between weather-like contexts and climate-like contexts. In the former context one has access to a relevant climatology (a relevant, arguably informative distribution prior to any model simulations), in the latter context that information is not available although one can fall back on the scientific basis upon which the model itself rests, and estimate the probability that the model output is in fact misinformative. This subjective "probability of a big surprise" is one way to communicate the probability of model-based information holding in practice, the probability that the

  4. A Mechanistic Beta-Binomial Probability Model for mRNA Sequencing Data.

    Science.gov (United States)

    Smith, Gregory R; Birtwistle, Marc R

    2016-01-01

    A main application for mRNA sequencing (mRNAseq) is determining lists of differentially-expressed genes (DEGs) between two or more conditions. Several software packages exist to produce DEGs from mRNAseq data, but they typically yield different DEGs, sometimes markedly so. The underlying probability model used to describe mRNAseq data is central to deriving DEGs, and not surprisingly most softwares use different models and assumptions to analyze mRNAseq data. Here, we propose a mechanistic justification to model mRNAseq as a binomial process, with data from technical replicates given by a binomial distribution, and data from biological replicates well-described by a beta-binomial distribution. We demonstrate good agreement of this model with two large datasets. We show that an emergent feature of the beta-binomial distribution, given parameter regimes typical for mRNAseq experiments, is the well-known quadratic polynomial scaling of variance with the mean. The so-called dispersion parameter controls this scaling, and our analysis suggests that the dispersion parameter is a continually decreasing function of the mean, as opposed to current approaches that impose an asymptotic value to the dispersion parameter at moderate mean read counts. We show how this leads to current approaches overestimating variance for moderately to highly expressed genes, which inflates false negative rates. Describing mRNAseq data with a beta-binomial distribution thus may be preferred since its parameters are relatable to the mechanistic underpinnings of the technique and may improve the consistency of DEG analysis across softwares, particularly for moderately to highly expressed genes.

  5. Scale-invariant transition probabilities in free word association trajectories

    Directory of Open Access Journals (Sweden)

    Martin Elias Costa

    2009-09-01

    Full Text Available Free-word association has been used as a vehicle to understand the organization of human thoughts. The original studies relied mainly on qualitative assertions, yielding the widely intuitive notion that trajectories of word associations are structured, yet considerably more random than organized linguistic text. Here we set to determine a precise characterization of this space, generating a large number of word association trajectories in a web implemented game. We embedded the trajectories in the graph of word co-occurrences from a linguistic corpus. To constrain possible transport models we measured the memory loss and the cycling probability. These two measures could not be reconciled by a bounded diffusive model since the cycling probability was very high (16 % of order-2 cycles implying a majority of short-range associations whereas the memory loss was very rapid (converging to the asymptotic value in ∼ 7 steps which, in turn, forced a high fraction of long-range associations. We show that memory loss and cycling probabilities of free word association trajectories can be simultaneously accounted by a model in which transitions are determined by a scale invariant probability distribution.

  6. Tumor control probability after a radiation of animal tumors

    International Nuclear Information System (INIS)

    Urano, Muneyasu; Ando, Koichi; Koike, Sachiko; Nesumi, Naofumi

    1975-01-01

    Tumor control and regrowth probability of animal tumors irradiated with a single x-ray dose were determined, using a spontaneous C3H mouse mammary carcinoma. Cellular radiation sensitivity of tumor cells and tumor control probability of the tumor were examined by the TD 50 and TCD 50 assays respectively. Tumor growth kinetics were measured by counting the percentage of labelled mitosis and by measuring the growth curve. A mathematical analysis of tumor control probability was made from these results. A formula proposed, accounted for cell population kinetics or division probability model, cell sensitivity to radiation and number of tumor cells. (auth.)

  7. A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites

    Science.gov (United States)

    Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

    2009-05-01

    Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

  8. Exit probability of the one-dimensional q-voter model: Analytical results and simulations for large networks

    Science.gov (United States)

    Timpanaro, André M.; Prado, Carmen P. C.

    2014-05-01

    We discuss the exit probability of the one-dimensional q-voter model and present tools to obtain estimates about this probability, both through simulations in large networks (around 107 sites) and analytically in the limit where the network is infinitely large. We argue that the result E(ρ )=ρq/ρq+(1-ρ)q, that was found in three previous works [F. Slanina, K. Sznajd-Weron, and P. Przybyła, Europhys. Lett. 82, 18006 (2008), 10.1209/0295-5075/82/18006; R. Lambiotte and S. Redner, Europhys. Lett. 82, 18007 (2008), 10.1209/0295-5075/82/18007, for the case q =2; and P. Przybyła, K. Sznajd-Weron, and M. Tabiszewski, Phys. Rev. E 84, 031117 (2011), 10.1103/PhysRevE.84.031117, for q >2] using small networks (around 103 sites), is a good approximation, but there are noticeable deviations that appear even for small systems and that do not disappear when the system size is increased (with the notable exception of the case q =2). We also show that, under some simple and intuitive hypotheses, the exit probability must obey the inequality ρq/ρq+(1-ρ)≤E(ρ)≤ρ/ρ +(1-ρ)q in the infinite size limit. We believe this settles in the negative the suggestion made [S. Galam and A. C. R. Martins, Europhys. Lett. 95, 48005 (2001), 10.1209/0295-5075/95/48005] that this result would be a finite size effect, with the exit probability actually being a step function. We also show how the result that the exit probability cannot be a step function can be reconciled with the Galam unified frame, which was also a source of controversy.

  9. Probability Model of Allele Frequency of Alzheimer’s Disease Genetic Risk Factor

    Directory of Open Access Journals (Sweden)

    Afshin Fayyaz-Movaghar

    2016-06-01

    Full Text Available Background and Purpose: The identification of genetics risk factors of human diseases is very important. This study is conducted to model the allele frequencies (AFs of Alzheimer’s disease. Materials and Methods: In this study, several candidate probability distributions are fitted on a data set of Alzheimer’s disease genetic risk factor. Unknown parameters of the considered distributions are estimated, and some criterions of goodness-of-fit are calculated for the sake of comparison. Results: Based on some statistical criterions, the beta distribution gives the best fit on AFs. However, the estimate values of the parameters of beta distribution lead us to the standard uniform distribution. Conclusion: The AFs of Alzheimer’s disease follow the standard uniform distribution.

  10. Foundations of probability

    International Nuclear Information System (INIS)

    Fraassen, B.C. van

    1979-01-01

    The interpretation of probabilities in physical theories are considered, whether quantum or classical. The following points are discussed 1) the functions P(μ, Q) in terms of which states and propositions can be represented, are classical (Kolmogoroff) probabilities, formally speaking, 2) these probabilities are generally interpreted as themselves conditional, and the conditions are mutually incompatible where the observables are maximal and 3) testing of the theory typically takes the form of confronting the expectation values of observable Q calculated with probability measures P(μ, Q) for states μ; hence, of comparing the probabilities P(μ, Q)(E) with the frequencies of occurrence of the corresponding events. It seems that even the interpretation of quantum mechanics, in so far as it concerns what the theory says about the empirical (i.e. actual, observable) phenomena, deals with the confrontation of classical probability measures with observable frequencies. This confrontation is studied. (Auth./C.F.)

  11. Impact of the infectious period on epidemics

    Science.gov (United States)

    Wilkinson, Robert R.; Sharkey, Kieran J.

    2018-05-01

    The duration of the infectious period is a crucial determinant of the ability of an infectious disease to spread. We consider an epidemic model that is network based and non-Markovian, containing classic Kermack-McKendrick, pairwise, message passing, and spatial models as special cases. For this model, we prove a monotonic relationship between the variability of the infectious period (with fixed mean) and the probability that the infection will reach any given subset of the population by any given time. For certain families of distributions, this result implies that epidemic severity is decreasing with respect to the variance of the infectious period. The striking importance of this relationship is demonstrated numerically. We then prove, with a fixed basic reproductive ratio (R0), a monotonic relationship between the variability of the posterior transmission probability (which is a function of the infectious period) and the probability that the infection will reach any given subset of the population by any given time. Thus again, even when R0 is fixed, variability of the infectious period tends to dampen the epidemic. Numerical results illustrate this but indicate the relationship is weaker. We then show how our results apply to message passing, pairwise, and Kermack-McKendrick epidemic models, even when they are not exactly consistent with the stochastic dynamics. For Poissonian contact processes, and arbitrarily distributed infectious periods, we demonstrate how systems of delay differential equations and ordinary differential equations can provide upper and lower bounds, respectively, for the probability that any given individual has been infected by any given time.

  12. Non-Archimedean Probability

    NARCIS (Netherlands)

    Benci, Vieri; Horsten, Leon; Wenmackers, Sylvia

    We propose an alternative approach to probability theory closely related to the framework of numerosity theory: non-Archimedean probability (NAP). In our approach, unlike in classical probability theory, all subsets of an infinite sample space are measurable and only the empty set gets assigned

  13. Trial type probability modulates the cost of antisaccades

    Science.gov (United States)

    Chiau, Hui-Yan; Tseng, Philip; Su, Jia-Han; Tzeng, Ovid J. L.; Hung, Daisy L.; Muggleton, Neil G.

    2011-01-01

    The antisaccade task, where eye movements are made away from a target, has been used to investigate the flexibility of cognitive control of behavior. Antisaccades usually have longer saccade latencies than prosaccades, the so-called antisaccade cost. Recent studies have shown that this antisaccade cost can be modulated by event probability. This may mean that the antisaccade cost can be reduced, or even reversed, if the probability of surrounding events favors the execution of antisaccades. The probabilities of prosaccades and antisaccades were systematically manipulated by changing the proportion of a certain type of trial in an interleaved pro/antisaccades task. We aimed to disentangle the intertwined relationship between trial type probabilities and the antisaccade cost with the ultimate goal of elucidating how probabilities of trial types modulate human flexible behaviors, as well as the characteristics of such modulation effects. To this end, we examined whether implicit trial type probability can influence saccade latencies and also manipulated the difficulty of cue discriminability to see how effects of trial type probability would change when the demand on visual perceptual analysis was high or low. A mixed-effects model was applied to the analysis to dissect the factors contributing to the modulation effects of trial type probabilities. Our results suggest that the trial type probability is one robust determinant of antisaccade cost. These findings highlight the importance of implicit probability in the flexibility of cognitive control of behavior. PMID:21543748

  14. Handbook of probability

    CERN Document Server

    Florescu, Ionut

    2013-01-01

    THE COMPLETE COLLECTION NECESSARY FOR A CONCRETE UNDERSTANDING OF PROBABILITY Written in a clear, accessible, and comprehensive manner, the Handbook of Probability presents the fundamentals of probability with an emphasis on the balance of theory, application, and methodology. Utilizing basic examples throughout, the handbook expertly transitions between concepts and practice to allow readers an inclusive introduction to the field of probability. The book provides a useful format with self-contained chapters, allowing the reader easy and quick reference. Each chapter includes an introductio

  15. Probability-1

    CERN Document Server

    Shiryaev, Albert N

    2016-01-01

    This book contains a systematic treatment of probability from the ground up, starting with intuitive ideas and gradually developing more sophisticated subjects, such as random walks, martingales, Markov chains, the measure-theoretic foundations of probability theory, weak convergence of probability measures, and the central limit theorem. Many examples are discussed in detail, and there are a large number of exercises. The book is accessible to advanced undergraduates and can be used as a text for independent study. To accommodate the greatly expanded material in the third edition of Probability, the book is now divided into two volumes. This first volume contains updated references and substantial revisions of the first three chapters of the second edition. In particular, new material has been added on generating functions, the inclusion-exclusion principle, theorems on monotonic classes (relying on a detailed treatment of “π-λ” systems), and the fundamental theorems of mathematical statistics.

  16. Probability Machines: Consistent Probability Estimation Using Nonparametric Learning Machines

    Science.gov (United States)

    Malley, J. D.; Kruppa, J.; Dasgupta, A.; Malley, K. G.; Ziegler, A.

    2011-01-01

    Summary Background Most machine learning approaches only provide a classification for binary responses. However, probabilities are required for risk estimation using individual patient characteristics. It has been shown recently that every statistical learning machine known to be consistent for a nonparametric regression problem is a probability machine that is provably consistent for this estimation problem. Objectives The aim of this paper is to show how random forests and nearest neighbors can be used for consistent estimation of individual probabilities. Methods Two random forest algorithms and two nearest neighbor algorithms are described in detail for estimation of individual probabilities. We discuss the consistency of random forests, nearest neighbors and other learning machines in detail. We conduct a simulation study to illustrate the validity of the methods. We exemplify the algorithms by analyzing two well-known data sets on the diagnosis of appendicitis and the diagnosis of diabetes in Pima Indians. Results Simulations demonstrate the validity of the method. With the real data application, we show the accuracy and practicality of this approach. We provide sample code from R packages in which the probability estimation is already available. This means that all calculations can be performed using existing software. Conclusions Random forest algorithms as well as nearest neighbor approaches are valid machine learning methods for estimating individual probabilities for binary responses. Freely available implementations are available in R and may be used for applications. PMID:21915433

  17. Generalized Network Psychometrics : Combining Network and Latent Variable Models

    NARCIS (Netherlands)

    Epskamp, S.; Rhemtulla, M.; Borsboom, D.

    2017-01-01

    We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

  18. Dynamical pairwise entanglement and two-point correlations in the three-ligand spin-star structure

    Science.gov (United States)

    Motamedifar, M.

    2017-10-01

    We consider the three-ligand spin-star structure through homogeneous Heisenberg interactions (XXX-3LSSS) in the framework of dynamical pairwise entanglement. It is shown that the time evolution of the central qubit ;one-particle; state (COPS) brings about the generation of quantum W states at periodical time instants. On the contrary, W states cannot be generated from the time evolution of a ligand ;one-particle; state (LOPS). We also investigate the dynamical behavior of two-point quantum correlations as well as the expectation values of the different spin-components for each element in the XXX-3LSSS. It is found that when a W state is generated, the same value of the concurrence between any two arbitrary qubits arises from the xx and yy two-point quantum correlations. On the opposite, zz quantum correlation between any two qubits vanishes at these time instants.

  19. Future southcentral US wildfire probability due to climate change

    Science.gov (United States)

    Stambaugh, Michael C.; Guyette, Richard P.; Stroh, Esther D.; Struckhoff, Matthew A.; Whittier, Joanna B.

    2018-01-01

    Globally, changing fire regimes due to climate is one of the greatest threats to ecosystems and society. In this paper, we present projections of future fire probability for the southcentral USA using downscaled climate projections and the Physical Chemistry Fire Frequency Model (PC2FM). Future fire probability is projected to both increase and decrease across the study region of Oklahoma, New Mexico, and Texas. Among all end-of-century projections, change in fire probabilities (CFPs) range from − 51 to + 240%. Greatest absolute increases in fire probability are shown for areas within the range of approximately 75 to 160 cm mean annual precipitation (MAP), regardless of climate model. Although fire is likely to become more frequent across the southcentral USA, spatial patterns may remain similar unless significant increases in precipitation occur, whereby more extensive areas with increased fire probability are predicted. Perhaps one of the most important results is illumination of climate changes where fire probability response (+, −) may deviate (i.e., tipping points). Fire regimes of southcentral US ecosystems occur in a geographic transition zone from reactant- to reaction-limited conditions, potentially making them uniquely responsive to different scenarios of temperature and precipitation changes. Identification and description of these conditions may help anticipate fire regime changes that will affect human health, agriculture, species conservation, and nutrient and water cycling.

  20. Heuristics can produce surprisingly rational probability estimates: Comment on Costello and Watts (2014).

    Science.gov (United States)

    Nilsson, Håkan; Juslin, Peter; Winman, Anders

    2016-01-01

    Costello and Watts (2014) present a model assuming that people's knowledge of probabilities adheres to probability theory, but that their probability judgments are perturbed by a random noise in the retrieval from memory. Predictions for the relationships between probability judgments for constituent events and their disjunctions and conjunctions, as well as for sums of such judgments were derived from probability theory. Costello and Watts (2014) report behavioral data showing that subjective probability judgments accord with these predictions. Based on the finding that subjective probability judgments follow probability theory, Costello and Watts (2014) conclude that the results imply that people's probability judgments embody the rules of probability theory and thereby refute theories of heuristic processing. Here, we demonstrate the invalidity of this conclusion by showing that all of the tested predictions follow straightforwardly from an account assuming heuristic probability integration (Nilsson, Winman, Juslin, & Hansson, 2009). We end with a discussion of a number of previous findings that harmonize very poorly with the predictions by the model suggested by Costello and Watts (2014). (c) 2015 APA, all rights reserved).