Rivera, Diego; Rivas, Yessica; Godoy, Alex
2015-02-01
Hydrological models are simplified representations of natural processes and subject to errors. Uncertainty bounds are a commonly used way to assess the impact of an input or model architecture uncertainty in model outputs. Different sets of parameters could have equally robust goodness-of-fit indicators, which is known as Equifinality. We assessed the outputs from a lumped conceptual hydrological model to an agricultural watershed in central Chile under strong interannual variability (coefficient of variability of 25%) by using the Equifinality concept and uncertainty bounds. The simulation period ran from January 1999 to December 2006. Equifinality and uncertainty bounds from GLUE methodology (Generalized Likelihood Uncertainty Estimation) were used to identify parameter sets as potential representations of the system. The aim of this paper is to exploit the use of uncertainty bounds to differentiate behavioural parameter sets in a simple hydrological model. Then, we analyze the presence of equifinality in order to improve the identification of relevant hydrological processes. The water balance model for Chillan River exhibits, at a first stage, equifinality. However, it was possible to narrow the range for the parameters and eventually identify a set of parameters representing the behaviour of the watershed (a behavioural model) in agreement with observational and soft data (calculation of areal precipitation over the watershed using an isohyetal map). The mean width of the uncertainty bound around the predicted runoff for the simulation period decreased from 50 to 20 m3s-1 after fixing the parameter controlling the areal precipitation over the watershed. This decrement is equivalent to decreasing the ratio between simulated and observed discharge from 5.2 to 2.5. Despite the criticisms against the GLUE methodology, such as the lack of statistical formality, it is identified as a useful tool assisting the modeller with the identification of critical parameters.
Wang, Z.
2015-12-01
For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Vrugt, Jasper A.; Madsen, Henrik
2008-01-01
propose an alternative strategy to determine the value of the cutoff threshold based on the appropriate coverage of the resulting uncertainty bounds. We demonstrate the superiority of this revised GLUE method with three different conceptual watershed models of increasing complexity, using both synthetic......In the last few decades hydrologists have made tremendous progress in using dynamic simulation models for the analysis and understanding of hydrologic systems. However, predictions with these models are often deterministic and as such they focus on the most probable forecast, without an explicit...... of applications. However, the MC based sampling strategy of the prior parameter space typically utilized in GLUE is not particularly efficient in finding behavioral simulations. This becomes especially problematic for high-dimensional parameter estimation problems, and in the case of complex simulation models...
Directory of Open Access Journals (Sweden)
Seung Oh Lee
2013-10-01
Full Text Available Collection and investigation of flood information are essential to understand the nature of floods, but this has proved difficult in data-poor environments, or in developing or under-developed countries due to economic and technological limitations. The development of remote sensing data, GIS, and modeling techniques have, therefore, proved to be useful tools in the analysis of the nature of floods. Accordingly, this study attempts to estimate a flood discharge using the generalized likelihood uncertainty estimation (GLUE methodology and a 1D hydraulic model, with remote sensing data and topographic data, under the assumed condition that there is no gauge station in the Missouri river, Nebraska, and Wabash River, Indiana, in the United States. The results show that the use of Landsat leads to a better discharge approximation on a large-scale reach than on a small-scale. Discharge approximation using the GLUE depended on the selection of likelihood measures. Consideration of physical conditions in study reaches could, therefore, contribute to an appropriate selection of informal likely measurements. The river discharge assessed by using Landsat image and the GLUE Methodology could be useful in supplementing flood information for flood risk management at a planning level in ungauged basins. However, it should be noted that this approach to the real-time application might be difficult due to the GLUE procedure.
Quantifying uncertainty, variability and likelihood for ordinary differential equation models
LENUS (Irish Health Repository)
Weisse, Andrea Y
2010-10-28
Abstract Background In many applications, ordinary differential equation (ODE) models are subject to uncertainty or variability in initial conditions and parameters. Both, uncertainty and variability can be quantified in terms of a probability density function on the state and parameter space. Results The partial differential equation that describes the evolution of this probability density function has a form that is particularly amenable to application of the well-known method of characteristics. The value of the density at some point in time is directly accessible by the solution of the original ODE extended by a single extra dimension (for the value of the density). This leads to simple methods for studying uncertainty, variability and likelihood, with significant advantages over more traditional Monte Carlo and related approaches especially when studying regions with low probability. Conclusions While such approaches based on the method of characteristics are common practice in other disciplines, their advantages for the study of biological systems have so far remained unrecognized. Several examples illustrate performance and accuracy of the approach and its limitations.
Generalized empirical likelihood methods for analyzing longitudinal data
Wang, S.; Qian, L.; Carroll, R. J.
2010-01-01
Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks
Practical likelihood analysis for spatial generalized linear mixed models
DEFF Research Database (Denmark)
Bonat, W. H.; Ribeiro, Paulo Justiniano
2016-01-01
We investigate an algorithm for maximum likelihood estimation of spatial generalized linear mixed models based on the Laplace approximation. We compare our algorithm with a set of alternative approaches for two datasets from the literature. The Rhizoctonia root rot and the Rongelap are......, respectively, examples of binomial and count datasets modeled by spatial generalized linear mixed models. Our results show that the Laplace approximation provides similar estimates to Markov Chain Monte Carlo likelihood, Monte Carlo expectation maximization, and modified Laplace approximation. Some advantages...... of Laplace approximation include the computation of the maximized log-likelihood value, which can be used for model selection and tests, and the possibility to obtain realistic confidence intervals for model parameters based on profile likelihoods. The Laplace approximation also avoids the tuning...
Bayesian interpretation of Generalized empirical likelihood by maximum entropy
Rochet , Paul
2011-01-01
We study a parametric estimation problem related to moment condition models. As an alternative to the generalized empirical likelihood (GEL) and the generalized method of moments (GMM), a Bayesian approach to the problem can be adopted, extending the MEM procedure to parametric moment conditions. We show in particular that a large number of GEL estimators can be interpreted as a maximum entropy solution. Moreover, we provide a more general field of applications by proving the method to be rob...
Generalized empirical likelihood methods for analyzing longitudinal data
Wang, S.
2010-02-16
Efficient estimation of parameters is a major objective in analyzing longitudinal data. We propose two generalized empirical likelihood based methods that take into consideration within-subject correlations. A nonparametric version of the Wilks theorem for the limiting distributions of the empirical likelihood ratios is derived. It is shown that one of the proposed methods is locally efficient among a class of within-subject variance-covariance matrices. A simulation study is conducted to investigate the finite sample properties of the proposed methods and compare them with the block empirical likelihood method by You et al. (2006) and the normal approximation with a correctly estimated variance-covariance. The results suggest that the proposed methods are generally more efficient than existing methods which ignore the correlation structure, and better in coverage compared to the normal approximation with correctly specified within-subject correlation. An application illustrating our methods and supporting the simulation study results is also presented.
Sampling of systematic errors to estimate likelihood weights in nuclear data uncertainty propagation
International Nuclear Information System (INIS)
Helgesson, P.; Sjöstrand, H.; Koning, A.J.; Rydén, J.; Rochman, D.; Alhassan, E.; Pomp, S.
2016-01-01
In methodologies for nuclear data (ND) uncertainty assessment and propagation based on random sampling, likelihood weights can be used to infer experimental information into the distributions for the ND. As the included number of correlated experimental points grows large, the computational time for the matrix inversion involved in obtaining the likelihood can become a practical problem. There are also other problems related to the conventional computation of the likelihood, e.g., the assumption that all experimental uncertainties are Gaussian. In this study, a way to estimate the likelihood which avoids matrix inversion is investigated; instead, the experimental correlations are included by sampling of systematic errors. It is shown that the model underlying the sampling methodology (using univariate normal distributions for random and systematic errors) implies a multivariate Gaussian for the experimental points (i.e., the conventional model). It is also shown that the likelihood estimates obtained through sampling of systematic errors approach the likelihood obtained with matrix inversion as the sample size for the systematic errors grows large. In studied practical cases, it is seen that the estimates for the likelihood weights converge impractically slowly with the sample size, compared to matrix inversion. The computational time is estimated to be greater than for matrix inversion in cases with more experimental points, too. Hence, the sampling of systematic errors has little potential to compete with matrix inversion in cases where the latter is applicable. Nevertheless, the underlying model and the likelihood estimates can be easier to intuitively interpret than the conventional model and the likelihood function involving the inverted covariance matrix. Therefore, this work can both have pedagogical value and be used to help motivating the conventional assumption of a multivariate Gaussian for experimental data. The sampling of systematic errors could also
GENERALIZATION OF RAYLEIGH MAXIMUM LIKELIHOOD DESPECKLING FILTER USING QUADRILATERAL KERNELS
Directory of Open Access Journals (Sweden)
S. Sridevi
2013-02-01
Full Text Available Speckle noise is the most prevalent noise in clinical ultrasound images. It visibly looks like light and dark spots and deduce the pixel intensity as murkiest. Gazing at fetal ultrasound images, the impact of edge and local fine details are more palpable for obstetricians and gynecologists to carry out prenatal diagnosis of congenital heart disease. A robust despeckling filter has to be contrived to proficiently suppress speckle noise and simultaneously preserve the features. The proposed filter is the generalization of Rayleigh maximum likelihood filter by the exploitation of statistical tools as tuning parameters and use different shapes of quadrilateral kernels to estimate the noise free pixel from neighborhood. The performance of various filters namely Median, Kuwahura, Frost, Homogenous mask filter and Rayleigh maximum likelihood filter are compared with the proposed filter in terms PSNR and image profile. Comparatively the proposed filters surpass the conventional filters.
Uncertainty about the true source. A note on the likelihood ratio at the activity level.
Taroni, Franco; Biedermann, Alex; Bozza, Silvia; Comte, Jennifer; Garbolino, Paolo
2012-07-10
This paper focuses on likelihood ratio based evaluations of fibre evidence in cases in which there is uncertainty about whether or not the reference item available for analysis - that is, an item typically taken from the suspect or seized at his home - is the item actually worn at the time of the offence. A likelihood ratio approach is proposed that, for situations in which certain categorical assumptions can be made about additionally introduced parameters, converges to formula described in existing literature. The properties of the proposed likelihood ratio approach are analysed through sensitivity analyses and discussed with respect to possible argumentative implications that arise in practice. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.
Maximum-likelihood fitting of data dominated by Poisson statistical uncertainties
International Nuclear Information System (INIS)
Stoneking, M.R.; Den Hartog, D.J.
1996-06-01
The fitting of data by χ 2 -minimization is valid only when the uncertainties in the data are normally distributed. When analyzing spectroscopic or particle counting data at very low signal level (e.g., a Thomson scattering diagnostic), the uncertainties are distributed with a Poisson distribution. The authors have developed a maximum-likelihood method for fitting data that correctly treats the Poisson statistical character of the uncertainties. This method maximizes the total probability that the observed data are drawn from the assumed fit function using the Poisson probability function to determine the probability for each data point. The algorithm also returns uncertainty estimates for the fit parameters. They compare this method with a χ 2 -minimization routine applied to both simulated and real data. Differences in the returned fits are greater at low signal level (less than ∼20 counts per measurement). the maximum-likelihood method is found to be more accurate and robust, returning a narrower distribution of values for the fit parameters with fewer outliers
Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood e...
Extending the Applicability of the Generalized Likelihood Function for Zero-Inflated Data Series
Oliveira, Debora Y.; Chaffe, Pedro L. B.; Sá, João. H. M.
2018-03-01
Proper uncertainty estimation for data series with a high proportion of zero and near zero observations has been a challenge in hydrologic studies. This technical note proposes a modification to the Generalized Likelihood function that accounts for zero inflation of the error distribution (ZI-GL). We compare the performance of the proposed ZI-GL with the original Generalized Likelihood function using the entire data series (GL) and by simply suppressing zero observations (GLy>0). These approaches were applied to two interception modeling examples characterized by data series with a significant number of zeros. The ZI-GL produced better uncertainty ranges than the GL as measured by the precision, reliability and volumetric bias metrics. The comparison between ZI-GL and GLy>0 highlights the need for further improvement in the treatment of residuals from near zero simulations when a linear heteroscedastic error model is considered. Aside from the interception modeling examples illustrated herein, the proposed ZI-GL may be useful for other hydrologic studies, such as for the modeling of the runoff generation in hillslopes and ephemeral catchments.
Cheng, Qin-Bo; Chen, Xi; Xu, Chong-Yu; Reinhardt-Imjela, Christian; Schulte, Achim
2014-11-01
In this study, the likelihood functions for uncertainty analysis of hydrological models are compared and improved through the following steps: (1) the equivalent relationship between the Nash-Sutcliffe Efficiency coefficient (NSE) and the likelihood function with Gaussian independent and identically distributed residuals is proved; (2) a new estimation method of the Box-Cox transformation (BC) parameter is developed to improve the effective elimination of the heteroscedasticity of model residuals; and (3) three likelihood functions-NSE, Generalized Error Distribution with BC (BC-GED) and Skew Generalized Error Distribution with BC (BC-SGED)-are applied for SWAT-WB-VSA (Soil and Water Assessment Tool - Water Balance - Variable Source Area) model calibration in the Baocun watershed, Eastern China. Performances of calibrated models are compared using the observed river discharges and groundwater levels. The result shows that the minimum variance constraint can effectively estimate the BC parameter. The form of the likelihood function significantly impacts on the calibrated parameters and the simulated results of high and low flow components. SWAT-WB-VSA with the NSE approach simulates flood well, but baseflow badly owing to the assumption of Gaussian error distribution, where the probability of the large error is low, but the small error around zero approximates equiprobability. By contrast, SWAT-WB-VSA with the BC-GED or BC-SGED approach mimics baseflow well, which is proved in the groundwater level simulation. The assumption of skewness of the error distribution may be unnecessary, because all the results of the BC-SGED approach are nearly the same as those of the BC-GED approach.
[Dealing with diagnostic uncertainty in general practice].
Wübken, Magdalena; Oswald, Jana; Schneider, Antonius
2013-01-01
In general, the prevalence of diseases is low in primary care. Therefore, the positive predictive value of diagnostic tests is lower than in hospitals where patients are highly selected. In addition, the patients present with milder forms of disease; and many diseases might hide behind the initial symptom(s). These facts lead to diagnostic uncertainty which is somewhat inherent to general practice. This narrative review discusses different sources of and reasons for uncertainty and strategies to deal with it in the context of the current literature. Fear of uncertainty correlates with higher diagnostic activities. The attitude towards uncertainty correlates with the choice of medical speciality by vocational trainees or medical students. An intolerance of uncertainty, which still increases as medicine is making steady progress, might partly explain the growing shortage of general practitioners. The bio-psycho-social context appears to be important to diagnostic decision-making. The effect of intuition and heuristics are investigated by cognitive psychologists. It is still unclear whether these aspects are prone to bias or useful, which might depend on the context of medical decisions. Good communication is of great importance to share uncertainty with the patients in a transparent way and to alleviate shared decision-making. Dealing with uncertainty should be seen as an important core component of general practice and needs to be investigated in more detail to improve the respective medical decisions. Copyright © 2013. Published by Elsevier GmbH.
Hall, Steven R.; Walker, Bruce K.
1990-01-01
A new failure detection and isolation algorithm for linear dynamic systems is presented. This algorithm, the Orthogonal Series Generalized Likelihood Ratio (OSGLR) test, is based on the assumption that the failure modes of interest can be represented by truncated series expansions. This assumption leads to a failure detection algorithm with several desirable properties. Computer simulation results are presented for the detection of the failures of actuators and sensors of a C-130 aircraft. The results show that the OSGLR test generally performs as well as the GLR test in terms of time to detect a failure and is more robust to failure mode uncertainty. However, the OSGLR test is also somewhat more sensitive to modeling errors than the GLR test.
Quantum wells and the generalized uncertainty principle
International Nuclear Information System (INIS)
Blado, Gardo; Owens, Constance; Meyers, Vincent
2014-01-01
The finite and infinite square wells are potentials typically discussed in undergraduate quantum mechanics courses. In this paper, we discuss these potentials in the light of the recent studies of the modification of the Heisenberg uncertainty principle into a generalized uncertainty principle (GUP) as a consequence of attempts to formulate a quantum theory of gravity. The fundamental concepts of the minimal length scale and the GUP are discussed and the modified energy eigenvalues and transmission coefficient are derived. (paper)
Generalized Landau-Pollak uncertainty relation
International Nuclear Information System (INIS)
Miyadera, Takayuki; Imai, Hideki
2007-01-01
The Landau-Pollak uncertainty relation treats a pair of rank one projection valued measures and imposes a restriction on their probability distributions. It gives a nontrivial bound for summation of their maximum values. We give a generalization of this bound (weak version of the Landau-Pollak uncertainty relation). Our generalization covers a pair of positive operator valued measures. A nontrivial but slightly weak inequality that can treat an arbitrary number of positive operator valued measures is also presented. A possible application to the problem of separability criterion is also suggested
Energy and Uncertainty in General Relativity
Cooperstock, F. I.; Dupre, M. J.
2018-03-01
The issue of energy and its potential localizability in general relativity has challenged physicists for more than a century. Many non-invariant measures were proposed over the years but an invariant measure was never found. We discovered the invariant localized energy measure by expanding the domain of investigation from space to spacetime. We note from relativity that the finiteness of the velocity of propagation of interactions necessarily induces indefiniteness in measurements. This is because the elements of actual physical systems being measured as well as their detectors are characterized by entire four-velocity fields, which necessarily leads to information from a measured system being processed by the detector in a spread of time. General relativity adds additional indefiniteness because of the variation in proper time between elements. The uncertainty is encapsulated in a generalized uncertainty principle, in parallel with that of Heisenberg, which incorporates the localized contribution of gravity to energy. This naturally leads to a generalized uncertainty principle for momentum as well. These generalized forms and the gravitational contribution to localized energy would be expected to be of particular importance in the regimes of ultra-strong gravitational fields. We contrast our invariant spacetime energy measure with the standard 3-space energy measure which is familiar from special relativity, appreciating why general relativity demands a measure in spacetime as opposed to 3-space. We illustrate the misconceptions by certain authors of our approach.
A revision of the generalized uncertainty principle
International Nuclear Information System (INIS)
Bambi, Cosimo
2008-01-01
The generalized uncertainty principle arises from the Heisenberg uncertainty principle when gravity is taken into account, so the leading order correction to the standard formula is expected to be proportional to the gravitational constant G N = L 2 Pl . On the other hand, the emerging picture suggests a set of departures from the standard theory which demand a revision of all the arguments used to deduce heuristically the new rule. In particular, one can now argue that the leading order correction to the Heisenberg uncertainty principle is proportional to the first power of the Planck length L Pl . If so, the departures from ordinary quantum mechanics would be much less suppressed than what is commonly thought
Generalized uncertainty principle and quantum gravity phenomenology
Bosso, Pasquale
The fundamental physical description of Nature is based on two mutually incompatible theories: Quantum Mechanics and General Relativity. Their unification in a theory of Quantum Gravity (QG) remains one of the main challenges of theoretical physics. Quantum Gravity Phenomenology (QGP) studies QG effects in low-energy systems. The basis of one such phenomenological model is the Generalized Uncertainty Principle (GUP), which is a modified Heisenberg uncertainty relation and predicts a deformed canonical commutator. In this thesis, we compute Planck-scale corrections to angular momentum eigenvalues, the hydrogen atom spectrum, the Stern-Gerlach experiment, and the Clebsch-Gordan coefficients. We then rigorously analyze the GUP-perturbed harmonic oscillator and study new coherent and squeezed states. Furthermore, we introduce a scheme for increasing the sensitivity of optomechanical experiments for testing QG effects. Finally, we suggest future projects that may potentially test QG effects in the laboratory.
Quantum Action Principle with Generalized Uncertainty Principle
Gu, Jie
2013-01-01
One of the common features in all promising candidates of quantum gravity is the existence of a minimal length scale, which naturally emerges with a generalized uncertainty principle, or equivalently a modified commutation relation. Schwinger's quantum action principle was modified to incorporate this modification, and was applied to the calculation of the kernel of a free particle, partly recovering the result previously studied using path integral.
A review of the generalized uncertainty principle
International Nuclear Information System (INIS)
Tawfik, Abdel Nasser; Diab, Abdel Magied
2015-01-01
Based on string theory, black hole physics, doubly special relativity and some ‘thought’ experiments, minimal distance and/or maximum momentum are proposed. As alternatives to the generalized uncertainty principle (GUP), the modified dispersion relation, the space noncommutativity, the Lorentz invariance violation, and the quantum-gravity-induced birefringence effects are summarized. The origin of minimal measurable quantities and the different GUP approaches are reviewed and the corresponding observations are analysed. Bounds on the GUP parameter are discussed and implemented in the understanding of recent PLANCK observations of cosmic inflation. The higher-order GUP approaches predict minimal length uncertainty with and without maximum momenta. Possible arguments against the GUP are discussed; for instance, the concern about its compatibility with the equivalence principles, the universality of gravitational redshift and the free fall and law of reciprocal action are addressed. (review)
Linear Programming Problems for Generalized Uncertainty
Thipwiwatpotjana, Phantipa
2010-01-01
Uncertainty occurs when there is more than one realization that can represent an information. This dissertation concerns merely discrete realizations of an uncertainty. Different interpretations of an uncertainty and their relationships are addressed when the uncertainty is not a probability of each realization. A well known model that can handle…
Directory of Open Access Journals (Sweden)
L. STRAGEVITCH
1997-03-01
Full Text Available The equations of the method based on the maximum likelihood principle have been rewritten in a suitable generalized form to allow the use of any number of implicit constraints in the determination of model parameters from experimental data and from the associated experimental uncertainties. In addition to the use of any number of constraints, this method also allows data, with different numbers of constraints, to be reduced simultaneously. Application of the method is illustrated in the reduction of liquid-liquid equilibrium data of binary, ternary and quaternary systems simultaneously
Towards Thermodynamics with Generalized Uncertainty Principle
International Nuclear Information System (INIS)
Moussa, Mohamed; Farag Ali, Ahmed
2014-01-01
Various frameworks of quantum gravity predict a modification in the Heisenberg uncertainty principle to a so-called generalized uncertainty principle (GUP). Introducing quantum gravity effect makes a considerable change in the density of states inside the volume of the phase space which changes the statistical and thermodynamical properties of any physical system. In this paper we investigate the modification in thermodynamic properties of ideal gases and photon gas. The partition function is calculated and using it we calculated a considerable growth in the thermodynamical functions for these considered systems. The growth may happen due to an additional repulsive force between constitutes of gases which may be due to the existence of GUP, hence predicting a considerable increase in the entropy of the system. Besides, by applying GUP on an ideal gas in a trapped potential, it is found that GUP assumes a minimum measurable value of thermal wavelength of particles which agrees with discrete nature of the space that has been derived in previous studies from the GUP
[Dealing with uncertainty--the hypermodernity of general practice].
Barth, Niklas; Nassehi, Armin; Schneider, Antonius
2014-01-01
The general practitioner is fundamentally dealing with uncertainty. On the one hand, we want to demonstrate that uncertainty cannot simply be stipulated as a matter of fact. Instead, we will show that this uncertainty is a performative effect of the primary care setting. On the other hand, we want to point out that the general practitioner's ability to bear uncertainty is a genuinely hypermodern way of productively dealing with uncertainty. Copyright © 2013. Published by Elsevier GmbH.
Generalized linear models with random effects unified analysis via H-likelihood
Lee, Youngjo; Pawitan, Yudi
2006-01-01
Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of...
Generalized Empirical Likelihood-Based Focused Information Criterion and Model Averaging
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Naoya Sueishi
2013-07-01
Full Text Available This paper develops model selection and averaging methods for moment restriction models. We first propose a focused information criterion based on the generalized empirical likelihood estimator. We address the issue of selecting an optimal model, rather than a correct model, for estimating a specific parameter of interest. Then, this study investigates a generalized empirical likelihood-based model averaging estimator that minimizes the asymptotic mean squared error. A simulation study suggests that our averaging estimator can be a useful alternative to existing post-selection estimators.
Lorentz violation and generalized uncertainty principle
Lambiase, Gaetano; Scardigli, Fabio
2018-04-01
Investigations on possible violation of Lorentz invariance have been widely pursued in the last decades, both from theoretical and experimental sides. A comprehensive framework to formulate the problem is the standard model extension (SME) proposed by A. Kostelecky, where violation of Lorentz invariance is encoded into specific coefficients. Here we present a procedure to link the deformation parameter β of the generalized uncertainty principle to the SME coefficients of the gravity sector. The idea is to compute the Hawking temperature of a black hole in two different ways. The first way involves the deformation parameter β , and therefore we get a deformed Hawking temperature containing the parameter β . The second way involves a deformed Schwarzschild metric containing the Lorentz violating terms s¯μ ν of the gravity sector of the SME. The comparison between the two different techniques yields a relation between β and s¯μ ν. In this way bounds on β transferred from s¯μ ν are improved by many orders of magnitude when compared with those derived in other gravitational frameworks. Also the opposite possibility of bounds transferred from β to s¯μ ν is briefly discussed.
Penfield, Randall D.; Bergeron, Jennifer M.
2005-01-01
This article applies a weighted maximum likelihood (WML) latent trait estimator to the generalized partial credit model (GPCM). The relevant equations required to obtain the WML estimator using the Newton-Raphson algorithm are presented, and a simulation study is described that compared the properties of the WML estimator to those of the maximum…
International Nuclear Information System (INIS)
Romanowicz, Renata; Young, Peter C.
2003-01-01
Stochastic Transfer Function (STF) and Generalised Likelihood Uncertainty Estimation (GLUE) techniques are outlined and applied to an environmental problem concerned with marine dose assessment. The goal of both methods in this application is the estimation and prediction of the environmental variables, together with their associated probability distributions. In particular, they are used to estimate the amount of radionuclides transferred to marine biota from a given source: the British Nuclear Fuel Ltd (BNFL) repository plant in Sellafield, UK. The complexity of the processes involved, together with the large dispersion and scarcity of observations regarding radionuclide concentrations in the marine environment, require efficient data assimilation techniques. In this regard, the basic STF methods search for identifiable, linear model structures that capture the maximum amount of information contained in the data with a minimal parameterisation. They can be extended for on-line use, based on recursively updated Bayesian estimation and, although applicable to only constant or time-variable parameter (non-stationary) linear systems in the form used in this paper, they have the potential for application to non-linear systems using recently developed State Dependent Parameter (SDP) non-linear STF models. The GLUE based-methods, on the other hand, formulate the problem of estimation using a more general Bayesian approach, usually without prior statistical identification of the model structure. As a result, they are applicable to almost any linear or non-linear stochastic model, although they are much less efficient both computationally and in their use of the information contained in the observations. As expected in this particular environmental application, it is shown that the STF methods give much narrower confidence limits for the estimates due to their more efficient use of the information contained in the data. Exploiting Monte Carlo Simulation (MCS) analysis
On-line validation of linear process models using generalized likelihood ratios
International Nuclear Information System (INIS)
Tylee, J.L.
1981-12-01
A real-time method for testing the validity of linear models of nonlinear processes is described and evaluated. Using generalized likelihood ratios, the model dynamics are continually monitored to see if the process has moved far enough away from the nominal linear model operating point to justify generation of a new linear model. The method is demonstrated using a seventh-order model of a natural circulation steam generator
Directory of Open Access Journals (Sweden)
Rajat Malik
Full Text Available A class of discrete-time models of infectious disease spread, referred to as individual-level models (ILMs, are typically fitted in a Bayesian Markov chain Monte Carlo (MCMC framework. These models quantify probabilistic outcomes regarding the risk of infection of susceptible individuals due to various susceptibility and transmissibility factors, including their spatial distance from infectious individuals. The infectious pressure from infected individuals exerted on susceptible individuals is intrinsic to these ILMs. Unfortunately, quantifying this infectious pressure for data sets containing many individuals can be computationally burdensome, leading to a time-consuming likelihood calculation and, thus, computationally prohibitive MCMC-based analysis. This problem worsens when using data augmentation to allow for uncertainty in infection times. In this paper, we develop sampling methods that can be used to calculate a fast, approximate likelihood when fitting such disease models. A simple random sampling approach is initially considered followed by various spatially-stratified schemes. We test and compare the performance of our methods with both simulated data and data from the 2001 foot-and-mouth disease (FMD epidemic in the U.K. Our results indicate that substantial computation savings can be obtained--albeit, of course, with some information loss--suggesting that such techniques may be of use in the analysis of very large epidemic data sets.
Some Implications of Two Forms of the Generalized Uncertainty Principle
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Mohammed M. Khalil
2014-01-01
Full Text Available Various theories of quantum gravity predict the existence of a minimum length scale, which leads to the modification of the standard uncertainty principle to the Generalized Uncertainty Principle (GUP. In this paper, we study two forms of the GUP and calculate their implications on the energy of the harmonic oscillator and the hydrogen atom more accurately than previous studies. In addition, we show how the GUP modifies the Lorentz force law and the time-energy uncertainty principle.
Generalized uncertainty relations and characteristic invariants for the multimode states
International Nuclear Information System (INIS)
Sudarshan, E.C.G.; Chiu, C.B.; Bhamathi, G.
1995-01-01
The close relationship between the zero-point energy, the uncertainty relation, coherent states, squeezed states, and correlated states for one mode is investigated. This group theoretic perspective of the problem enables the parametrization and identification of their multimode generalization. A simple and efficient method of determining the canonical structure of the generalized correlated states is presented. Implication of canonical commutation relations for correlations are not exhausted by the Heisenberg uncertainty relation, not even by the Schroedinger-Robertson uncertainty inequality, but there are relations in the multimode case that are the generalization of the Schroedinger-Robertson relation
David, André; Petrucciani, Giovanni
2015-01-01
Using the likelihood ratio test statistic, we present a method which can be employed to test the hypothesis of a single Higgs boson using the matrix of measured signal strengths. This method can be applied in the presence of censored data and takes into account uncertainties on the measurements. The p-value against the hypothesis of a single Higgs boson is defined from the expected distribution of the test statistic, generated using pseudo-experiments. The applicability of the likelihood-based test is demonstrated using numerical examples with uncertainties and missing matrix elements.
Minimal length uncertainty and generalized non-commutative geometry
International Nuclear Information System (INIS)
Farmany, A.; Abbasi, S.; Darvishi, M.T.; Khani, F.; Naghipour, A.
2009-01-01
A generalized formulation of non-commutative geometry for the Bargmann-Fock space of quantum field theory is presented. The analysis is related to the symmetry of the simplistic space and a minimal length uncertainty.
Generalized uncertainty principles, effective Newton constant and regular black holes
Li, Xiang; Ling, Yi; Shen, You-Gen; Liu, Cheng-Zhou; He, Hong-Sheng; Xu, Lan-Fang
2016-01-01
In this paper, we explore the quantum spacetimes that are potentially connected with the generalized uncertainty principles. By analyzing the gravity-induced quantum interference pattern and the Gedanken for weighting photon, we find that the generalized uncertainty principles inspire the effective Newton constant as same as our previous proposal. A characteristic momentum associated with the tidal effect is suggested, which incorporates the quantum effect with the geometric nature of gravity...
CERN. Geneva
2015-01-01
Most physics results at the LHC end in a likelihood ratio test. This includes discovery and exclusion for searches as well as mass, cross-section, and coupling measurements. The use of Machine Learning (multivariate) algorithms in HEP is mainly restricted to searches, which can be reduced to classification between two fixed distributions: signal vs. background. I will show how we can extend the use of ML classifiers to distributions parameterized by physical quantities like masses and couplings as well as nuisance parameters associated to systematic uncertainties. This allows for one to approximate the likelihood ratio while still using a high dimensional feature vector for the data. Both the MEM and ABC approaches mentioned above aim to provide inference on model parameters (like cross-sections, masses, couplings, etc.). ABC is fundamentally tied Bayesian inference and focuses on the “likelihood free” setting where only a simulator is available and one cannot directly compute the likelihood for the dat...
Generalized production planning problem under interval uncertainty
Directory of Open Access Journals (Sweden)
Samir A. Abass
2010-06-01
Full Text Available Data in many real life engineering and economical problems suffer from inexactness. Herein we assume that we are given some intervals in which the data can simultaneously and independently perturb. We consider the generalized production planning problem with interval data. The interval data are in both of the objective function and constraints. The existing results concerning the qualitative and quantitative analysis of basic notions in parametric production planning problem. These notions are the set of feasible parameters, the solvability set and the stability set of the first kind.
The role of general relativity in the uncertainty principle
International Nuclear Information System (INIS)
Padmanabhan, T.
1986-01-01
The role played by general relativity in quantum mechanics (especially as regards the uncertainty principle) is investigated. It is confirmed that the validity of time-energy uncertainty does depend on gravitational time dilation. It is also shown that there exists an intrinsic lower bound to the accuracy with which acceleration due to gravity can be measured. The motion of equivalence principle in quantum mechanics is clarified. (author)
Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test
Madakyaru, Muddu
2017-02-16
Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale nature of process dynamics, a MSPLS algorithm combining PLS and wavelet analysis is used as modeling framework. Then, GLR hypothesis testing is applied using the uncorrelated residuals obtained from MSPLS model to improve the anomaly detection abilities of these latent variable based fault detection methods even further. Applications to a simulated distillation column data are used to evaluate the proposed MSPLS-GLR algorithm.
Improved anomaly detection using multi-scale PLS and generalized likelihood ratio test
Madakyaru, Muddu; Harrou, Fouzi; Sun, Ying
2017-01-01
Process monitoring has a central role in the process industry to enhance productivity, efficiency, and safety, and to avoid expensive maintenance. In this paper, a statistical approach that exploit the advantages of multiscale PLS models (MSPLS) and those of a generalized likelihood ratio (GLR) test to better detect anomalies is proposed. Specifically, to consider the multivariate and multi-scale nature of process dynamics, a MSPLS algorithm combining PLS and wavelet analysis is used as modeling framework. Then, GLR hypothesis testing is applied using the uncorrelated residuals obtained from MSPLS model to improve the anomaly detection abilities of these latent variable based fault detection methods even further. Applications to a simulated distillation column data are used to evaluate the proposed MSPLS-GLR algorithm.
Investigation of Free Particle Propagator with Generalized Uncertainty Problem
International Nuclear Information System (INIS)
Hassanabadi, H.; Ghobakhloo, F.
2016-01-01
We consider the Schrödinger equation with a generalized uncertainty principle for a free particle. We then transform the problem into a second-order ordinary differential equation and thereby obtain the corresponding propagator. The result of ordinary quantum mechanics is recovered for vanishing minimal length parameter.
Gauge theories under incorporation of a generalized uncertainty principle
International Nuclear Information System (INIS)
Kober, Martin
2010-01-01
There is considered an extension of gauge theories according to the assumption of a generalized uncertainty principle which implies a minimal length scale. A modification of the usual uncertainty principle implies an extended shape of matter field equations like the Dirac equation. If there is postulated invariance of such a generalized field equation under local gauge transformations, the usual covariant derivative containing the gauge potential has to be replaced by a generalized covariant derivative. This leads to a generalized interaction between the matter field and the gauge field as well as to an additional self-interaction of the gauge field. Since the existence of a minimal length scale seems to be a necessary assumption of any consistent quantum theory of gravity, the gauge principle is a constitutive ingredient of the standard model, and even gravity can be described as gauge theory of local translations or Lorentz transformations, the presented extension of gauge theories appears as a very important consideration.
Owen, Art B
2001-01-01
Empirical likelihood provides inferences whose validity does not depend on specifying a parametric model for the data. Because it uses a likelihood, the method has certain inherent advantages over resampling methods: it uses the data to determine the shape of the confidence regions, and it makes it easy to combined data from multiple sources. It also facilitates incorporating side information, and it simplifies accounting for censored, truncated, or biased sampling.One of the first books published on the subject, Empirical Likelihood offers an in-depth treatment of this method for constructing confidence regions and testing hypotheses. The author applies empirical likelihood to a range of problems, from those as simple as setting a confidence region for a univariate mean under IID sampling, to problems defined through smooth functions of means, regression models, generalized linear models, estimating equations, or kernel smooths, and to sampling with non-identically distributed data. Abundant figures offer vi...
Bonnice, W. F.; Motyka, P.; Wagner, E.; Hall, S. R.
1986-01-01
The performance of the orthogonal series generalized likelihood ratio (OSGLR) test in detecting and isolating commercial aircraft control surface and actuator failures is evaluated. A modification to incorporate age-weighting which significantly reduces the sensitivity of the algorithm to modeling errors is presented. The steady-state implementation of the algorithm based on a single linear model valid for a cruise flight condition is tested using a nonlinear aircraft simulation. A number of off-nominal no-failure flight conditions including maneuvers, nonzero flap deflections, different turbulence levels and steady winds were tested. Based on the no-failure decision functions produced by off-nominal flight conditions, the failure detection and isolation performance at the nominal flight condition was determined. The extension of the algorithm to a wider flight envelope by scheduling on dynamic pressure and flap deflection is examined. Based on this testing, the OSGLR algorithm should be capable of detecting control surface failures that would affect the safe operation of a commercial aircraft. Isolation may be difficult if there are several surfaces which produce similar effects on the aircraft. Extending the algorithm over the entire operating envelope of a commercial aircraft appears feasible.
Generalization of uncertainty relation for quantum and stochastic systems
Koide, T.; Kodama, T.
2018-06-01
The generalized uncertainty relation applicable to quantum and stochastic systems is derived within the stochastic variational method. This relation not only reproduces the well-known inequality in quantum mechanics but also is applicable to the Gross-Pitaevskii equation and the Navier-Stokes-Fourier equation, showing that the finite minimum uncertainty between the position and the momentum is not an inherent property of quantum mechanics but a common feature of stochastic systems. We further discuss the possible implication of the present study in discussing the application of the hydrodynamic picture to microscopic systems, like relativistic heavy-ion collisions.
THE GENERALIZED MAXIMUM LIKELIHOOD METHOD APPLIED TO HIGH PRESSURE PHASE EQUILIBRIUM
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Lúcio CARDOZO-FILHO
1997-12-01
Full Text Available The generalized maximum likelihood method was used to determine binary interaction parameters between carbon dioxide and components of orange essential oil. Vapor-liquid equilibrium was modeled with Peng-Robinson and Soave-Redlich-Kwong equations, using a methodology proposed in 1979 by Asselineau, Bogdanic and Vidal. Experimental vapor-liquid equilibrium data on binary mixtures formed with carbon dioxide and compounds usually found in orange essential oil were used to test the model. These systems were chosen to demonstrate that the maximum likelihood method produces binary interaction parameters for cubic equations of state capable of satisfactorily describing phase equilibrium, even for a binary such as ethanol/CO2. Results corroborate that the Peng-Robinson, as well as the Soave-Redlich-Kwong, equation can be used to describe phase equilibrium for the following systems: components of essential oil of orange/CO2.Foi empregado o método da máxima verossimilhança generalizado para determinação de parâmetros de interação binária entre os componentes do óleo essencial de laranja e dióxido de carbono. Foram usados dados experimentais de equilíbrio líquido-vapor de misturas binárias de dióxido de carbono e componentes do óleo essencial de laranja. O equilíbrio líquido-vapor foi modelado com as equações de Peng-Robinson e de Soave-Redlich-Kwong usando a metodologia proposta em 1979 por Asselineau, Bogdanic e Vidal. A escolha destes sistemas teve como objetivo demonstrar que o método da máxima verosimilhança produz parâmetros de interação binária, para equações cúbicas de estado capazes de descrever satisfatoriamente até mesmo o equilíbrio para o binário etanol/CO2. Os resultados comprovam que tanto a equação de Peng-Robinson quanto a de Soave-Redlich-Kwong podem ser empregadas para descrever o equilíbrio de fases para o sistemas: componentes do óleo essencial de laranja/CO2.
Correlated quadratures of resonance fluorescence and the generalized uncertainty relation
Arnoldus, Henk F.; George, Thomas F.; Gross, Rolf W. F.
1994-01-01
Resonance fluorescence from a two-state atom has been predicted to exhibit quadrature squeezing below the Heisenberg uncertainty limit, provided that the optical parameters (Rabi frequency, detuning, laser linewidth, etc.) are chosen carefully. When the correlation between two quadratures of the radiation field does not vanish, however, the Heisenberg limit for quantum fluctuations might be an unrealistic lower bound. A generalized uncertainty relation, due to Schroedinger, takes into account the possible correlation between the quadrature components of the radiation, and it suggests a modified definition of squeezing. We show that the coherence between the two levels of a laser-driven atom is responsible for the correlation between the quadrature components of the emitted fluorescence, and that the Schrodinger uncertainty limit increases monotonically with the coherence. On the other hand, the fluctuations in the quadrature field diminish with an increasing coherence, and can disappear completely when the coherence reaches 1/2, provided that certain phase relations hold.
“Stringy” coherent states inspired by generalized uncertainty principle
Ghosh, Subir; Roy, Pinaki
2012-05-01
Coherent States with Fractional Revival property, that explicitly satisfy the Generalized Uncertainty Principle (GUP), have been constructed in the context of Generalized Harmonic Oscillator. The existence of such states is essential in motivating the GUP based phenomenological results present in the literature which otherwise would be of purely academic interest. The effective phase space is Non-Canonical (or Non-Commutative in popular terminology). Our results have a smooth commutative limit, equivalent to Heisenberg Uncertainty Principle. The Fractional Revival time analysis yields an independent bound on the GUP parameter. Using this and similar bounds obtained here, we derive the largest possible value of the (GUP induced) minimum length scale. Mandel parameter analysis shows that the statistics is Sub-Poissonian. Correspondence Principle is deformed in an interesting way. Our computational scheme is very simple as it requires only first order corrected energy values and undeformed basis states.
“Stringy” coherent states inspired by generalized uncertainty principle
International Nuclear Information System (INIS)
Ghosh, Subir; Roy, Pinaki
2012-01-01
Coherent States with Fractional Revival property, that explicitly satisfy the Generalized Uncertainty Principle (GUP), have been constructed in the context of Generalized Harmonic Oscillator. The existence of such states is essential in motivating the GUP based phenomenological results present in the literature which otherwise would be of purely academic interest. The effective phase space is Non-Canonical (or Non-Commutative in popular terminology). Our results have a smooth commutative limit, equivalent to Heisenberg Uncertainty Principle. The Fractional Revival time analysis yields an independent bound on the GUP parameter. Using this and similar bounds obtained here, we derive the largest possible value of the (GUP induced) minimum length scale. Mandel parameter analysis shows that the statistics is Sub-Poissonian. Correspondence Principle is deformed in an interesting way. Our computational scheme is very simple as it requires only first order corrected energy values and undeformed basis states.
Bundick, W. T.
1985-01-01
The application of the Generalized Likelihood Ratio technique to the detection and identification of aircraft control element failures has been evaluated in a linear digital simulation of the longitudinal dynamics of a B-737 aircraft. Simulation results show that the technique has potential but that the effects of wind turbulence and Kalman filter model errors are problems which must be overcome.
Energy Technology Data Exchange (ETDEWEB)
Weyant, Anja; Wood-Vasey, W. Michael [Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT PACC), Physics and Astronomy Department, University of Pittsburgh, Pittsburgh, PA 15260 (United States); Schafer, Chad, E-mail: anw19@pitt.edu [Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213 (United States)
2013-02-20
Cosmological inference becomes increasingly difficult when complex data-generating processes cannot be modeled by simple probability distributions. With the ever-increasing size of data sets in cosmology, there is an increasing burden placed on adequate modeling; systematic errors in the model will dominate where previously these were swamped by statistical errors. For example, Gaussian distributions are an insufficient representation for errors in quantities like photometric redshifts. Likewise, it can be difficult to quantify analytically the distribution of errors that are introduced in complex fitting codes. Without a simple form for these distributions, it becomes difficult to accurately construct a likelihood function for the data as a function of parameters of interest. Approximate Bayesian computation (ABC) provides a means of probing the posterior distribution when direct calculation of a sufficiently accurate likelihood is intractable. ABC allows one to bypass direct calculation of the likelihood but instead relies upon the ability to simulate the forward process that generated the data. These simulations can naturally incorporate priors placed on nuisance parameters, and hence these can be marginalized in a natural way. We present and discuss ABC methods in the context of supernova cosmology using data from the SDSS-II Supernova Survey. Assuming a flat cosmology and constant dark energy equation of state, we demonstrate that ABC can recover an accurate posterior distribution. Finally, we show that ABC can still produce an accurate posterior distribution when we contaminate the sample with Type IIP supernovae.
International Nuclear Information System (INIS)
Weyant, Anja; Wood-Vasey, W. Michael; Schafer, Chad
2013-01-01
Cosmological inference becomes increasingly difficult when complex data-generating processes cannot be modeled by simple probability distributions. With the ever-increasing size of data sets in cosmology, there is an increasing burden placed on adequate modeling; systematic errors in the model will dominate where previously these were swamped by statistical errors. For example, Gaussian distributions are an insufficient representation for errors in quantities like photometric redshifts. Likewise, it can be difficult to quantify analytically the distribution of errors that are introduced in complex fitting codes. Without a simple form for these distributions, it becomes difficult to accurately construct a likelihood function for the data as a function of parameters of interest. Approximate Bayesian computation (ABC) provides a means of probing the posterior distribution when direct calculation of a sufficiently accurate likelihood is intractable. ABC allows one to bypass direct calculation of the likelihood but instead relies upon the ability to simulate the forward process that generated the data. These simulations can naturally incorporate priors placed on nuisance parameters, and hence these can be marginalized in a natural way. We present and discuss ABC methods in the context of supernova cosmology using data from the SDSS-II Supernova Survey. Assuming a flat cosmology and constant dark energy equation of state, we demonstrate that ABC can recover an accurate posterior distribution. Finally, we show that ABC can still produce an accurate posterior distribution when we contaminate the sample with Type IIP supernovae.
Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models
Mesters, G.; Koopman, S.J.; Ooms, M.
2016-01-01
An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance sampling and on a linear Gaussian approximating
Generalized uncertainty principle, quantum gravity and Horava-Lifshitz gravity
International Nuclear Information System (INIS)
Myung, Yun Soo
2009-01-01
We investigate a close connection between generalized uncertainty principle (GUP) and deformed Horava-Lifshitz (HL) gravity. The GUP commutation relations correspond to the UV-quantum theory, while the canonical commutation relations represent the IR-quantum theory. Inspired by this UV/IR quantum mechanics, we obtain the GUP-corrected graviton propagator by introducing UV-momentum p i =p 0i (1+βp 0 2 ) and compare this with tensor propagators in the HL gravity. Two are the same up to p 0 4 -order.
Craciunescu, Teddy; Peluso, Emmanuele; Murari, Andrea; Gelfusa, Michela; JET Contributors
2018-05-01
The total emission of radiation is a crucial quantity to calculate the power balances and to understand the physics of any Tokamak. Bolometric systems are the main tool to measure this important physical quantity through quite sophisticated tomographic inversion methods. On the Joint European Torus, the coverage of the bolometric diagnostic, due to the availability of basically only two projection angles, is quite limited, rendering the inversion a very ill-posed mathematical problem. A new approach, based on the maximum likelihood, has therefore been developed and implemented to alleviate one of the major weaknesses of traditional tomographic techniques: the difficulty to determine routinely the confidence intervals in the results. The method has been validated by numerical simulations with phantoms to assess the quality of the results and to optimise the configuration of the parameters for the main types of emissivity encountered experimentally. The typical levels of statistical errors, which may significantly influence the quality of the reconstructions, have been identified. The systematic tests with phantoms indicate that the errors in the reconstructions are quite limited and their effect on the total radiated power remains well below 10%. A comparison with other approaches to the inversion and to the regularization has also been performed.
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Masood, Syed [Department of Physics, International Islamic University, H-10 Sector, Islamabad (Pakistan); Faizal, Mir, E-mail: mirfaizalmir@gmail.com [Irving K. Barber School of Arts and Sciences, University of British Columbia – Okanagan, Kelowna, BC V1V 1V7 (Canada); Department of Physics and Astronomy, University of Lethbridge, Lethbridge, AB T1K 3M4 (Canada); Zaz, Zaid [Department of Electronics and Communication Engineering, University of Kashmir, Srinagar, Kashmir, 190006 (India); Ali, Ahmed Farag [Department of Physics, Faculty of Science, Benha University, Benha, 13518 (Egypt); Raza, Jamil [Department of Physics, International Islamic University, H-10 Sector, Islamabad (Pakistan); Shah, Mushtaq B. [Department of Physics, National Institute of Technology, Srinagar, Kashmir, 190006 (India)
2016-12-10
In this paper, we will propose the most general form of the deformation of Heisenberg algebra motivated by the generalized uncertainty principle. This deformation of the Heisenberg algebra will deform all quantum mechanical systems. The form of the generalized uncertainty principle used to motivate these results will be motivated by the space fractional quantum mechanics, and non-locality in quantum mechanical systems. We also analyse a specific limit of this generalized deformation for one dimensional system, and in that limit, a nonlocal deformation of the momentum operator generates a local deformation of all one dimensional quantum mechanical systems. We analyse the low energy effects of this deformation on a harmonic oscillator, Landau levels, Lamb shift, and potential barrier. We also demonstrate that this deformation leads to a discretization of space.
International Nuclear Information System (INIS)
Masood, Syed; Faizal, Mir; Zaz, Zaid; Ali, Ahmed Farag; Raza, Jamil; Shah, Mushtaq B.
2016-01-01
In this paper, we will propose the most general form of the deformation of Heisenberg algebra motivated by the generalized uncertainty principle. This deformation of the Heisenberg algebra will deform all quantum mechanical systems. The form of the generalized uncertainty principle used to motivate these results will be motivated by the space fractional quantum mechanics, and non-locality in quantum mechanical systems. We also analyse a specific limit of this generalized deformation for one dimensional system, and in that limit, a nonlocal deformation of the momentum operator generates a local deformation of all one dimensional quantum mechanical systems. We analyse the low energy effects of this deformation on a harmonic oscillator, Landau levels, Lamb shift, and potential barrier. We also demonstrate that this deformation leads to a discretization of space.
Bueno, R. A.
1977-01-01
Results of the generalized likelihood ratio (GLR) technique for the detection of failures in aircraft application are presented, and its relationship to the properties of the Kalman-Bucy filter is examined. Under the assumption that the system is perfectly modeled, the detectability and distinguishability of four failure types are investigated by means of analysis and simulations. Detection of failures is found satisfactory, but problems in identifying correctly the mode of a failure may arise. These issues are closely examined as well as the sensitivity of GLR to modeling errors. The advantages and disadvantages of this technique are discussed, and various modifications are suggested to reduce its limitations in performance and computational complexity.
Horizon Wavefunction of Generalized Uncertainty Principle Black Holes
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Luciano Manfredi
2016-01-01
Full Text Available We study the Horizon Wavefunction (HWF description of a Generalized Uncertainty Principle inspired metric that admits sub-Planckian black holes, where the black hole mass m is replaced by M=m1+β/2MPl2/m2. Considering the case of a wave-packet shaped by a Gaussian distribution, we compute the HWF and the probability PBH that the source is a (quantum black hole, that is, that it lies within its horizon radius. The case β0, where a minimum in PBH is encountered, thus meaning that every particle has some probability of decaying to a black hole. Furthermore, for sufficiently large β we find that every particle is a quantum black hole, in agreement with the intuitive effect of increasing β, which creates larger M and RH terms. This is likely due to a “dimensional reduction” feature of the model, where the black hole characteristics for sub-Planckian black holes mimic those in (1+1 dimensions and the horizon size grows as RH~M-1.
Incorporating Nuisance Parameters in Likelihoods for Multisource Spectra
Conway, J.S.
2011-01-01
We describe here the general mathematical approach to constructing likelihoods for fitting observed spectra in one or more dimensions with multiple sources, including the effects of systematic uncertainties represented as nuisance parameters, when the likelihood is to be maximized with respect to these parameters. We consider three types of nuisance parameters: simple multiplicative factors, source spectra "morphing" parameters, and parameters representing statistical uncertainties in the predicted source spectra.
GENERAL RISKS AND UNCERTAINTIES OF REPORTING AND MANAGEMENT REPORTING RISKS
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CAMELIA I. LUNGU
2011-04-01
Full Text Available Purpose: Highlighting risks and uncertainties reporting based on a literature review research. Objectives: The delimitation of risk management models and uncertainties in fundamental research. Research method: Fundamental research study directed to identify the relevant risks’ models presented in entities’ financial statements. Uncertainty is one of the fundamental coordinates of our world. As showed J.K. Galbraith (1978, the world now lives under the age of uncertainty. Moreover, we can say that contemporary society development could be achieved by taking decisions under uncertainty, though, risks. Growing concern for the study of uncertainty, its effects and precautions led to the rather recent emergence of a new science, science of hazards (les cindyniques - l.fr. (Kenvern, 1991. Current analysis of risk are dominated by Beck’s (1992 notion that a risk society now exists whereby we have become more concerned about our impact upon nature than the impact of nature upon us. Clearly, risk permeates most aspects of corporate but also of regular life decision-making and few can predict with any precision the future. The risk is almost always a major variable in real-world corporate decision-making, and managers that ignore it are in a real peril. In these circumstances, a possible answer is assuming financial discipline with an appropriate system of incentives.
Kling, Daniel; Tillmar, Andreas; Egeland, Thore; Mostad, Petter
2015-09-01
Several applications necessitate an unbiased determination of relatedness, be it in linkage or association studies or in a forensic setting. An appropriate model to compute the joint probability of some genetic data for a set of persons given some hypothesis about the pedigree structure is then required. The increasing number of markers available through high-density SNP microarray typing and NGS technologies intensifies the demand, where using a large number of markers may lead to biased results due to strong dependencies between closely located loci, both within pedigrees (linkage) and in the population (allelic association or linkage disequilibrium (LD)). We present a new general model, based on a Markov chain for inheritance patterns and another Markov chain for founder allele patterns, the latter allowing us to account for LD. We also demonstrate a specific implementation for X chromosomal markers that allows for computation of likelihoods based on hypotheses of alleged relationships and genetic marker data. The algorithm can simultaneously account for linkage, LD, and mutations. We demonstrate its feasibility using simulated examples. The algorithm is implemented in the software FamLinkX, providing a user-friendly GUI for Windows systems (FamLinkX, as well as further usage instructions, is freely available at www.famlink.se ). Our software provides the necessary means to solve cases where no previous implementation exists. In addition, the software has the possibility to perform simulations in order to further study the impact of linkage and LD on computed likelihoods for an arbitrary set of markers.
Galili, Tal; Meilijson, Isaac
2016-01-02
The Rao-Blackwell theorem offers a procedure for converting a crude unbiased estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood estimator is inefficient, and an unbiased generalized Bayes estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised September 2015.].
Mandal, Shyamapada; Santhi, B.; Sridhar, S.; Vinolia, K.; Swaminathan, P.
2017-06-01
In this paper, an online fault detection and classification method is proposed for thermocouples used in nuclear power plants. In the proposed method, the fault data are detected by the classification method, which classifies the fault data from the normal data. Deep belief network (DBN), a technique for deep learning, is applied to classify the fault data. The DBN has a multilayer feature extraction scheme, which is highly sensitive to a small variation of data. Since the classification method is unable to detect the faulty sensor; therefore, a technique is proposed to identify the faulty sensor from the fault data. Finally, the composite statistical hypothesis test, namely generalized likelihood ratio test, is applied to compute the fault pattern of the faulty sensor signal based on the magnitude of the fault. The performance of the proposed method is validated by field data obtained from thermocouple sensors of the fast breeder test reactor.
Cooke, Georga; Tapley, Amanda; Holliday, Elizabeth; Morgan, Simon; Henderson, Kim; Ball, Jean; van Driel, Mieke; Spike, Neil; Kerr, Rohan; Magin, Parker
2017-12-01
Tolerance for ambiguity is essential for optimal learning and professional competence. General practice trainees must be, or must learn to be, adept at managing clinical uncertainty. However, few studies have examined associations of intolerance of uncertainty in this group. The aim of this study was to establish levels of tolerance of uncertainty in Australian general practice trainees and associations of uncertainty with demographic, educational and training practice factors. A cross-sectional analysis was performed on the Registrar Clinical Encounters in Training (ReCEnT) project, an ongoing multi-site cohort study. Scores on three of the four independent subscales of the Physicians' Reaction to Uncertainty (PRU) instrument were analysed as outcome variables in linear regression models with trainee and practice factors as independent variables. A total of 594 trainees contributed data on a total of 1209 occasions. Trainees in earlier training terms had higher scores for 'Anxiety due to uncertainty', 'Concern about bad outcomes' and 'Reluctance to disclose diagnosis/treatment uncertainty to patients'. Beyond this, findings suggest two distinct sets of associations regarding reaction to uncertainty. Firstly, affective aspects of uncertainty (the 'Anxiety' and 'Concern' subscales) were associated with female gender, less experience in hospital prior to commencing general practice training, and graduation overseas. Secondly, a maladaptive response to uncertainty (the 'Reluctance to disclose' subscale) was associated with urban practice, health qualifications prior to studying medicine, practice in an area of higher socio-economic status, and being Australian-trained. This study has established levels of three measures of trainees' responses to uncertainty and associations with these responses. The current findings suggest differing 'phenotypes' of trainees with high 'affective' responses to uncertainty and those reluctant to disclose uncertainty to patients. More
Supersymmetry Breaking as a new source for the Generalized Uncertainty Principle
Faizal, Mir
2016-01-01
In this letter, we will demonstrate that the breaking of supersymmetry by a non-anticommutative deformation can be used to generate the generalized uncertainty principle. We will analyze the physical reasons for this observation, in the framework of string theory. We also discuss the relation between the generalized uncertainty principle and the Lee–Wick field theories.
Supersymmetry breaking as a new source for the generalized uncertainty principle
Energy Technology Data Exchange (ETDEWEB)
Faizal, Mir, E-mail: mirfaizalmir@gmail.com
2016-06-10
In this letter, we will demonstrate that the breaking of supersymmetry by a non-anticommutative deformation can be used to generate the generalized uncertainty principle. We will analyze the physical reasons for this observation, in the framework of string theory. We also discuss the relation between the generalized uncertainty principle and the Lee–Wick field theories.
Directory of Open Access Journals (Sweden)
Katarzyna A Dembek
Full Text Available BACKGROUND: Medical management of critically ill equine neonates (foals can be expensive and labor intensive. Predicting the odds of foal survival using clinical information could facilitate the decision-making process for owners and clinicians. Numerous prognostic indicators and mathematical models to predict outcome in foals have been published; however, a validated scoring method to predict survival in sick foals has not been reported. The goal of this study was to develop and validate a scoring system that can be used by clinicians to predict likelihood of survival of equine neonates based on clinical data obtained on admission. METHODS AND RESULTS: Data from 339 hospitalized foals of less than four days of age admitted to three equine hospitals were included to develop the model. Thirty seven variables including historical information, physical examination and laboratory findings were analyzed by generalized boosted regression modeling (GBM to determine which ones would be included in the survival score. Of these, six variables were retained in the final model. The weight for each variable was calculated using a generalized linear model and the probability of survival for each total score was determined. The highest (7 and the lowest (0 scores represented 97% and 3% probability of survival, respectively. Accuracy of this survival score was validated in a prospective study on data from 283 hospitalized foals from the same three hospitals. Sensitivity, specificity, positive and negative predictive values for the survival score in the prospective population were 96%, 71%, 91%, and 85%, respectively. CONCLUSIONS: The survival score developed in our study was validated in a large number of foals with a wide range of diseases and can be easily implemented using data available in most equine hospitals. GBM was a useful tool to develop the survival score. Further evaluations of this scoring system in field conditions are needed.
Squeezed states of the generalized minimum uncertainty state for the Caldirola-Kanai Hamiltonian
International Nuclear Information System (INIS)
Kim, Sang Pyo
2003-01-01
We show that the ground state of the well-known pseudo-stationary states for the Caldirola-Kanai Hamiltonian is a generalized minimum uncertainty state, which has the minimum allowed uncertainty ΔqΔp = ℎσ 0 /2, where σ 0 (≥1) is a constant depending on the damping factor and natural frequency. The most general symmetric Gaussian states are obtained as the one-parameter squeezed states of the pseudo-stationary ground state. It is further shown that the coherent states of the pseudo-stationary ground state constitute another class of the generalized minimum uncertainty states
Project management under uncertainty beyond beta: The generalized bicubic distribution
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José García Pérez
2016-01-01
Full Text Available The beta distribution has traditionally been employed in the PERT methodology and generally used for modeling bounded continuous random variables based on expert’s judgment. The impossibility of estimating four parameters from the three values provided by the expert when the beta distribution is assumed to be the underlying distribution has been widely debated. This paper presents the generalized bicubic distribution as a good alternative to the beta distribution since, when the variance depends on the mode, the generalized bicubic distribution approximates the kurtosis of the Gaussian distribution better than the beta distribution. In addition, this distribution presents good properties in the PERT methodology in relation to moderation and conservatism criteria. Two empirical applications are presented to demonstrate the adequateness of this new distribution.
Uncertainty Analysis of Few Group Cross Sections Based on Generalized Perturbation Theory
International Nuclear Information System (INIS)
Han, Tae Young; Lee, Hyun Chul; Noh, Jae Man
2014-01-01
In this paper, the methodology of the sensitivity and uncertainty analysis code based on GPT was described and the preliminary verification calculations on the PMR200 pin cell problem were carried out. As a result, they are in a good agreement when compared with the results by TSUNAMI. From this study, it is expected that MUSAD code based on GPT can produce the uncertainty of the homogenized few group microscopic cross sections for a core simulator. For sensitivity and uncertainty analyses for general core responses, a two-step method is available and it utilizes the generalized perturbation theory (GPT) for homogenized few group cross sections in the first step and stochastic sampling method for general core responses in the second step. The uncertainty analysis procedure based on GPT in the first step needs the generalized adjoint solution from a cell or lattice code. For this, the generalized adjoint solver has been integrated into DeCART in our previous work. In this paper, MUSAD (Modues of Uncertainty and Sensitivity Analysis for DeCART) code based on the classical perturbation theory was expanded to the function of the sensitivity and uncertainty analysis for few group cross sections based on GPT. First, the uncertainty analysis method based on GPT was described and, in the next section, the preliminary results of the verification calculation on a VHTR pin cell problem were compared with the results by TSUNAMI of SCALE 6.1
Directory of Open Access Journals (Sweden)
Herwig Reiter
2010-01-01
Full Text Available The article proposes a general, empirically grounded model for analyzing biographical uncertainty. The model is based on findings from a qualitative-explorative study of transforming meanings of unemployment among young people in post-Soviet Lithuania. In a first step, the particular features of the uncertainty puzzle in post-communist youth transitions are briefly discussed. A historical event like the collapse of state socialism in Europe, similar to the recent financial and economic crisis, is a generator of uncertainty par excellence: it undermines the foundations of societies and the taken-for-grantedness of related expectations. Against this background, the case of a young woman and how she responds to the novel threat of unemployment in the transition to the world of work is introduced. Her uncertainty management in the specific time perspective of certainty production is then conceptually rephrased by distinguishing three types or levels of biographical uncertainty: knowledge, outcome, and recognition uncertainty. Biographical uncertainty, it is argued, is empirically observable through the analysis of acting and projecting at the biographical level. The final part synthesizes the empirical findings and the conceptual discussion into a stratification model of biographical uncertainty as a general tool for the biographical analysis of uncertainty phenomena. URN: urn:nbn:de:0114-fqs100120
Indian Academy of Sciences (India)
To reflect this uncertainty in the climate scenarios, the use of AOGCMs that explicitly simulate the carbon cycle and chemistry of all the substances are needed. The Hadley Centre has developed a version of the climate model that allows the effect of climate change on the carbon cycle and its feedback into climate, to be ...
International Nuclear Information System (INIS)
Silva, T.A. da
1988-01-01
The comparison between the uncertainty method recommended by International Atomic Energy Agency (IAEA) and the and the International Weight and Measure Commitee (CIPM) are showed, for the calibration of clinical dosimeters in the secondary standard Dosimetry Laboratory (SSDL). (C.G.C.) [pt
Generalized Uncertainty Quantification for Linear Inverse Problems in X-ray Imaging
Energy Technology Data Exchange (ETDEWEB)
Fowler, Michael James [Clarkson Univ., Potsdam, NY (United States)
2014-04-25
In industrial and engineering applications, X-ray radiography has attained wide use as a data collection protocol for the assessment of material properties in cases where direct observation is not possible. The direct measurement of nuclear materials, particularly when they are under explosive or implosive loading, is not feasible, and radiography can serve as a useful tool for obtaining indirect measurements. In such experiments, high energy X-rays are pulsed through a scene containing material of interest, and a detector records a radiograph by measuring the radiation that is not attenuated in the scene. One approach to the analysis of these radiographs is to model the imaging system as an operator that acts upon the object being imaged to produce a radiograph. In this model, the goal is to solve an inverse problem to reconstruct the values of interest in the object, which are typically material properties such as density or areal density. The primary objective in this work is to provide quantitative solutions with uncertainty estimates for three separate applications in X-ray radiography: deconvolution, Abel inversion, and radiation spot shape reconstruction. For each problem, we introduce a new hierarchical Bayesian model for determining a posterior distribution on the unknowns and develop efficient Markov chain Monte Carlo (MCMC) methods for sampling from the posterior. A Poisson likelihood, based on a noise model for photon counts at the detector, is combined with a prior tailored to each application: an edge-localizing prior for deconvolution; a smoothing prior with non-negativity constraints for spot reconstruction; and a full covariance sampling prior based on a Wishart hyperprior for Abel inversion. After developing our methods in a general setting, we demonstrate each model on both synthetically generated datasets, including those from a well known radiation transport code, and real high energy radiographs taken at two U. S. Department of Energy
Generalized uncertainty principle as a consequence of the effective field theory
Energy Technology Data Exchange (ETDEWEB)
Faizal, Mir, E-mail: mirfaizalmir@gmail.com [Irving K. Barber School of Arts and Sciences, University of British Columbia – Okanagan, Kelowna, British Columbia V1V 1V7 (Canada); Department of Physics and Astronomy, University of Lethbridge, Lethbridge, Alberta T1K 3M4 (Canada); Ali, Ahmed Farag, E-mail: ahmed.ali@fsc.bu.edu.eg [Department of Physics, Faculty of Science, Benha University, Benha, 13518 (Egypt); Netherlands Institute for Advanced Study, Korte Spinhuissteeg 3, 1012 CG Amsterdam (Netherlands); Nassar, Ali, E-mail: anassar@zewailcity.edu.eg [Department of Physics, Zewail City of Science and Technology, 12588, Giza (Egypt)
2017-02-10
We will demonstrate that the generalized uncertainty principle exists because of the derivative expansion in the effective field theories. This is because in the framework of the effective field theories, the minimum measurable length scale has to be integrated away to obtain the low energy effective action. We will analyze the deformation of a massive free scalar field theory by the generalized uncertainty principle, and demonstrate that the minimum measurable length scale corresponds to a second more massive scale in the theory, which has been integrated away. We will also analyze CFT operators dual to this deformed scalar field theory, and observe that scaling of the new CFT operators indicates that they are dual to this more massive scale in the theory. We will use holographic renormalization to explicitly calculate the renormalized boundary action with counter terms for this scalar field theory deformed by generalized uncertainty principle, and show that the generalized uncertainty principle contributes to the matter conformal anomaly.
Generalized uncertainty principle as a consequence of the effective field theory
Directory of Open Access Journals (Sweden)
Mir Faizal
2017-02-01
Full Text Available We will demonstrate that the generalized uncertainty principle exists because of the derivative expansion in the effective field theories. This is because in the framework of the effective field theories, the minimum measurable length scale has to be integrated away to obtain the low energy effective action. We will analyze the deformation of a massive free scalar field theory by the generalized uncertainty principle, and demonstrate that the minimum measurable length scale corresponds to a second more massive scale in the theory, which has been integrated away. We will also analyze CFT operators dual to this deformed scalar field theory, and observe that scaling of the new CFT operators indicates that they are dual to this more massive scale in the theory. We will use holographic renormalization to explicitly calculate the renormalized boundary action with counter terms for this scalar field theory deformed by generalized uncertainty principle, and show that the generalized uncertainty principle contributes to the matter conformal anomaly.
The 13th Malaysian general election: Uncertainties and expectations
M. Moniruzzaman
2013-01-01
Malaysia held its 13th general election on May 5, 2013 which was a contest between two coalitions, the ruling Barisan Nasional (BN) and the opposition Pakatan Rakyat (PR). The latter’s expectations for an outright win did not materialise. The election results have shown a rather status quo with minor losses and gains for the two coalitions. This study analysed the 13th election and found a number of noticeable trends. The Chinese voters have voted overwhelmingly for the opposition. The politi...
The 13th Malaysian general election: Uncertainties and expectations
Directory of Open Access Journals (Sweden)
M. Moniruzzaman
2013-06-01
Full Text Available Malaysia held its 13th general election on May 5, 2013 which was a contest between two coalitions, the ruling Barisan Nasional (BN and the opposition Pakatan Rakyat (PR. The latter’s expectations for an outright win did not materialise. The election results have shown a rather status quo with minor losses and gains for the two coalitions. This study analysed the 13th election and found a number of noticeable trends. The Chinese voters have voted overwhelmingly for the opposition. The political culture of Malaysia is also shifting toward more participatory type with increased social and economic mobility. The days of one-party dominance in Malaysia are apparently over. The electoral politics in Malaysia might become more polarised along ethnic lines which may require reshuffling of its coalition framework and design.
Directory of Open Access Journals (Sweden)
Cooke Georga PE
2013-01-01
Full Text Available Abstract Background Burnout and intolerance of uncertainty have been linked to low job satisfaction and lower quality patient care. While resilience is related to these concepts, no study has examined these three concepts in a cohort of doctors. The objective of this study was to measure resilience, burnout, compassion satisfaction, personal meaning in patient care and intolerance of uncertainty in Australian general practice (GP registrars. Methods We conducted a paper-based cross-sectional survey of GP registrars in Australia from June to July 2010, recruited from a newsletter item or registrar education events. Survey measures included the Resilience Scale-14, a single-item scale for burnout, Professional Quality of Life (ProQOL scale, Personal Meaning in Patient Care scale, Intolerance of Uncertainty-12 scale, and Physician Response to Uncertainty scale. Results 128 GP registrars responded (response rate 90%. Fourteen percent of registrars were found to be at risk of burnout using the single-item scale for burnout, but none met the criteria for burnout using the ProQOL scale. Secondary traumatic stress, general intolerance of uncertainty, anxiety due to clinical uncertainty and reluctance to disclose uncertainty to patients were associated with being at higher risk of burnout, but sex, age, practice location, training duration, years since graduation, and reluctance to disclose uncertainty to physicians were not. Only ten percent of registrars had high resilience scores. Resilience was positively associated with compassion satisfaction and personal meaning in patient care. Resilience was negatively associated with burnout, secondary traumatic stress, inhibitory anxiety, general intolerance to uncertainty, concern about bad outcomes and reluctance to disclose uncertainty to patients. Conclusions GP registrars in this survey showed a lower level of burnout than in other recent surveys of the broader junior doctor population in both Australia
Generalized Bootstrap Method for Assessment of Uncertainty in Semivariogram Inference
Olea, R.A.; Pardo-Iguzquiza, E.
2011-01-01
The semivariogram and its related function, the covariance, play a central role in classical geostatistics for modeling the average continuity of spatially correlated attributes. Whereas all methods are formulated in terms of the true semivariogram, in practice what can be used are estimated semivariograms and models based on samples. A generalized form of the bootstrap method to properly model spatially correlated data is used to advance knowledge about the reliability of empirical semivariograms and semivariogram models based on a single sample. Among several methods available to generate spatially correlated resamples, we selected a method based on the LU decomposition and used several examples to illustrate the approach. The first one is a synthetic, isotropic, exhaustive sample following a normal distribution, the second example is also a synthetic but following a non-Gaussian random field, and a third empirical sample consists of actual raingauge measurements. Results show wider confidence intervals than those found previously by others with inadequate application of the bootstrap. Also, even for the Gaussian example, distributions for estimated semivariogram values and model parameters are positively skewed. In this sense, bootstrap percentile confidence intervals, which are not centered around the empirical semivariogram and do not require distributional assumptions for its construction, provide an achieved coverage similar to the nominal coverage. The latter cannot be achieved by symmetrical confidence intervals based on the standard error, regardless if the standard error is estimated from a parametric equation or from bootstrap. ?? 2010 International Association for Mathematical Geosciences.
Acar, Elif F.; Sun, Lei
2012-01-01
Motivated by genetic association studies of SNPs with genotype uncertainty, we propose a generalization of the Kruskal-Wallis test that incorporates group uncertainty when comparing k samples. The extended test statistic is based on probability-weighted rank-sums and follows an asymptotic chi-square distribution with k-1 degrees of freedom under the null hypothesis. Simulation studies confirm the validity and robustness of the proposed test in finite samples. Application to a genome-wide asso...
International Nuclear Information System (INIS)
Wall, M.J.W.
1992-01-01
The notion of open-quotes probabilityclose quotes is generalized to that of open-quotes likelihood,close quotes and a natural logical structure is shown to exist for any physical theory which predicts likelihoods. Two physically based axioms are given for this logical structure to form an orthomodular poset, with an order-determining set of states. The results strengthen the basis of the quantum logic approach to axiomatic quantum theory. 25 refs
Generalized uncertainty principle and entropy of three-dimensional rotating acoustic black hole
International Nuclear Information System (INIS)
Zhao, HuiHua; Li, GuangLiang; Zhang, LiChun
2012-01-01
Using the new equation of state density from the generalized uncertainty principle, we investigate statistics entropy of a 3-dimensional rotating acoustic black hole. When λ introduced in the generalized uncertainty principle takes a specific value, we obtain an area entropy and a correction term associated with the acoustic black hole. In this method, there does not exist any divergence and one needs not the small mass approximation in the original brick-wall model. -- Highlights: ► Statistics entropy of a 3-dimensional rotating acoustic black hole is studied. ► We obtain an area entropy and a correction term associated with it. ► We make λ introduced in the generalized uncertainty principle take a specific value. ► There does not exist any divergence in this method.
Acar, Elif F; Sun, Lei
2013-06-01
Motivated by genetic association studies of SNPs with genotype uncertainty, we propose a generalization of the Kruskal-Wallis test that incorporates group uncertainty when comparing k samples. The extended test statistic is based on probability-weighted rank-sums and follows an asymptotic chi-square distribution with k - 1 degrees of freedom under the null hypothesis. Simulation studies confirm the validity and robustness of the proposed test in finite samples. Application to a genome-wide association study of type 1 diabetic complications further demonstrates the utilities of this generalized Kruskal-Wallis test for studies with group uncertainty. The method has been implemented as an open-resource R program, GKW. © 2013, The International Biometric Society.
Wu, Baolin; Guan, Weihua
2015-06-01
Acar and Sun (2013, Biometrics 69, 427-435) presented a generalized Kruskal-Wallis (GKW) test for genetic association studies that incorporated the genotype uncertainty and showed its robust and competitive performance compared to existing methods. We present another interesting way to derive the GKW test via a rank linear model. © 2014, The International Biometric Society.
Wu, Baolin; Guan, Weihua
2014-01-01
Acar and Sun (2013, Biometrics, 69, 427-435) presented a generalized Kruskal-Wallis (GKW) test for genetic association studies that incorporated the genotype uncertainty and showed its robust and competitive performance compared to existing methods. We present another interesting way to derive the GKW test via a rank linear model.
Earthquake likelihood model testing
Schorlemmer, D.; Gerstenberger, M.C.; Wiemer, S.; Jackson, D.D.; Rhoades, D.A.
2007-01-01
wide range of possible testing procedures exist. Jolliffe and Stephenson (2003) present different forecast verifications from atmospheric science, among them likelihood testing of probability forecasts and testing the occurrence of binary events. Testing binary events requires that for each forecasted event, the spatial, temporal and magnitude limits be given. Although major earthquakes can be considered binary events, the models within the RELM project express their forecasts on a spatial grid and in 0.1 magnitude units; thus the results are a distribution of rates over space and magnitude. These forecasts can be tested with likelihood tests.In general, likelihood tests assume a valid null hypothesis against which a given hypothesis is tested. The outcome is either a rejection of the null hypothesis in favor of the test hypothesis or a nonrejection, meaning the test hypothesis cannot outperform the null hypothesis at a given significance level. Within RELM, there is no accepted null hypothesis and thus the likelihood test needs to be expanded to allow comparable testing of equipollent hypotheses.To test models against one another, we require that forecasts are expressed in a standard format: the average rate of earthquake occurrence within pre-specified limits of hypocentral latitude, longitude, depth, magnitude, time period, and focal mechanisms. Focal mechanisms should either be described as the inclination of P-axis, declination of P-axis, and inclination of the T-axis, or as strike, dip, and rake angles. Schorlemmer and Gerstenberger (2007, this issue) designed classes of these parameters such that similar models will be tested against each other. These classes make the forecasts comparable between models. Additionally, we are limited to testing only what is precisely defined and consistently reported in earthquake catalogs. Therefore it is currently not possible to test such information as fault rupture length or area, asperity location, etc. Also, to account
Generalized uncertainty principle and the maximum mass of ideal white dwarfs
Energy Technology Data Exchange (ETDEWEB)
Rashidi, Reza, E-mail: reza.rashidi@srttu.edu
2016-11-15
The effects of a generalized uncertainty principle on the structure of an ideal white dwarf star is investigated. The equation describing the equilibrium configuration of the star is a generalized form of the Lane–Emden equation. It is proved that the star always has a finite size. It is then argued that the maximum mass of such an ideal white dwarf tends to infinity, as opposed to the conventional case where it has a finite value.
The phylogenetic likelihood library.
Flouri, T; Izquierdo-Carrasco, F; Darriba, D; Aberer, A J; Nguyen, L-T; Minh, B Q; Von Haeseler, A; Stamatakis, A
2015-03-01
We introduce the Phylogenetic Likelihood Library (PLL), a highly optimized application programming interface for developing likelihood-based phylogenetic inference and postanalysis software. The PLL implements appropriate data structures and functions that allow users to quickly implement common, error-prone, and labor-intensive tasks, such as likelihood calculations, model parameter as well as branch length optimization, and tree space exploration. The highly optimized and parallelized implementation of the phylogenetic likelihood function and a thorough documentation provide a framework for rapid development of scalable parallel phylogenetic software. By example of two likelihood-based phylogenetic codes we show that the PLL improves the sequential performance of current software by a factor of 2-10 while requiring only 1 month of programming time for integration. We show that, when numerical scaling for preventing floating point underflow is enabled, the double precision likelihood calculations in the PLL are up to 1.9 times faster than those in BEAGLE. On an empirical DNA dataset with 2000 taxa the AVX version of PLL is 4 times faster than BEAGLE (scaling enabled and required). The PLL is available at http://www.libpll.org under the GNU General Public License (GPL). © The Author(s) 2014. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.
Communicating and dealing with uncertainty in general practice: the association with neuroticism.
Directory of Open Access Journals (Sweden)
Antonius Schneider
Full Text Available Diagnostic reasoning in primary care setting where presented problems and patients are mostly unselected appears as a complex process. The aim was to develop a questionnaire to describe how general practitioners (GPs deal with uncertainty to gain more insight into the decisional process. The association of personality traits with medical decision making was investigated additionally.Raw items were identified by literature research and focus group. Items were improved by interviewing ten GPs with thinking-aloud-method. A personal case vignette related to a complex and uncertainty situation was introduced. The final questionnaire was administered to 228 GPs in Germany. Factorial validity was calculated with explorative and confirmatory factor analysis. The results of the Communicating and Dealing with Uncertainty (CoDU-questionnaire were compared with the scales of the 'Physician Reaction to Uncertainty' (PRU questionnaire and with the personality traits which were determined with the Big Five Inventory (BFI-K.The items could be assigned to four scales with varying internal consistency, namely 'communicating uncertainty' (Cronbach alpha 0.79, 'diagnostic action' (0.60, 'intuition' (0.39 and 'extended social anamnesis' (0.69. Neuroticism was positively associated with all PRU scales 'anxiety due to uncertainty' (Pearson correlation 0.487, 'concerns about bad outcomes' (0.488, 'reluctance to disclose uncertainty to patients' (0.287, 'reluctance to disclose mistakes to physicians' (0.212 and negatively associated with the CoDU scale 'communicating uncertainty' (-0.242 (p<0.01 for all. 'Extraversion' (0.146; p<0.05, 'agreeableness' (0.145, p<0.05, 'conscientiousness' (0.168, p<0.05 and 'openness to experience' (0.186, p<0.01 were significantly positively associated with 'communicating uncertainty'. 'Extraversion' (0.162, 'consciousness' (0.158 and 'openness to experience' (0.155 were associated with 'extended social anamnesis' (p<0.05.The
DEFF Research Database (Denmark)
Larsén, Xiaoli Guo; Mann, Jakob; Rathmann, Ole
2015-01-01
This study examines the various sources to the uncertainties in the application of two widely used extreme value distribution functions, the generalized extreme value distribution (GEVD) and the generalized Pareto distribution (GPD). The study is done through the analysis of measurements from...... as a guideline for applying GEVD and GPD to wind time series of limited length. The data analysis shows that, with reasonable choice of relevant parameters, GEVD and GPD give consistent estimates of the return winds. For GEVD, the base period should be chosen in accordance with the occurrence of the extreme wind...
Brown, Joseph W; Parins-Fukuchi, Caroline; Stull, Gregory W; Vargas, Oscar M; Smith, Stephen A
2017-10-11
Puttick et al. (2017 Proc. R. Soc. B 284 , 20162290 (doi:10.1098/rspb.2016.2290)) performed a simulation study to compare accuracy among methods of inferring phylogeny from discrete morphological characters. They report that a Bayesian implementation of the Mk model (Lewis 2001 Syst. Biol. 50 , 913-925 (doi:10.1080/106351501753462876)) was most accurate (but with low resolution), while a maximum-likelihood (ML) implementation of the same model was least accurate. They conclude by strongly advocating that Bayesian implementations of the Mk model should be the default method of analysis for such data. While we appreciate the authors' attempt to investigate the accuracy of alternative methods of analysis, their conclusion is based on an inappropriate comparison of the ML point estimate, which does not consider confidence, with the Bayesian consensus, which incorporates estimation credibility into the summary tree. Using simulation, we demonstrate that ML and Bayesian estimates are concordant when confidence and credibility are comparably reflected in summary trees, a result expected from statistical theory. We therefore disagree with the conclusions of Puttick et al. and consider their prescription of any default method to be poorly founded. Instead, we recommend caution and thoughtful consideration of the model or method being applied to a morphological dataset. © 2017 The Author(s).
The Quark-Gluon Plasma Equation of State and the Generalized Uncertainty Principle
Directory of Open Access Journals (Sweden)
L. I. Abou-Salem
2015-01-01
Full Text Available The quark-gluon plasma (QGP equation of state within a minimal length scenario or Generalized Uncertainty Principle (GUP is studied. The Generalized Uncertainty Principle is implemented on deriving the thermodynamics of ideal QGP at a vanishing chemical potential. We find a significant effect for the GUP term. The main features of QCD lattice results were quantitatively achieved in case of nf=0, nf=2, and nf=2+1 flavors for the energy density, the pressure, and the interaction measure. The exciting point is the large value of bag pressure especially in case of nf=2+1 flavor which reflects the strong correlation between quarks in this bag which is already expected. One can notice that the asymptotic behavior which is characterized by Stephan-Boltzmann limit would be satisfied.
How to: understanding SWAT model uncertainty relative to measured results
Watershed models are being relied upon to contribute to most policy-making decisions of watershed management, and the demand for an accurate accounting of complete model uncertainty is rising. Generalized likelihood uncertainty estimation (GLUE) is a widely used method for quantifying uncertainty i...
Uncertainty Quantification of Composite Laminate Damage with the Generalized Information Theory
Energy Technology Data Exchange (ETDEWEB)
J. Lucero; F. Hemez; T. Ross; K.Kline; J.Hundhausen; T. Tippetts
2006-05-01
This work presents a survey of five theories to assess the uncertainty of projectile impact induced damage on multi-layered carbon-epoxy composite plates. Because the types of uncertainty dealt with in this application are multiple (variability, ambiguity, and conflict) and because the data sets collected are sparse, characterizing the amount of delamination damage with probability theory alone is possible but incomplete. This motivates the exploration of methods contained within a broad Generalized Information Theory (GIT) that rely on less restrictive assumptions than probability theory. Probability, fuzzy sets, possibility, and imprecise probability (probability boxes (p-boxes) and Dempster-Shafer) are used to assess the uncertainty in composite plate damage. Furthermore, this work highlights the usefulness of each theory. The purpose of the study is not to compare directly the different GIT methods but to show that they can be deployed on a practical application and to compare the assumptions upon which these theories are based. The data sets consist of experimental measurements and finite element predictions of the amount of delamination and fiber splitting damage as multilayered composite plates are impacted by a projectile at various velocities. The physical experiments consist of using a gas gun to impact suspended plates with a projectile accelerated to prescribed velocities, then, taking ultrasound images of the resulting delamination. The nonlinear, multiple length-scale numerical simulations couple local crack propagation implemented through cohesive zone modeling to global stress-displacement finite element analysis. The assessment of damage uncertainty is performed in three steps by, first, considering the test data only; then, considering the simulation data only; finally, performing an assessment of total uncertainty where test and simulation data sets are combined. This study leads to practical recommendations for reducing the uncertainty and
Generally Recognized as Safe: Uncertainty Surrounding E-Cigarette Flavoring Safety
Sears, Clara G.; Hart, Joy L.; Walker, Kandi L.; Robertson, Rose Marie
2017-01-01
Despite scientific uncertainty regarding the relative safety of inhaling e-cigarette aerosol and flavorings, some consumers regard the U.S. Food and Drug Administration’s “generally recognized as safe” (GRAS) designation as evidence of flavoring safety. In this study, we assessed how college students’ perceptions of e-cigarette flavoring safety are related to understanding of the GRAS designation. During spring 2017, an online questionnaire was administered to college students. Chi-square p-v...
International Nuclear Information System (INIS)
Keele, B.D.
2005-01-01
A collimated portable gamma-ray detector will be used to quantify the plutonium content of items that can be approximated as a point, line, or area geometry with respect to the detector. These items can include ducts, piping, glove boxes, isolated equipment inside of gloveboxes, and HEPA filters. The Generalized Geometry Holdup (GGH) model is used for the reduction of counting data. This document specifies the calculations to reduce counting data into contained plutonium and the associated total measurement uncertainty.
Generalized Uncertainty Principle and Black Hole Entropy of Higher-Dimensional de Sitter Spacetime
International Nuclear Information System (INIS)
Zhao Haixia; Hu Shuangqi; Zhao Ren; Li Huaifan
2007-01-01
Recently, there has been much attention devoted to resolving the quantum corrections to the Bekenstein-Hawking black hole entropy. In particular, many researchers have expressed a vested interest in the coefficient of the logarithmic term of the black hole entropy correction term. In this paper, we calculate the correction value of the black hole entropy by utilizing the generalized uncertainty principle and obtain the correction term caused by the generalized uncertainty principle. Because in our calculation we think that the Bekenstein-Hawking area theorem is still valid after considering the generalized uncertainty principle, we derive that the coefficient of the logarithmic term of the black hole entropy correction term is positive. This result is different from the known result at present. Our method is valid not only for four-dimensional spacetimes but also for higher-dimensional spacetimes. In the whole process, the physics idea is clear and calculation is simple. It offers a new way for studying the entropy correction of the complicated spacetime.
Covariant energy–momentum and an uncertainty principle for general relativity
Energy Technology Data Exchange (ETDEWEB)
Cooperstock, F.I., E-mail: cooperst@uvic.ca [Department of Physics and Astronomy, University of Victoria, P.O. Box 3055, Victoria, B.C. V8W 3P6 (Canada); Dupre, M.J., E-mail: mdupre@tulane.edu [Department of Mathematics, Tulane University, New Orleans, LA 70118 (United States)
2013-12-15
We introduce a naturally-defined totally invariant spacetime energy expression for general relativity incorporating the contribution from gravity. The extension links seamlessly to the action integral for the gravitational field. The demand that the general expression for arbitrary systems reduces to the Tolman integral in the case of stationary bounded distributions, leads to the matter-localized Ricci integral for energy–momentum in support of the energy localization hypothesis. The role of the observer is addressed and as an extension of the special relativistic case, the field of observers comoving with the matter is seen to compute the intrinsic global energy of a system. The new localized energy supports the Bonnor claim that the Szekeres collapsing dust solutions are energy-conserving. It is suggested that in the extreme of strong gravity, the Heisenberg Uncertainty Principle be generalized in terms of spacetime energy–momentum. -- Highlights: •We present a totally invariant spacetime energy expression for general relativity incorporating the contribution from gravity. •Demand for the general expression to reduce to the Tolman integral for stationary systems supports the Ricci integral as energy–momentum. •Localized energy via the Ricci integral is consistent with the energy localization hypothesis. •New localized energy supports the Bonnor claim that the Szekeres collapsing dust solutions are energy-conserving. •Suggest the Heisenberg Uncertainty Principle be generalized in terms of spacetime energy–momentum in strong gravity extreme.
International Nuclear Information System (INIS)
Tawfik, A.
2013-01-01
We investigate the impacts of Generalized Uncertainty Principle (GUP) proposed by some approaches to quantum gravity such as String Theory and Doubly Special Relativity on black hole thermodynamics and Salecker-Wigner inequalities. Utilizing Heisenberg uncertainty principle, the Hawking temperature, Bekenstein entropy, specific heat, emission rate and decay time are calculated. As the evaporation entirely eats up the black hole mass, the specific heat vanishes and the temperature approaches infinity with an infinite radiation rate. It is found that the GUP approach prevents the black hole from the entire evaporation. It implies the existence of remnants at which the specific heat vanishes. The same role is played by the Heisenberg uncertainty principle in constructing the hydrogen atom. We discuss how the linear GUP approach solves the entire-evaporation-problem. Furthermore, the black hole lifetime can be estimated using another approach; the Salecker-Wigner inequalities. Assuming that the quantum position uncertainty is limited to the minimum wavelength of measuring signal, Wigner second inequality can be obtained. If the spread of quantum clock is limited to some minimum value, then the modified black hole lifetime can be deduced. Based on linear GUP approach, the resulting lifetime difference depends on black hole relative mass and the difference between black hole mass with and without GUP is not negligible
International Nuclear Information System (INIS)
Kim, Wontae; Oh, John J.
2008-01-01
We derive the formula of the black hole entropy with a minimal length of the Planck size by counting quantum modes of scalar fields in the vicinity of the black hole horizon, taking into account the generalized uncertainty principle (GUP). This formula is applied to some intriguing examples of black holes - the Schwarzschild black hole, the Reissner-Nordstrom black hole, and the magnetically charged dilatonic black hole. As a result, it is shown that the GUP parameter can be determined by imposing the black hole entropy-area relationship, which has a Planck length scale and a universal form within the near-horizon expansion
A general method dealing with correlations in uncertainty propagation in fault trees
International Nuclear Information System (INIS)
Qin Zhang
1989-01-01
This paper deals with the correlations among the failure probabilities (frequencies) of not only the identical basic events but also other basic events in a fault tree. It presents a general and simple method to include these correlations in uncertainty propagation. Two examples illustrate this method and show that neglecting these correlations results in large underestimation of the top event failure probability (frequency). One is the failure of the primary pump in a chemical reactor cooling system, the other example is an accident to a road transport truck carrying toxic waste. (author)
Directory of Open Access Journals (Sweden)
Kristen Pickles
Full Text Available Prostate-specific antigen (PSA testing for prostate cancer is controversial. There are unresolved tensions and disagreements amongst experts, and clinical guidelines conflict. This both reflects and generates significant uncertainty about the appropriateness of screening. Little is known about general practitioners' (GPs' perspectives and experiences in relation to PSA testing of asymptomatic men. In this paper we asked the following questions: (1 What are the primary sources of uncertainty as described by GPs in the context of PSA testing? (2 How do GPs experience and respond to different sources of uncertainty?This was a qualitative study that explored general practitioners' current approaches to, and reasoning about, PSA testing of asymptomatic men. We draw on accounts generated from interviews with 69 general practitioners located in Australia (n = 40 and the United Kingdom (n = 29. The interviews were conducted in 2013-2014. Data were analysed using grounded theory methods. Uncertainty in PSA testing was identified as a core issue.Australian GPs reported experiencing substantially more uncertainty than UK GPs. This seemed partly explainable by notable differences in conditions of practice between the two countries. Using Han et al's taxonomy of uncertainty as an initial framework, we first outline the different sources of uncertainty GPs (mostly Australian described encountering in relation to prostate cancer screening and what the uncertainty was about. We then suggest an extension to Han et al's taxonomy based on our analysis of data relating to the varied ways that GPs manage uncertainties in the context of PSA testing. We outline three broad strategies: (1 taking charge of uncertainty; (2 engaging others in managing uncertainty; and (3 transferring the responsibility for reducing or managing some uncertainties to other parties.Our analysis suggests some GPs experienced uncertainties associated with ambiguous guidance and the
Murray, Aja Louise; Booth, Tom; Eisner, Manuel; Obsuth, Ingrid; Ribeaud, Denis
2018-05-22
Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p factor) in classifying, researching, diagnosing, and treating psychiatric disorders depends (among other issues) on the extent to which comorbidity is symptom-general rather than staying largely within the confines of narrower transdiagnostic factors such as internalizing and externalizing. In this study, we compared three methods of estimating p factor strength. We compared omega hierarchical and explained common variance calculated from confirmatory factor analysis (CFA) bifactor models with maximum likelihood (ML) estimation, from exploratory structural equation modeling/exploratory factor analysis models with a bifactor rotation, and from Bayesian structural equation modeling (BSEM) bifactor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings might be the preferred option. However, CFA with ML also performed well provided secondary loadings were modeled. We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n = 1,286) and a university counseling sample (n = 359).
Generally Recognized as Safe: Uncertainty Surrounding E-Cigarette Flavoring Safety
Directory of Open Access Journals (Sweden)
Clara G. Sears
2017-10-01
Full Text Available Despite scientific uncertainty regarding the relative safety of inhaling e-cigarette aerosol and flavorings, some consumers regard the U.S. Food and Drug Administration’s “generally recognized as safe” (GRAS designation as evidence of flavoring safety. In this study, we assessed how college students’ perceptions of e-cigarette flavoring safety are related to understanding of the GRAS designation. During spring 2017, an online questionnaire was administered to college students. Chi-square p-values and multivariable logistic regression were employed to compare perceptions among participants considering e-cigarette flavorings as safe and those considering e-cigarette flavorings to be unsafe. The total sample size was 567 participants. Only 22% knew that GRAS designation meant that a product is safe to ingest, not inhale, inject, or use topically. Of participants who considered flavorings to be GRAS, the majority recognized that the designation meant a product is safe to ingest but also considered it safe to inhale. Although scientific uncertainty on the overall safety of flavorings in e-cigarettes remains, health messaging can educate the public about the GRAS designation and its irrelevance to e-cigarette safety.
Effect of Generalized Uncertainty Principle on Main-Sequence Stars and White Dwarfs
Directory of Open Access Journals (Sweden)
Mohamed Moussa
2015-01-01
Full Text Available This paper addresses the effect of generalized uncertainty principle, emerged from different approaches of quantum gravity within Planck scale, on thermodynamic properties of photon, nonrelativistic ideal gases, and degenerate fermions. A modification in pressure, particle number, and energy density are calculated. Astrophysical objects such as main-sequence stars and white dwarfs are examined and discussed as an application. A modification in Lane-Emden equation due to a change in a polytropic relation caused by the presence of quantum gravity is investigated. The applicable range of quantum gravity parameters is estimated. The bounds in the perturbed parameters are relatively large but they may be considered reasonable values in the astrophysical regime.
Energy Technology Data Exchange (ETDEWEB)
Feng, Z.W.; Zu, X.T. [University of Electronic Science and Technology of China, School of Physical Electronics, Chengdu (China); Li, H.L. [University of Electronic Science and Technology of China, School of Physical Electronics, Chengdu (China); Shenyang Normal University, College of Physics Science and Technology, Shenyang (China); Yang, S.Z. [China West Normal University, Physics and Space Science College, Nanchong (China)
2016-04-15
We investigate the thermodynamics of Schwarzschild-Tangherlini black hole in the context of the generalized uncertainty principle (GUP). The corrections to the Hawking temperature, entropy and the heat capacity are obtained via the modified Hamilton-Jacobi equation. These modifications show that the GUP changes the evolution of the Schwarzschild-Tangherlini black hole. Specially, the GUP effect becomes susceptible when the radius or mass of the black hole approaches the order of Planck scale, it stops radiating and leads to a black hole remnant. Meanwhile, the Planck scale remnant can be confirmed through the analysis of the heat capacity. Those phenomena imply that the GUP may give a way to solve the information paradox. Besides, we also investigate the possibilities to observe the black hole at the Large Hadron Collider (LHC), and the results demonstrate that the black hole cannot be produced in the recent LHC. (orig.)
Thermodynamics of a class of regular black holes with a generalized uncertainty principle
Maluf, R. V.; Neves, Juliano C. S.
2018-05-01
In this article, we present a study on thermodynamics of a class of regular black holes. Such a class includes Bardeen and Hayward regular black holes. We obtained thermodynamic quantities like the Hawking temperature, entropy, and heat capacity for the entire class. As part of an effort to indicate some physical observable to distinguish regular black holes from singular black holes, we suggest that regular black holes are colder than singular black holes. Besides, contrary to the Schwarzschild black hole, that class of regular black holes may be thermodynamically stable. From a generalized uncertainty principle, we also obtained the quantum-corrected thermodynamics for the studied class. Such quantum corrections provide a logarithmic term for the quantum-corrected entropy.
Directory of Open Access Journals (Sweden)
Xiang-Qian Li
2016-12-01
Full Text Available This study considers the generalized uncertainty principle, which incorporates the central idea of large extra dimensions, to investigate the processes involved when massive spin-1 particles tunnel from Reissner–Nordstrom and Kerr black holes under the effects of quantum gravity. For the black hole, the quantum gravity correction decelerates the increase in temperature. Up to O(1Mf2, the corrected temperatures are affected by the mass and angular momentum of the emitted vector bosons. In addition, the temperature of the Kerr black hole becomes uneven due to rotation. When the mass of the black hole approaches the order of the higher dimensional Planck mass Mf, it stops radiating and yields a black hole remnant.
Ponder, Kara A.
In the late 1990s, Type Ia supernovae (SNeIa) led to the discovery that the Universe is expanding at an accelerating rate due to dark energy. Since then, many different tracers of acceleration have been used to characterize dark energy, but the source of cosmic acceleration has remained a mystery. To better understand dark energy, future surveys such as the ground-based Large Synoptic Survey Telescope and the space-based Wide-Field Infrared Survey Telescope will collect thousands of SNeIa to use as a primary dark energy probe. These large surveys will be systematics limited, which makes it imperative for our insight regarding systematics to dramatically increase over the next decade for SNeIa to continue to contribute to precision cosmology. I approach this problem by improving statistical methods in the likelihood analysis and collecting near infrared (NIR) SNeIa with their host galaxies to improve the nearby data set and search for additional systematics. Using more statistically robust methods to account for systematics within the likelihood function can increase accuracy in cosmological parameters with a minimal precision loss. Though a sample of at least 10,000 SNeIa is necessary to confirm multiple populations of SNeIa, the bias in cosmology is ˜ 2 sigma with only 2,500 SNeIa. This work focused on an example systematic (host galaxy correlations), but it can be generalized for any systematic that can be represented by a distribution of multiple Gaussians. The SweetSpot survey gathered 114 low-redshift, NIR SNeIa that will act as a crucial anchor sample for the future high redshift surveys. NIR observations are not as affected by dust contamination, which may lead to increased understanding of systematics seen in optical wavelengths. We obtained spatially resolved spectra for 32 SweetSpot host galaxies to test for local host galaxy correlations. For the first time, we probe global host galaxy correlations with NIR brightnesses from the current literature
Directory of Open Access Journals (Sweden)
Danny eFlemming
2015-12-01
Full Text Available We examined in two empirical studies how situational and personal aspects of uncertainty influence laypeople’s understanding of the uncertainty of scientific information, with focus on the detection of tentativeness and perception of scientific credibility. In the first study (N = 48, we investigated the impact of a perceived conflict due to contradicting information as a situational, text-inherent aspect of uncertainty. The aim of the second study (N = 61 was to explore the role of general self-efficacy as an intra-personal uncertainty factor. In Study 1, participants read one of two versions of an introductory text in a between-group design. This text provided them with an overview about the neurosurgical procedure of deep brain stimulation (DBS. The text expressed a positive attitude toward DBS in one experimental condition or focused on the negative aspects of this method in the other condition. Then participants in both conditions read the same text that dealt with a study about DBS as experimental treatment in a small sample of patients with major depression. Perceived conflict between the two texts was found to increase the perception of tentativeness and to decrease the perception of scientific credibility, implicating that text-inherent aspects have significant effects on critical appraisal. The results of Study 2 demonstrated that participants with higher general self-efficacy detected the tentativeness to a lesser degree and assumed a higher level of scientific credibility, indicating a more naïve understanding of scientific information. This appears to be contradictory to large parts of previous findings that showed positive effects of high self-efficacy on learning. Both studies showed that perceived tentativeness and perceived scientific credibility of medical information contradicted each other. We conclude that there is a need for supporting laypeople in understanding the uncertainty of scientific information and that
f(R in Holographic and Agegraphic Dark Energy Models and the Generalized Uncertainty Principle
Directory of Open Access Journals (Sweden)
Barun Majumder
2013-01-01
Full Text Available We studied a unified approach with the holographic, new agegraphic, and f(R dark energy model to construct the form of f(R which in general is responsible for the curvature driven explanation of the very early inflation along with presently observed late time acceleration. We considered the generalized uncertainty principle in our approach which incorporated the corrections in the entropy-area relation and thereby modified the energy densities for the cosmological dark energy models considered. We found that holographic and new agegraphic f(R gravity models can behave like phantom or quintessence models in the spatially flat FRW universe. We also found a distinct term in the form of f(R which goes as R 3 / 2 due to the consideration of the GUP modified energy densities. Although the presence of this term in the action can be important in explaining the early inflationary scenario, Capozziello et al. recently showed that f(R ~ R 3 / 2 leads to an accelerated expansion, that is, a negative value for the deceleration parameter q which fits well with SNeIa and WMAP data.
Barth, Timothy J.
2014-01-01
This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.
Li, Xi-Zeng; Su, Bao-Xia
1996-01-01
It is found that the field of the combined mode of the probe wave and the phase-conjugate wave in the process of non-degenerate four-wave mixing exhibits higher-order squeezing to all even orders. And the generalized uncertainty relations in this process are also presented.
Quantifying remarks to the question of uncertainties of the 'general dose assessment fundamentals'
International Nuclear Information System (INIS)
Brenk, H.D.; Vogt, K.J.
1982-12-01
Dose prediction models are always subject to uncertainties due to a number of factors including deficiencies in the model structure and uncertainties of the model input parameter values. In lieu of validation experiments the evaluation of these uncertainties is restricted to scientific judgement. Several attempts have been made in the literature to evaluate the uncertainties of the current dose assessment models resulting from uncertainties of the model input parameter values using stochastic approaches. Less attention, however, has been paid to potential sources of systematic over- and underestimations of the predicted doses due to deficiencies in the model structure. The present study addresses this aspect with regard to dose assessment models currently used for regulatory purposes. The influence of a number of basic simplifications and conservative assumptions has been investigated. Our systematic approach is exemplified by a comparison of doses evaluated on the basis of the regulatory guide model and a more realistic model respectively. This is done for 3 critical exposure pathways. As a result of this comparison it can be concluded that the currently used regularoty-type models include significant safety factors resulting in a systematic overprediction of dose to man up to two orders of magnitude. For this reason there are some indications that these models usually more than compensate the bulk of the stochastic uncertainties caused by the variability of the input parameter values. (orig.) [de
International Nuclear Information System (INIS)
Apostolakis, G.E.
2004-01-01
George Apostolakis (MIT) presented an introduction to the concept of risk and uncertainty management and their use in technical and scientific studies. He noted that Quantitative Risk Assessment (QRA) provides support to the overall treatment of a system as an integrated socio-technical system. Specifically, QRA aims to answer the questions: - What can go wrong (e.g., accident sequences or scenarios)? - How likely are these sequences or scenarios? - What are the consequences of these sequences or scenarios? The Quantitative Risk Assessment deals with two major types of uncertainty. An assessment requires a 'model of the world', and this preferably would be a deterministic model based on underlying processes. In practice, there are uncertainties in this model of the world relating to variability or randomness that cannot be accounted for directly in a deterministic model and that may require a probabilistic or aleatory model. Both deterministic and aleatory models of the world have assumptions and parameters, and there are 'state-of-knowledge' or epistemic uncertainties associated with these. Sensitivity studies or eliciting expert opinion can be used to address the uncertainties in assumptions, and the level of confidence in parameter values can be characterised using probability distributions (pdfs). Overall, the distinction between aleatory and epistemic uncertainties is not always clear, and both can be treated mathematically in the same way. Lessons on safety assessments that can be learnt from experience at nuclear power plants are that beliefs about what is important can be wrong if a risk assessment is not performed. Also, precautionary approaches are not always conservative if failure modes are not identified. Nevertheless, it is important to recognize that uncertainties will remain despite a quantitative risk assessment: e.g., is the scenario list complete, are the models accepted as reasonable, and are parameter probability distributions representative of
Sotirov, Sotir
2016-01-01
The book offers a comprehensive and timely overview of advanced mathematical tools for both uncertainty analysis and modeling of parallel processes, with a special emphasis on intuitionistic fuzzy sets and generalized nets. The different chapters, written by active researchers in their respective areas, are structured to provide a coherent picture of this interdisciplinary yet still evolving field of science. They describe key tools and give practical insights into and research perspectives on the use of Atanassov's intuitionistic fuzzy sets and logic, and generalized nets for describing and dealing with uncertainty in different areas of science, technology and business, in a single, to date unique book. Here, readers find theoretical chapters, dealing with intuitionistic fuzzy operators, membership functions and algorithms, among other topics, as well as application-oriented chapters, reporting on the implementation of methods and relevant case studies in management science, the IT industry, medicine and/or ...
Shababi, Homa; Chung, Won Sang
2018-04-01
In this paper, using the new type of D-dimensional nonperturbative Generalized Uncertainty Principle (GUP) which has predicted both a minimal length uncertainty and a maximal observable momentum,1 first, we obtain the maximally localized states and express their connections to [P. Pedram, Phys. Lett. B 714, 317 (2012)]. Then, in the context of our proposed GUP and using the generalized Schrödinger equation, we solve some important problems including particle in a box and one-dimensional hydrogen atom. Next, implying modified Bohr-Sommerfeld quantization, we obtain energy spectra of quantum harmonic oscillator and quantum bouncer. Finally, as an example, we investigate some statistical properties of a free particle, including partition function and internal energy, in the presence of the mentioned GUP.
Yook, Keunyoung; Kim, Keun-Hyang; Suh, Shin Young; Lee, Kang Soo
2010-08-01
Intolerance of uncertainty (IU) can be defined as a cognitive bias that affects how a person perceives, interprets, and responds to uncertain situations. Although IU has been reported mainly in literature relating to worry and anxiety symptoms, it may be also important to investigate the relationship between IU, rumination, and depression in a clinical sample. Furthermore, individuals who are intolerant of uncertainty easily experience stress and could cope with stressful situations using repetitive thought such as worry and rumination. Thus, we investigated whether different forms of repetitive thought differentially mediate the relationship between IU and psychological symptoms. Participants included 27 patients with MDD, 28 patients with GAD, and 16 patients with comorbid GAD/MDD. Even though worry, rumination, IU, anxiety, and depressive symptoms correlated substantially with each other, worry partially mediated the relationship between IU and anxiety whereas rumination completely mediated the relationship between IU and depressive symptoms. (c) 2010 Elsevier Ltd. All rights reserved.
Humor Styles and the Intolerance of Uncertainty Model of Generalized Anxiety
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Nicholas A. Kuiper
2014-08-01
Full Text Available Past research suggests that sense of humor may play a role in anxiety. The present study builds upon this work by exploring how individual differences in various humor styles, such as affiliative, self-enhancing, and self-defeating humor, may fit within a contemporary research model of anxiety. In this model, intolerance of uncertainty is a fundamental personality characteristic that heightens excessive worry, thus increasing anxiety. We further propose that greater intolerance of uncertainty may also suppress the use of adaptive humor (affiliate and self-enhancing, and foster the increased use of maladaptive self-defeating humor. Initial correlational analyses provide empirical support for these proposals. In addition, we found that excessive worry and affiliative humor both served as significant mediators. In particular, heightened intolerance of uncertainty lead to both excessive worry and a reduction in affiliative humor use, which, in turn, increased anxiety. We also explored potential humor mediating effects for each of the individual worry content domains in this model. These analyses confirmed the importance of affiliative humor as a mediator for worry pertaining to a wide range of content domains (e.g., relationships, lack of confidence, the future and work. These findings were then discussed in terms of a combined model that considers how humor styles may impact the social sharing of positive and negative emotions.
Essays on empirical likelihood in economics
Gao, Z.
2012-01-01
This thesis intends to exploit the roots of empirical likelihood and its related methods in mathematical programming and computation. The roots will be connected and the connections will induce new solutions for the problems of estimation, computation, and generalization of empirical likelihood.
Soave, David; Sun, Lei
2017-09-01
We generalize Levene's test for variance (scale) heterogeneity between k groups for more complex data, when there are sample correlation and group membership uncertainty. Following a two-stage regression framework, we show that least absolute deviation regression must be used in the stage 1 analysis to ensure a correct asymptotic χk-12/(k-1) distribution of the generalized scale (gS) test statistic. We then show that the proposed gS test is independent of the generalized location test, under the joint null hypothesis of no mean and no variance heterogeneity. Consequently, we generalize the recently proposed joint location-scale (gJLS) test, valuable in settings where there is an interaction effect but one interacting variable is not available. We evaluate the proposed method via an extensive simulation study and two genetic association application studies. © 2017 The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.
Crupi, Vincenzo; Nelson, Jonathan D; Meder, Björn; Cevolani, Gustavo; Tentori, Katya
2018-06-17
Searching for information is critical in many situations. In medicine, for instance, careful choice of a diagnostic test can help narrow down the range of plausible diseases that the patient might have. In a probabilistic framework, test selection is often modeled by assuming that people's goal is to reduce uncertainty about possible states of the world. In cognitive science, psychology, and medical decision making, Shannon entropy is the most prominent and most widely used model to formalize probabilistic uncertainty and the reduction thereof. However, a variety of alternative entropy metrics (Hartley, Quadratic, Tsallis, Rényi, and more) are popular in the social and the natural sciences, computer science, and philosophy of science. Particular entropy measures have been predominant in particular research areas, and it is often an open issue whether these divergences emerge from different theoretical and practical goals or are merely due to historical accident. Cutting across disciplinary boundaries, we show that several entropy and entropy reduction measures arise as special cases in a unified formalism, the Sharma-Mittal framework. Using mathematical results, computer simulations, and analyses of published behavioral data, we discuss four key questions: How do various entropy models relate to each other? What insights can be obtained by considering diverse entropy models within a unified framework? What is the psychological plausibility of different entropy models? What new questions and insights for research on human information acquisition follow? Our work provides several new pathways for theoretical and empirical research, reconciling apparently conflicting approaches and empirical findings within a comprehensive and unified information-theoretic formalism. Copyright © 2018 Cognitive Science Society, Inc.
Modelling uncertainty with generalized credal sets: application to conjunction and decision
Bronevich, Andrey G.; Rozenberg, Igor N.
2018-01-01
To model conflict, non-specificity and contradiction in information, upper and lower generalized credal sets are introduced. Any upper generalized credal set is a convex subset of plausibility measures interpreted as lower probabilities whose bodies of evidence consist of singletons and a certain event. Analogously, contradiction is modelled in the theory of evidence by a belief function that is greater than zero at empty set. Based on generalized credal sets, we extend the conjunctive rule for contradictory sources of information, introduce constructions like natural extension in the theory of imprecise probabilities and show that the model of generalized credal sets coincides with the model of imprecise probabilities if the profile of a generalized credal set consists of probability measures. We give ways how the introduced model can be applied to decision problems.
Anderson, Kristin G; Dugas, Michel J; Koerner, Naomi; Radomsky, Adam S; Savard, Pierre; Turcotte, Julie
2012-12-01
Interpretations of negative, positive, and ambiguous situations were examined in individuals with generalized anxiety disorder (GAD), other anxiety disorders (ANX), and no psychiatric condition (CTRL). Additionally, relationships between specific beliefs about uncertainty (Uncertainty Has Negative Behavioral and Self-Referent Implications [IUS-NI], and Uncertainty Is Unfair and Spoils Everything [IUS-US]) and interpretations were explored. The first hypothesis (that the clinical groups would report more concern for negative, positive, and ambiguous situations than would the CTRL group) was supported. The second hypothesis (that the GAD group would report more concern for ambiguous situations than would the ANX group) was not supported; both groups reported similar levels of concern for ambiguous situations. Exploratory analyses revealed no differences between the GAD and ANX groups in their interpretations of positive and negative situations. Finally, the IUS-US predicted interpretations of negative and ambiguous situations in the full sample, whereas the IUS-NI did not. Clinical implications are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Angulo, J. C.
2011-01-01
Rigorous and universal relationships among radial expectation values of any D-dimensional quantum-mechanical system are obtained, using Renyi-like position-momentum inequalities in an information-theoretical framework. Although the results are expressed in terms of four moments (two in position space and two in the momentum one), especially interesting are the cases that provide expressions of uncertainty in terms of products a > 1/a b > 1/b , widely considered in the literature, including the famous Heisenberg relationship 2 > 2 >≥D 2 /4. Improved bounds for these products have recently been provided, but are always restricted to positive orders a,b>0. The interesting part of this work are the inequalities for negative orders. A study of these relationships is carried out for atomic systems in their ground state. Some results are given in terms of relevant physical quantities, including the kinetic and electron-nucleus attraction energies, the diamagnetic susceptibility, and the height of the peak of the Compton profile, among others.
Planck 2013 results. XV. CMB power spectra and likelihood
DEFF Research Database (Denmark)
Tauber, Jan; Bartlett, J.G.; Bucher, M.
2014-01-01
This paper presents the Planck 2013 likelihood, a complete statistical description of the two-point correlation function of the CMB temperature fluctuations that accounts for all known relevant uncertainties, both instrumental and astrophysical in nature. We use this likelihood to derive our best...
On Bayesian treatment of systematic uncertainties in confidence interval calculation
Tegenfeldt, Fredrik
2005-01-01
In high energy physics, a widely used method to treat systematic uncertainties in confidence interval calculations is based on combining a frequentist construction of confidence belts with a Bayesian treatment of systematic uncertainties. In this note we present a study of the coverage of this method for the standard Likelihood Ratio (aka Feldman & Cousins) construction for a Poisson process with known background and Gaussian or log-Normal distributed uncertainties in the background or signal efficiency. For uncertainties in the signal efficiency of upto 40 % we find over-coverage on the level of 2 to 4 % depending on the size of uncertainties and the region in signal space. Uncertainties in the background generally have smaller effect on the coverage. A considerable smoothing of the coverage curves is observed. A software package is presented which allows fast calculation of the confidence intervals for a variety of assumptions on shape and size of systematic uncertainties for different nuisance paramete...
Uncertainty Assessment in Long Term Urban Drainage Modelling
DEFF Research Database (Denmark)
Thorndahl, Søren
the probability of system failures (defined as either flooding or surcharge of manholes or combined sewer overflow); (2) an application of the Generalized Likelihood Uncertainty Estimation methodology in which an event based stochastic calibration is performed; and (3) long term Monte Carlo simulations...
Hui, Chen; Zhihui, Yang
2017-12-01
China has entered the aging society, but the social support systems for the elderly are underdeveloped, which may make the elderly feel anxiety about their health and life quality. Given the prevalence of generalized anxiety disorder (GAD) in the elderly, it is very important to pay more attention to the treatment for old adults. Although cognitive behavioral therapy targeting intolerance of uncertainty (CBT-IU) has been applied to different groups of patients with GAD, few studies have been performed to date. In addition, the effects of CBT-IU are not well understood, especially when applied to older adults with GAD. Sixty-three Chinese older adults with a principal diagnosis of GAD were enrolled. Of these, 32 were randomized to receive group CBT-IU (intervention group) and 31 were untreated (control group). GAD and related symptoms were assessed using the Penn State Worry Questionnaire, Intolerance of Uncertainty Scale-Chinese Version, Beck Anxiety Inventory, Beck Depression Inventory, Why Worry-II scale, Cognitive Avoidance Questionnaire, Generalized Anxiety Disorder Questionnaire-IV, and Generalized Anxiety Disorder Severity Scale across the intervention. The changes between pre and after the intervention were collected, as well as the six-month follow-up. F test and repeated-measures ANOVA were conducted to analyze the data. Compared to control group, the measures' scores of experimental group decreased significantly after the intervention and six-month follow-up. Besides the main effects for time and group were significant, the interaction effect for group × time was also significant. These results indicated the improvement of the CBT-IU group and the persistence of effect after six months. Group CBT-IU is effective in Chinese older adults with GAD. The effects of CBT-IU on GAD symptoms persist for at least six months after treatment.
International Nuclear Information System (INIS)
Zhang, Shao-Jun; Miao, Yan-Gang; Zhao, Ying-Jie
2015-01-01
As a generalized uncertainty principle (GUP) leads to the effects of the minimal length of the order of the Planck scale and UV/IR mixing, some significant physical concepts and quantities are modified or corrected correspondingly. On the one hand, we derive the maximally localized states—the physical states displaying the minimal length uncertainty associated with a new GUP proposed in our previous work. On the other hand, in the framework of this new GUP we calculate quantum corrections to the thermodynamic quantities of the Schwardzschild black hole, such as the Hawking temperature, the entropy, and the heat capacity, and give a remnant mass of the black hole at the end of the evaporation process. Moreover, we compare our results with that obtained in the frameworks of several other GUPs. In particular, we observe a significant difference between the situations with and without the consideration of the UV/IR mixing effect in the quantum corrections to the evaporation rate and the decay time. That is, the decay time can greatly be prolonged in the former case, which implies that the quantum correction from the UV/IR mixing effect may give rise to a radical rather than a tiny influence to the Hawking radiation.
Posterior distributions for likelihood ratios in forensic science.
van den Hout, Ardo; Alberink, Ivo
2016-09-01
Evaluation of evidence in forensic science is discussed using posterior distributions for likelihood ratios. Instead of eliminating the uncertainty by integrating (Bayes factor) or by conditioning on parameter values, uncertainty in the likelihood ratio is retained by parameter uncertainty derived from posterior distributions. A posterior distribution for a likelihood ratio can be summarised by the median and credible intervals. Using the posterior mean of the distribution is not recommended. An analysis of forensic data for body height estimation is undertaken. The posterior likelihood approach has been criticised both theoretically and with respect to applicability. This paper addresses the latter and illustrates an interesting application area. Copyright © 2016 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Gioia Bottesi
2016-11-01
Full Text Available The Intolerance of Uncertainty Model (IUM of Generalized Anxiety Disorder (GAD attributes a key role to Intolerance of Uncertainty (IU, and additional roles to Positive Beliefs about Worry (PBW, Negative Problem Orientation (NPO, and Cognitive Avoidance (CA, in the development and maintenance of worry, the core feature of GAD. Despite the role of the IUM components in worry and GAD has been considerably demonstrated, to date no studies have explicitly assessed whether and how PBW, NPO, and CA might turn IU into worry and somatic anxiety. The current studies sought to re-examine the IUM by assessing the relationships between the model’s components on two different non-clinical samples made up of UK and Italian undergraduate students. One-hundred and seventy UK undergraduates and 488 Italian undergraduates completed measures assessing IU, worry, somatic anxiety, depression, and refined measures of NPO, CA, and PBW. In each sample, two mediation models were conducted in order to test whether PBW, NPO, and CA differentially mediate the path from IU to worry and the path from IU to somatic anxiety. Secondly, it was tested whether IU also moderates the mediations. Main findings showed that, in the UK sample, only NPO mediated the path from IU to worry; as far as concern the path to anxiety, none of the putative mediators were significant. Differently, in the Italian sample PBW and NPO were mediators in the path from IU to worry, whereas only CA played a mediational role in the path from IU to somatic anxiety. Lastly, IU was observed to moderate only the association between NPO and worry, and only in the Italian sample. Some important cross-cultural, conceptual, and methodological issues raised from main results are discussed.
Energy Technology Data Exchange (ETDEWEB)
Schunert, Sebastian; Wang, Congjian; Wang, Yaqi; Kong, Fande; Ortensi, Javier; Baker, Benjamin; Gleicher, Frederick; DeHart, Mark; Martineau, Richard
2017-04-01
Rattlesnake and MAMMOTH are the designated TREAT analysis tools currently being developed at the Idaho National Laboratory. Concurrent with development of the multi-physics, multi-scale capabilities, sensitivity analysis and uncertainty quantification (SA/UQ) capabilities are required for predicitive modeling of the TREAT reactor. For steady-state SA/UQ, that is essential for setting initial conditions for the transients, generalized perturbation theory (GPT) will be used. This work describes the implementation of a PETSc based solver for the generalized adjoint equations that constitute a inhomogeneous, rank deficient problem. The standard approach is to use an outer iteration strategy with repeated removal of the fundamental mode contamination. The described GPT algorithm directly solves the GPT equations without the need of an outer iteration procedure by using Krylov subspaces that are orthogonal to the operator’s nullspace. Three test problems are solved and provide sufficient verification for the Rattlesnake’s GPT capability. We conclude with a preliminary example evaluating the impact of the Boron distribution in the TREAT reactor using perturbation theory.
Zhang, Chenglong; Guo, Ping
2017-10-01
The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production.
Cook, Bruce D.; Bolstad, Paul V.; Naesset, Erik; Anderson, Ryan S.; Garrigues, Sebastian; Morisette, Jeffrey T.; Nickeson, Jaime; Davis, Kenneth J.
2009-01-01
Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the MOderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30 m to 1 km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600 ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400 m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.
Fahrenholtz, S; Fuentes, D; Stafford, R; Hazle, J
2012-06-01
Magnetic resonance-guided laser induced thermal therapy (MRgLITT) is a minimally invasive thermal treatment for metastatic brain lesions, offering an alternative to conventional surgery. The purpose of this investigation is to incorporate uncertainty quantification (UQ) into the biothermal parameters used in the Pennes bioheat transfer equation (BHT), in order to account for imprecise values available in the literature. The BHT is a partial differential equation commonly used in thermal therapy models. MRgLITT was performed on an in vivo canine brain in a previous investigation. The canine MRgLITT was modeled using the BHT. The BHT has four parameters'" microperfusion, conductivity, optical absorption, and optical scattering'"which lack precise measurements in living brain and tumor. The uncertainties in the parameters were expressed as probability distribution functions derived from literature values. A univariate generalized polynomial chaos (gPC) expansion was applied to the stochastic BHT. The gPC approach to UQ provides a novel methodology to calculate spatio-temporal voxel-wise means and variances of the predicted temperature distributions. The performance of the gPC predictions were evaluated retrospectively by comparison with MR thermal imaging (MRTI) acquired during the MRgLITT procedure in the canine model. The comparison was evaluated with root mean square difference (RMSD), isotherm contours, spatial profiles, and z-tests. The peak RMSD was ∼1.5 standard deviations for microperfusion, conductivity, and optical absorption, while optical scattering was ∼2.2 standard deviations. Isotherm contours and spatial profiles of the simulation's predicted mean plus or minus two standard deviations demonstrate the MRTI temperature was enclosed by the model's isotherm confidence interval predictions. An a = 0.01 z-test demonstrates agreement. The application of gPC for UQ is a potentially powerful means for providing predictive simulations despite poorly known
International Nuclear Information System (INIS)
Pronyaev, V.G.
2003-01-01
The information entropy is taken as a measure of knowledge about the object and the reduced univariante variance as a common measure of uncertainty. Covariances in the model versus non-model least square fits are discussed
Removal of Asperger's syndrome from the DSM V: community response to uncertainty.
Parsloe, Sarah M; Babrow, Austin S
2016-01-01
The May 2013 release of the new version of the Diagnostic and Statistical Manual of Mental Disorders (DSM V) subsumed Asperger's syndrome under the wider diagnostic label of autism spectrum disorder (ASD). The revision has created much uncertainty in the community affected by this condition. This study uses problematic integration theory and thematic analysis to investigate how participants in Wrong Planet, a large online community associated with autism and Asperger's syndrome, have constructed these uncertainties. The analysis illuminates uncertainties concerning both the likelihood of diagnosis and value of diagnosis, and it details specific issues within these two general areas of uncertainty. The article concludes with both conceptual and practical implications.
Uncertainty vs. Information (Invited)
Nearing, Grey
2017-04-01
Information theory is the branch of logic that describes how rational epistemic states evolve in the presence of empirical data (Knuth, 2005), and any logic of science is incomplete without such a theory. Developing a formal philosophy of science that recognizes this fact results in essentially trivial solutions to several longstanding problems are generally considered intractable, including: • Alleviating the need for any likelihood function or error model. • Derivation of purely logical falsification criteria for hypothesis testing. • Specification of a general quantitative method for process-level model diagnostics. More generally, I make the following arguments: 1. Model evaluation should not proceed by quantifying and/or reducing error or uncertainty, and instead should be approached as a problem of ensuring that our models contain as much information as our experimental data. I propose that the latter is the only question a scientist actually has the ability to ask. 2. Instead of building geophysical models as solutions to differential equations that represent conservation laws, we should build models as maximum entropy distributions constrained by conservation symmetries. This will allow us to derive predictive probabilities directly from first principles. Knuth, K. H. (2005) 'Lattice duality: The origin of probability and entropy', Neurocomputing, 67, pp. 245-274.
Likelihood devices in spatial statistics
Zwet, E.W. van
1999-01-01
One of the main themes of this thesis is the application to spatial data of modern semi- and nonparametric methods. Another, closely related theme is maximum likelihood estimation from spatial data. Maximum likelihood estimation is not common practice in spatial statistics. The method of moments
Directory of Open Access Journals (Sweden)
Kodner Robin B
2010-10-01
Full Text Available Abstract Background Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets. Results This paper introduces pplacer, a software package for phylogenetic placement and subsequent visualization. The algorithm can place twenty thousand short reads on a reference tree of one thousand taxa per hour per processor, has essentially linear time and memory complexity in the number of reference taxa, and is easy to run in parallel. Pplacer features calculation of the posterior probability of a placement on an edge, which is a statistically rigorous way of quantifying uncertainty on an edge-by-edge basis. It also can inform the user of the positional uncertainty for query sequences by calculating expected distance between placement locations, which is crucial in the estimation of uncertainty with a well-sampled reference tree. The software provides visualizations using branch thickness and color to represent number of placements and their uncertainty. A simulation study using reads generated from 631 COG alignments shows a high level of accuracy for phylogenetic placement over a wide range of alignment diversity, and the power of edge uncertainty estimates to measure placement confidence. Conclusions Pplacer enables efficient phylogenetic placement and subsequent visualization, making likelihood-based phylogenetics methodology practical for large collections of reads; it is freely available as source code, binaries, and a web service.
DEFF Research Database (Denmark)
Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.
2011-01-01
Distributed weather radar precipitation measurements are used as rainfall input for an urban drainage model, to simulate the runoff from a small catchment of Denmark. It is demonstrated how the Generalized Likelihood Uncertainty Estimation (GLUE) methodology can be implemented and used to estimate...
Likelihood-based inference for clustered line transect data
DEFF Research Database (Denmark)
Waagepetersen, Rasmus; Schweder, Tore
2006-01-01
The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...
Likelihood-based inference for clustered line transect data
DEFF Research Database (Denmark)
Waagepetersen, Rasmus Plenge; Schweder, Tore
The uncertainty in estimation of spatial animal density from line transect surveys depends on the degree of spatial clustering in the animal population. To quantify the clustering we model line transect data as independent thinnings of spatial shot-noise Cox processes. Likelihood-based inference...
Extended likelihood inference in reliability
International Nuclear Information System (INIS)
Martz, H.F. Jr.; Beckman, R.J.; Waller, R.A.
1978-10-01
Extended likelihood methods of inference are developed in which subjective information in the form of a prior distribution is combined with sampling results by means of an extended likelihood function. The extended likelihood function is standardized for use in obtaining extended likelihood intervals. Extended likelihood intervals are derived for the mean of a normal distribution with known variance, the failure-rate of an exponential distribution, and the parameter of a binomial distribution. Extended second-order likelihood methods are developed and used to solve several prediction problems associated with the exponential and binomial distributions. In particular, such quantities as the next failure-time, the number of failures in a given time period, and the time required to observe a given number of failures are predicted for the exponential model with a gamma prior distribution on the failure-rate. In addition, six types of life testing experiments are considered. For the binomial model with a beta prior distribution on the probability of nonsurvival, methods are obtained for predicting the number of nonsurvivors in a given sample size and for predicting the required sample size for observing a specified number of nonsurvivors. Examples illustrate each of the methods developed. Finally, comparisons are made with Bayesian intervals in those cases where these are known to exist
Review of Elaboration Likelihood Model of persuasion
藤原, 武弘; 神山, 貴弥
1989-01-01
This article mainly introduces Elaboration Likelihood Model (ELM), proposed by Petty & Cacioppo, that is, a general attitude change theory. ELM posturates two routes to persuasion; central and peripheral route. Attitude change by central route is viewed as resulting from a diligent consideration of the issue-relevant informations presented. On the other hand, attitude change by peripheral route is viewed as resulting from peripheral cues in the persuasion context. Secondly we compare these tw...
Energy Technology Data Exchange (ETDEWEB)
Schmidtlein, CR; Beattie, B; Humm, J [Memorial Sloan Kettering Cancer Center, New York, NY (United States); Li, S; Wu, Z; Xu, Y [Sun Yat-sen University, Guangzhou, Guangdong (China); Zhang, J; Shen, L [Syracuse University, Syracuse, NY (United States); Vogelsang, L [VirtualScopics, Rochester, NY (United States); Feiglin, D; Krol, A [SUNY Upstate Medical University, Syracuse, NY (United States)
2014-06-15
Purpose: To investigate the performance of a new penalized-likelihood PET image reconstruction algorithm using the 1{sub 1}-norm total-variation (TV) sum of the 1st through 4th-order gradients as the penalty. Simulated and brain patient data sets were analyzed. Methods: This work represents an extension of the preconditioned alternating projection algorithm (PAPA) for emission-computed tomography. In this new generalized algorithm (GPAPA), the penalty term is expanded to allow multiple components, in this case the sum of the 1st to 4th order gradients, to reduce artificial piece-wise constant regions (“staircase” artifacts typical for TV) seen in PAPA images penalized with only the 1st order gradient. Simulated data were used to test for “staircase” artifacts and to optimize the penalty hyper-parameter in the root-mean-squared error (RMSE) sense. Patient FDG brain scans were acquired on a GE D690 PET/CT (370 MBq at 1-hour post-injection for 10 minutes) in time-of-flight mode and in all cases were reconstructed using resolution recovery projectors. GPAPA images were compared PAPA and RMSE-optimally filtered OSEM (fully converged) in simulations and to clinical OSEM reconstructions (3 iterations, 32 subsets) with 2.6 mm XYGaussian and standard 3-point axial smoothing post-filters. Results: The results from the simulated data show a significant reduction in the 'staircase' artifact for GPAPA compared to PAPA and lower RMSE (up to 35%) compared to optimally filtered OSEM. A simple power-law relationship between the RMSE-optimal hyper-parameters and the noise equivalent counts (NEC) per voxel is revealed. Qualitatively, the patient images appear much sharper and with less noise than standard clinical images. The convergence rate is similar to OSEM. Conclusions: GPAPA reconstructions using the 1{sub 1}-norm total-variation sum of the 1st through 4th-order gradients as the penalty show great promise for the improvement of image quality over that
International Nuclear Information System (INIS)
Schmidtlein, CR; Beattie, B; Humm, J; Li, S; Wu, Z; Xu, Y; Zhang, J; Shen, L; Vogelsang, L; Feiglin, D; Krol, A
2014-01-01
Purpose: To investigate the performance of a new penalized-likelihood PET image reconstruction algorithm using the 1 1 -norm total-variation (TV) sum of the 1st through 4th-order gradients as the penalty. Simulated and brain patient data sets were analyzed. Methods: This work represents an extension of the preconditioned alternating projection algorithm (PAPA) for emission-computed tomography. In this new generalized algorithm (GPAPA), the penalty term is expanded to allow multiple components, in this case the sum of the 1st to 4th order gradients, to reduce artificial piece-wise constant regions (“staircase” artifacts typical for TV) seen in PAPA images penalized with only the 1st order gradient. Simulated data were used to test for “staircase” artifacts and to optimize the penalty hyper-parameter in the root-mean-squared error (RMSE) sense. Patient FDG brain scans were acquired on a GE D690 PET/CT (370 MBq at 1-hour post-injection for 10 minutes) in time-of-flight mode and in all cases were reconstructed using resolution recovery projectors. GPAPA images were compared PAPA and RMSE-optimally filtered OSEM (fully converged) in simulations and to clinical OSEM reconstructions (3 iterations, 32 subsets) with 2.6 mm XYGaussian and standard 3-point axial smoothing post-filters. Results: The results from the simulated data show a significant reduction in the 'staircase' artifact for GPAPA compared to PAPA and lower RMSE (up to 35%) compared to optimally filtered OSEM. A simple power-law relationship between the RMSE-optimal hyper-parameters and the noise equivalent counts (NEC) per voxel is revealed. Qualitatively, the patient images appear much sharper and with less noise than standard clinical images. The convergence rate is similar to OSEM. Conclusions: GPAPA reconstructions using the 1 1 -norm total-variation sum of the 1st through 4th-order gradients as the penalty show great promise for the improvement of image quality over that currently
Hua, Yongzhao; Dong, Xiwang; Li, Qingdong; Ren, Zhang
2017-05-18
This paper investigates the time-varying formation robust tracking problems for high-order linear multiagent systems with a leader of unknown control input in the presence of heterogeneous parameter uncertainties and external disturbances. The followers need to accomplish an expected time-varying formation in the state space and track the state trajectory produced by the leader simultaneously. First, a time-varying formation robust tracking protocol with a totally distributed form is proposed utilizing the neighborhood state information. With the adaptive updating mechanism, neither any global knowledge about the communication topology nor the upper bounds of the parameter uncertainties, external disturbances and leader's unknown input are required in the proposed protocol. Then, in order to determine the control parameters, an algorithm with four steps is presented, where feasible conditions for the followers to accomplish the expected time-varying formation tracking are provided. Furthermore, based on the Lyapunov-like analysis theory, it is proved that the formation tracking error can converge to zero asymptotically. Finally, the effectiveness of the theoretical results is verified by simulation examples.
Obtaining reliable Likelihood Ratio tests from simulated likelihood functions
DEFF Research Database (Denmark)
Andersen, Laura Mørch
It is standard practice by researchers and the default option in many statistical programs to base test statistics for mixed models on simulations using asymmetric draws (e.g. Halton draws). This paper shows that when the estimated likelihood functions depend on standard deviations of mixed param...
Uncertainty quantification for environmental models
Hill, Mary C.; Lu, Dan; Kavetski, Dmitri; Clark, Martyn P.; Ye, Ming
2012-01-01
Environmental models are used to evaluate the fate of fertilizers in agricultural settings (including soil denitrification), the degradation of hydrocarbons at spill sites, and water supply for people and ecosystems in small to large basins and cities—to mention but a few applications of these models. They also play a role in understanding and diagnosing potential environmental impacts of global climate change. The models are typically mildly to extremely nonlinear. The persistent demand for enhanced dynamics and resolution to improve model realism [17] means that lengthy individual model execution times will remain common, notwithstanding continued enhancements in computer power. In addition, high-dimensional parameter spaces are often defined, which increases the number of model runs required to quantify uncertainty [2]. Some environmental modeling projects have access to extensive funding and computational resources; many do not. The many recent studies of uncertainty quantification in environmental model predictions have focused on uncertainties related to data error and sparsity of data, expert judgment expressed mathematically through prior information, poorly known parameter values, and model structure (see, for example, [1,7,9,10,13,18]). Approaches for quantifying uncertainty include frequentist (potentially with prior information [7,9]), Bayesian [13,18,19], and likelihood-based. A few of the numerous methods, including some sensitivity and inverse methods with consequences for understanding and quantifying uncertainty, are as follows: Bayesian hierarchical modeling and Bayesian model averaging; single-objective optimization with error-based weighting [7] and multi-objective optimization [3]; methods based on local derivatives [2,7,10]; screening methods like OAT (one at a time) and the method of Morris [14]; FAST (Fourier amplitude sensitivity testing) [14]; the Sobol' method [14]; randomized maximum likelihood [10]; Markov chain Monte Carlo (MCMC) [10
Phylogenetic analysis using parsimony and likelihood methods.
Yang, Z
1996-02-01
allowed to differ between nucleotides or across sites, the probability that MP recovers the true topology, and especially its performance relative to that of the likelihood method, generally deteriorates. As the complexity of the process of nucleotide substitution in real sequences is well recognized, the likelihood method appears preferable to parsimony. However, the development of a statistical methodology for the efficient estimation of the tree topology remains a difficult open problem.
Ego involvement increases doping likelihood.
Ring, Christopher; Kavussanu, Maria
2018-08-01
Achievement goal theory provides a framework to help understand how individuals behave in achievement contexts, such as sport. Evidence concerning the role of motivation in the decision to use banned performance enhancing substances (i.e., doping) is equivocal on this issue. The extant literature shows that dispositional goal orientation has been weakly and inconsistently associated with doping intention and use. It is possible that goal involvement, which describes the situational motivational state, is a stronger determinant of doping intention. Accordingly, the current study used an experimental design to examine the effects of goal involvement, manipulated using direct instructions and reflective writing, on doping likelihood in hypothetical situations in college athletes. The ego-involving goal increased doping likelihood compared to no goal and a task-involving goal. The present findings provide the first evidence that ego involvement can sway the decision to use doping to improve athletic performance.
Multi-Channel Maximum Likelihood Pitch Estimation
DEFF Research Database (Denmark)
Christensen, Mads Græsbøll
2012-01-01
In this paper, a method for multi-channel pitch estimation is proposed. The method is a maximum likelihood estimator and is based on a parametric model where the signals in the various channels share the same fundamental frequency but can have different amplitudes, phases, and noise characteristics....... This essentially means that the model allows for different conditions in the various channels, like different signal-to-noise ratios, microphone characteristics and reverberation. Moreover, the method does not assume that a certain array structure is used but rather relies on a more general model and is hence...
Gaussian copula as a likelihood function for environmental models
Wani, O.; Espadas, G.; Cecinati, F.; Rieckermann, J.
2017-12-01
Parameter estimation of environmental models always comes with uncertainty. To formally quantify this parametric uncertainty, a likelihood function needs to be formulated, which is defined as the probability of observations given fixed values of the parameter set. A likelihood function allows us to infer parameter values from observations using Bayes' theorem. The challenge is to formulate a likelihood function that reliably describes the error generating processes which lead to the observed monitoring data, such as rainfall and runoff. If the likelihood function is not representative of the error statistics, the parameter inference will give biased parameter values. Several uncertainty estimation methods that are currently being used employ Gaussian processes as a likelihood function, because of their favourable analytical properties. Box-Cox transformation is suggested to deal with non-symmetric and heteroscedastic errors e.g. for flow data which are typically more uncertain in high flows than in periods with low flows. Problem with transformations is that the results are conditional on hyper-parameters, for which it is difficult to formulate the analyst's belief a priori. In an attempt to address this problem, in this research work we suggest learning the nature of the error distribution from the errors made by the model in the "past" forecasts. We use a Gaussian copula to generate semiparametric error distributions . 1) We show that this copula can be then used as a likelihood function to infer parameters, breaking away from the practice of using multivariate normal distributions. Based on the results from a didactical example of predicting rainfall runoff, 2) we demonstrate that the copula captures the predictive uncertainty of the model. 3) Finally, we find that the properties of autocorrelation and heteroscedasticity of errors are captured well by the copula, eliminating the need to use transforms. In summary, our findings suggest that copulas are an
Munilla, S; Cantet, R J C
2012-06-01
Consider the estimation of genetic (co)variance components from a maternal animal model (MAM) using a conjugated Bayesian approach. Usually, more uncertainty is expected a priori on the value of the maternal additive variance than on the value of the direct additive variance. However, it is not possible to model such differential uncertainty when assuming an inverted Wishart (IW) distribution for the genetic covariance matrix. Instead, consider the use of a generalized inverted Wishart (GIW) distribution. The GIW is essentially an extension of the IW distribution with a larger set of distinct parameters. In this study, the GIW distribution in its full generality is introduced and theoretical results regarding its use as the prior distribution for the genetic covariance matrix of the MAM are derived. In particular, we prove that the conditional conjugacy property holds so that parameter estimation can be accomplished via the Gibbs sampler. A sampling algorithm is also sketched. Furthermore, we describe how to specify the hyperparameters to account for differential prior opinion on the (co)variance components. A recursive strategy to elicit these parameters is then presented and tested using field records and simulated data. The procedure returned accurate estimates and reduced standard errors when compared with non-informative prior settings while improving the convergence rates. In general, faster convergence was always observed when a stronger weight was placed on the prior distributions. However, analyses based on the IW distribution have also produced biased estimates when the prior means were set to over-dispersed values. © 2011 Blackwell Verlag GmbH.
Likelihood estimators for multivariate extremes
Huser, Raphaë l; Davison, Anthony C.; Genton, Marc G.
2015-01-01
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Likelihood estimators for multivariate extremes
Huser, Raphaël
2015-11-17
The main approach to inference for multivariate extremes consists in approximating the joint upper tail of the observations by a parametric family arising in the limit for extreme events. The latter may be expressed in terms of componentwise maxima, high threshold exceedances or point processes, yielding different but related asymptotic characterizations and estimators. The present paper clarifies the connections between the main likelihood estimators, and assesses their practical performance. We investigate their ability to estimate the extremal dependence structure and to predict future extremes, using exact calculations and simulation, in the case of the logistic model.
Maximum likelihood of phylogenetic networks.
Jin, Guohua; Nakhleh, Luay; Snir, Sagi; Tuller, Tamir
2006-11-01
Horizontal gene transfer (HGT) is believed to be ubiquitous among bacteria, and plays a major role in their genome diversification as well as their ability to develop resistance to antibiotics. In light of its evolutionary significance and implications for human health, developing accurate and efficient methods for detecting and reconstructing HGT is imperative. In this article we provide a new HGT-oriented likelihood framework for many problems that involve phylogeny-based HGT detection and reconstruction. Beside the formulation of various likelihood criteria, we show that most of these problems are NP-hard, and offer heuristics for efficient and accurate reconstruction of HGT under these criteria. We implemented our heuristics and used them to analyze biological as well as synthetic data. In both cases, our criteria and heuristics exhibited very good performance with respect to identifying the correct number of HGT events as well as inferring their correct location on the species tree. Implementation of the criteria as well as heuristics and hardness proofs are available from the authors upon request. Hardness proofs can also be downloaded at http://www.cs.tau.ac.il/~tamirtul/MLNET/Supp-ML.pdf
Lindley, Dennis V
2013-01-01
Praise for the First Edition ""...a reference for everyone who is interested in knowing and handling uncertainty.""-Journal of Applied Statistics The critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty to be handled sensibly in everyday life. These ideas were extended to embrace the scientific method and to show how decisions, containing an uncertain element, could be rationally made.
Likelihood ratio decisions in memory: three implied regularities.
Glanzer, Murray; Hilford, Andrew; Maloney, Laurence T
2009-06-01
We analyze four general signal detection models for recognition memory that differ in their distributional assumptions. Our analyses show that a basic assumption of signal detection theory, the likelihood ratio decision axis, implies three regularities in recognition memory: (1) the mirror effect, (2) the variance effect, and (3) the z-ROC length effect. For each model, we present the equations that produce the three regularities and show, in computed examples, how they do so. We then show that the regularities appear in data from a range of recognition studies. The analyses and data in our study support the following generalization: Individuals make efficient recognition decisions on the basis of likelihood ratios.
Decision-making under great uncertainty
International Nuclear Information System (INIS)
Hansson, S.O.
1992-01-01
Five types of decision-uncertainty are distinguished: uncertainty of consequences, of values, of demarcation, of reliance, and of co-ordination. Strategies are proposed for each type of uncertainty. The general conclusion is that it is meaningful for decision theory to treat cases with greater uncertainty than the textbook case of 'decision-making under uncertainty'. (au)
A Predictive Likelihood Approach to Bayesian Averaging
Directory of Open Access Journals (Sweden)
Tomáš Jeřábek
2015-01-01
Full Text Available Multivariate time series forecasting is applied in a wide range of economic activities related to regional competitiveness and is the basis of almost all macroeconomic analysis. In this paper we combine multivariate density forecasts of GDP growth, inflation and real interest rates from four various models, two type of Bayesian vector autoregression (BVAR models, a New Keynesian dynamic stochastic general equilibrium (DSGE model of small open economy and DSGE-VAR model. The performance of models is identified using historical dates including domestic economy and foreign economy, which is represented by countries of the Eurozone. Because forecast accuracy of observed models are different, the weighting scheme based on the predictive likelihood, the trace of past MSE matrix, model ranks are used to combine the models. The equal-weight scheme is used as a simple combination scheme. The results show that optimally combined densities are comparable to the best individual models.
Koch, Michael
Measurement uncertainty is one of the key issues in quality assurance. It became increasingly important for analytical chemistry laboratories with the accreditation to ISO/IEC 17025. The uncertainty of a measurement is the most important criterion for the decision whether a measurement result is fit for purpose. It also delivers help for the decision whether a specification limit is exceeded or not. Estimation of measurement uncertainty often is not trivial. Several strategies have been developed for this purpose that will shortly be described in this chapter. In addition the different possibilities to take into account the uncertainty in compliance assessment are explained.
Planck 2013 results. XV. CMB power spectra and likelihood
Ade, P.A.R.; Armitage-Caplan, C.; Arnaud, M.; Ashdown, M.; Atrio-Barandela, F.; Aumont, J.; Baccigalupi, C.; Banday, A.J.; Barreiro, R.B.; Bartlett, J.G.; Battaner, E.; Benabed, K.; Benoit, A.; Benoit-Levy, A.; Bernard, J.P.; Bersanelli, M.; Bielewicz, P.; Bobin, J.; Bock, J.J.; Bonaldi, A.; Bonavera, L.; Bond, J.R.; Borrill, J.; Bouchet, F.R.; Boulanger, F.; Bridges, M.; Bucher, M.; Burigana, C.; Butler, R.C.; Calabrese, E.; Cardoso, J.F.; Catalano, A.; Challinor, A.; Chamballu, A.; Chiang, L.Y.; Chiang, H.C.; Christensen, P.R.; Church, S.; Clements, D.L.; Colombi, S.; Colombo, L.P.L.; Combet, C.; Couchot, F.; Coulais, A.; Crill, B.P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R.D.; Davis, R.J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Delouis, J.M.; Desert, F.X.; Dickinson, C.; Diego, J.M.; Dole, H.; Donzelli, S.; Dore, O.; Douspis, M.; Dunkley, J.; Dupac, X.; Efstathiou, G.; Elsner, F.; Ensslin, T.A.; Eriksen, H.K.; Finelli, F.; Forni, O.; Frailis, M.; Fraisse, A.A.; Franceschi, E.; Gaier, T.C.; Galeotta, S.; Galli, S.; Ganga, K.; Giard, M.; Giardino, G.; Giraud-Heraud, Y.; Gjerlow, E.; Gonzalez-Nuevo, J.; Gorski, K.M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J.E.; Hansen, F.K.; Hanson, D.; Harrison, D.; Helou, G.; Henrot-Versille, S.; Hernandez-Monteagudo, C.; Herranz, D.; Hildebrandt, S.R.; Hivon, E.; Hobson, M.; Holmes, W.A.; Hornstrup, A.; Hovest, W.; Huffenberger, K.M.; Hurier, G.; Jaffe, T.R.; Jaffe, A.H.; Jewell, J.; Jones, W.C.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Kiiveri, K.; Kisner, T.S.; Kneissl, R.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lahteenmaki, A.; Lamarre, J.M.; Lasenby, A.; Lattanzi, M.; Laureijs, R.J.; Lawrence, C.R.; Le Jeune, M.; Leach, S.; Leahy, J.P.; Leonardi, R.; Leon-Tavares, J.; Lesgourgues, J.; Liguori, M.; Lilje, P.B.; Lindholm, V.; Linden-Vornle, M.; Lopez-Caniego, M.; Lubin, P.M.; Macias-Perez, J.F.; Maffei, B.; Maino, D.; Mandolesi, N.; Marinucci, D.; Maris, M.; Marshall, D.J.; Martin, P.G.; Martinez-Gonzalez, E.; Masi, S.; Matarrese, S.; Matthai, F.; Mazzotta, P.; Meinhold, P.R.; Melchiorri, A.; Mendes, L.; Menegoni, E.; Mennella, A.; Migliaccio, M.; Millea, M.; Mitra, S.; Miville-Deschenes, M.A.; Molinari, D.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Naselsky, P.; Nati, F.; Natoli, P.; Netterfield, C.B.; Norgaard-Nielsen, H.U.; Noviello, F.; Novikov, D.; Novikov, I.; O'Dwyer, I.J.; Orieux, F.; Osborne, S.; Oxborrow, C.A.; Paci, F.; Pagano, L.; Pajot, F.; Paladini, R.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Paykari, P.; Perdereau, O.; Perotto, L.; Perrotta, F.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Popa, L.; Poutanen, T.; Pratt, G.W.; Prezeau, G.; Prunet, S.; Puget, J.L.; Rachen, J.P.; Rahlin, A.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Ricciardi, S.; Riller, T.; Ringeval, C.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Roudier, G.; Rowan-Robinson, M.; Rubino-Martin, J.A.; Rusholme, B.; Sandri, M.; Sanselme, L.; Santos, D.; Savini, G.; Scott, D.; Seiffert, M.D.; Shellard, E.P.S.; Spencer, L.D.; Starck, J.L.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sureau, F.; Sutton, D.; Suur-Uski, A.S.; Sygnet, J.F.; Tauber, J.A.; Tavagnacco, D.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Turler, M.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Varis, J.; Vielva, P.; Villa, F.; Vittorio, N.; Wade, L.A.; Wandelt, B.D.; Wehus, I.K.; White, M.; White, S.D.M.; Yvon, D.; Zacchei, A.; Zonca, A.
2014-01-01
We present the Planck likelihood, a complete statistical description of the two-point correlation function of the CMB temperature fluctuations. We use this likelihood to derive the Planck CMB power spectrum over three decades in l, covering 2 = 50, we employ a correlated Gaussian likelihood approximation based on angular cross-spectra derived from the 100, 143 and 217 GHz channels. We validate our likelihood through an extensive suite of consistency tests, and assess the impact of residual foreground and instrumental uncertainties on cosmological parameters. We find good internal agreement among the high-l cross-spectra with residuals of a few uK^2 at l <= 1000. We compare our results with foreground-cleaned CMB maps, and with cross-spectra derived from the 70 GHz Planck map, and find broad agreement in terms of spectrum residuals and cosmological parameters. The best-fit LCDM cosmology is in excellent agreement with preliminary Planck polarisation spectra. The standard LCDM cosmology is well constrained b...
Reliability analysis under epistemic uncertainty
International Nuclear Information System (INIS)
Nannapaneni, Saideep; Mahadevan, Sankaran
2016-01-01
This paper proposes a probabilistic framework to include both aleatory and epistemic uncertainty within model-based reliability estimation of engineering systems for individual limit states. Epistemic uncertainty is considered due to both data and model sources. Sparse point and/or interval data regarding the input random variables leads to uncertainty regarding their distribution types, distribution parameters, and correlations; this statistical uncertainty is included in the reliability analysis through a combination of likelihood-based representation, Bayesian hypothesis testing, and Bayesian model averaging techniques. Model errors, which include numerical solution errors and model form errors, are quantified through Gaussian process models and included in the reliability analysis. The probability integral transform is used to develop an auxiliary variable approach that facilitates a single-level representation of both aleatory and epistemic uncertainty. This strategy results in an efficient single-loop implementation of Monte Carlo simulation (MCS) and FORM/SORM techniques for reliability estimation under both aleatory and epistemic uncertainty. Two engineering examples are used to demonstrate the proposed methodology. - Highlights: • Epistemic uncertainty due to data and model included in reliability analysis. • A novel FORM-based approach proposed to include aleatory and epistemic uncertainty. • A single-loop Monte Carlo approach proposed to include both types of uncertainties. • Two engineering examples used for illustration.
Conditional uncertainty principle
Gour, Gilad; Grudka, Andrzej; Horodecki, Michał; Kłobus, Waldemar; Łodyga, Justyna; Narasimhachar, Varun
2018-04-01
We develop a general operational framework that formalizes the concept of conditional uncertainty in a measure-independent fashion. Our formalism is built upon a mathematical relation which we call conditional majorization. We define conditional majorization and, for the case of classical memory, we provide its thorough characterization in terms of monotones, i.e., functions that preserve the partial order under conditional majorization. We demonstrate the application of this framework by deriving two types of memory-assisted uncertainty relations, (1) a monotone-based conditional uncertainty relation and (2) a universal measure-independent conditional uncertainty relation, both of which set a lower bound on the minimal uncertainty that Bob has about Alice's pair of incompatible measurements, conditioned on arbitrary measurement that Bob makes on his own system. We next compare the obtained relations with their existing entropic counterparts and find that they are at least independent.
Gentes, Emily L; Ruscio, Ayelet Meron
2011-08-01
Intolerance of uncertainty (IU) has been suggested to reflect a specific risk factor for generalized anxiety disorder (GAD), but there have been no systematic attempts to evaluate the specificity of IU to GAD. This meta-analysis examined the cross-sectional association of IU with symptoms of GAD, major depressive disorder (MDD), and obsessive-compulsive disorder (OCD). Random effects analyses were conducted for two common definitions of IU, one that has predominated in studies of GAD (56 effect sizes) and another that has been favored in studies of OCD (29 effect sizes). Using the definition of IU developed for GAD, IU shared a mean correlation of .57 with GAD, .53 with MDD, and .50 with OCD. Using the alternate definition developed for OCD, IU shared a mean correlation of .46 with MDD and .42 with OCD, with no studies available for GAD. Post-hoc significance tests revealed that IU was more strongly related to GAD than to OCD when the GAD-specific definition of IU was used. No other differences were found in the magnitude of associations between IU and the three syndromes. We discuss implications of these findings for models of shared and specific features of emotional disorders and for future research efforts. Copyright © 2011 Elsevier Ltd. All rights reserved.
Uncertainty in hydrological signatures
McMillan, Hilary; Westerberg, Ida
2015-04-01
Information that summarises the hydrological behaviour or flow regime of a catchment is essential for comparing responses of different catchments to understand catchment organisation and similarity, and for many other modelling and water-management applications. Such information types derived as an index value from observed data are known as hydrological signatures, and can include descriptors of high flows (e.g. mean annual flood), low flows (e.g. mean annual low flow, recession shape), the flow variability, flow duration curve, and runoff ratio. Because the hydrological signatures are calculated from observed data such as rainfall and flow records, they are affected by uncertainty in those data. Subjective choices in the method used to calculate the signatures create a further source of uncertainty. Uncertainties in the signatures may affect our ability to compare different locations, to detect changes, or to compare future water resource management scenarios. The aim of this study was to contribute to the hydrological community's awareness and knowledge of data uncertainty in hydrological signatures, including typical sources, magnitude and methods for its assessment. We proposed a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrated it for a variety of commonly used signatures. The study was made for two data rich catchments, the 50 km2 Mahurangi catchment in New Zealand and the 135 km2 Brue catchment in the UK. For rainfall data the uncertainty sources included point measurement uncertainty, the number of gauges used in calculation of the catchment spatial average, and uncertainties relating to lack of quality control. For flow data the uncertainty sources included uncertainties in stage/discharge measurement and in the approximation of the true stage-discharge relation by a rating curve. The resulting uncertainties were compared across the different signatures and catchments, to quantify uncertainty
A maximum likelihood framework for protein design
Directory of Open Access Journals (Sweden)
Philippe Hervé
2006-06-01
Full Text Available Abstract Background The aim of protein design is to predict amino-acid sequences compatible with a given target structure. Traditionally envisioned as a purely thermodynamic question, this problem can also be understood in a wider context, where additional constraints are captured by learning the sequence patterns displayed by natural proteins of known conformation. In this latter perspective, however, we still need a theoretical formalization of the question, leading to general and efficient learning methods, and allowing for the selection of fast and accurate objective functions quantifying sequence/structure compatibility. Results We propose a formulation of the protein design problem in terms of model-based statistical inference. Our framework uses the maximum likelihood principle to optimize the unknown parameters of a statistical potential, which we call an inverse potential to contrast with classical potentials used for structure prediction. We propose an implementation based on Markov chain Monte Carlo, in which the likelihood is maximized by gradient descent and is numerically estimated by thermodynamic integration. The fit of the models is evaluated by cross-validation. We apply this to a simple pairwise contact potential, supplemented with a solvent-accessibility term, and show that the resulting models have a better predictive power than currently available pairwise potentials. Furthermore, the model comparison method presented here allows one to measure the relative contribution of each component of the potential, and to choose the optimal number of accessibility classes, which turns out to be much higher than classically considered. Conclusion Altogether, this reformulation makes it possible to test a wide diversity of models, using different forms of potentials, or accounting for other factors than just the constraint of thermodynamic stability. Ultimately, such model-based statistical analyses may help to understand the forces
The Laplace Likelihood Ratio Test for Heteroscedasticity
Directory of Open Access Journals (Sweden)
J. Martin van Zyl
2011-01-01
Full Text Available It is shown that the likelihood ratio test for heteroscedasticity, assuming the Laplace distribution, gives good results for Gaussian and fat-tailed data. The likelihood ratio test, assuming normality, is very sensitive to any deviation from normality, especially when the observations are from a distribution with fat tails. Such a likelihood test can also be used as a robust test for a constant variance in residuals or a time series if the data is partitioned into groups.
MXLKID: a maximum likelihood parameter identifier
International Nuclear Information System (INIS)
Gavel, D.T.
1980-07-01
MXLKID (MaXimum LiKelihood IDentifier) is a computer program designed to identify unknown parameters in a nonlinear dynamic system. Using noisy measurement data from the system, the maximum likelihood identifier computes a likelihood function (LF). Identification of system parameters is accomplished by maximizing the LF with respect to the parameters. The main body of this report briefly summarizes the maximum likelihood technique and gives instructions and examples for running the MXLKID program. MXLKID is implemented LRLTRAN on the CDC7600 computer at LLNL. A detailed mathematical description of the algorithm is given in the appendices. 24 figures, 6 tables
Event based uncertainty assessment in urban drainage modelling, applying the GLUE methodology
DEFF Research Database (Denmark)
Thorndahl, Søren; Beven, K.J.; Jensen, Jacob Birk
2008-01-01
of combined sewer overflow. The GLUE methodology is used to test different conceptual setups in order to determine if one model setup gives a better goodness of fit conditional on the observations than the other. Moreover, different methodological investigations of GLUE are conducted in order to test......In the present paper an uncertainty analysis on an application of the commercial urban drainage model MOUSE is conducted. Applying the Generalized Likelihood Uncertainty Estimation (GLUE) methodology the model is conditioned on observation time series from two flow gauges as well as the occurrence...... if the uncertainty analysis is unambiguous. It is shown that the GLUE methodology is very applicable in uncertainty analysis of this application of an urban drainage model, although it was shown to be quite difficult of get good fits of the whole time series....
Nearly Efficient Likelihood Ratio Tests for Seasonal Unit Roots
DEFF Research Database (Denmark)
Jansson, Michael; Nielsen, Morten Ørregaard
In an important generalization of zero frequency autore- gressive unit root tests, Hylleberg, Engle, Granger, and Yoo (1990) developed regression-based tests for unit roots at the seasonal frequencies in quarterly time series. We develop likelihood ratio tests for seasonal unit roots and show...... that these tests are "nearly efficient" in the sense of Elliott, Rothenberg, and Stock (1996), i.e. that their local asymptotic power functions are indistinguishable from the Gaussian power envelope. Currently available nearly efficient testing procedures for seasonal unit roots are regression-based and require...... the choice of a GLS detrending parameter, which our likelihood ratio tests do not....
Planck 2015 results: XI. CMB power spectra, likelihoods, and robustness of parameters
DEFF Research Database (Denmark)
Aghanim, N.; Arnaud, M.; Ashdown, M.
2016-01-01
on the same hybrid approach used for the previous release, i.e., a pixel-based likelihood at low multipoles (ℓ data and of Planck polarization......This paper presents the Planck 2015 likelihoods, statistical descriptions of the 2-point correlationfunctions of the cosmic microwave background (CMB) temperature and polarization fluctuations that account for relevant uncertainties, both instrumental and astrophysical in nature. They are based...... information, along with more detailed models of foregrounds and instrumental uncertainties. The increased redundancy brought by more than doubling the amount of data analysed enables further consistency checks and enhanced immunity to systematic effects. It also improves the constraining power of Planck...
Liu, Baoding
2015-01-01
When no samples are available to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degree that each event will happen. Perhaps some people think that the belief degree should be modeled by subjective probability or fuzzy set theory. However, it is usually inappropriate because both of them may lead to counterintuitive results in this case. In order to rationally deal with belief degrees, uncertainty theory was founded in 2007 and subsequently studied by many researchers. Nowadays, uncertainty theory has become a branch of axiomatic mathematics for modeling belief degrees. This is an introductory textbook on uncertainty theory, uncertain programming, uncertain statistics, uncertain risk analysis, uncertain reliability analysis, uncertain set, uncertain logic, uncertain inference, uncertain process, uncertain calculus, and uncertain differential equation. This textbook also shows applications of uncertainty theory to scheduling, logistics, networks, data mining, c...
DarkBit. A GAMBIT module for computing dark matter observables and likelihoods
Energy Technology Data Exchange (ETDEWEB)
Bringmann, Torsten; Dal, Lars A. [University of Oslo, Department of Physics, Oslo (Norway); Conrad, Jan; Edsjoe, Joakim; Farmer, Ben [AlbaNova University Centre, Oskar Klein Centre for Cosmoparticle Physics, Stockholm (Sweden); Stockholm University, Department of Physics, Stockholm (Sweden); Cornell, Jonathan M. [McGill University, Department of Physics, Montreal, QC (Canada); Kahlhoefer, Felix; Wild, Sebastian [DESY, Hamburg (Germany); Kvellestad, Anders; Savage, Christopher [NORDITA, Stockholm (Sweden); Putze, Antje [LAPTh, Universite de Savoie, CNRS, Annecy-le-Vieux (France); Scott, Pat [Blackett Laboratory, Imperial College London, Department of Physics, London (United Kingdom); Weniger, Christoph [University of Amsterdam, GRAPPA, Institute of Physics, Amsterdam (Netherlands); White, Martin [University of Adelaide, Department of Physics, Adelaide, SA (Australia); Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale, Parkville (Australia); Collaboration: The GAMBIT Dark Matter Workgroup
2017-12-15
We introduce DarkBit, an advanced software code for computing dark matter constraints on various extensions to the Standard Model of particle physics, comprising both new native code and interfaces to external packages. This release includes a dedicated signal yield calculator for gamma-ray observations, which significantly extends current tools by implementing a cascade-decay Monte Carlo, as well as a dedicated likelihood calculator for current and future experiments (gamLike). This provides a general solution for studying complex particle physics models that predict dark matter annihilation to a multitude of final states. We also supply a direct detection package that models a large range of direct detection experiments (DDCalc), and that provides the corresponding likelihoods for arbitrary combinations of spin-independent and spin-dependent scattering processes. Finally, we provide custom relic density routines along with interfaces to DarkSUSY, micrOMEGAs, and the neutrino telescope likelihood package nulike. DarkBit is written in the framework of the Global And Modular Beyond the Standard Model Inference Tool (GAMBIT), providing seamless integration into a comprehensive statistical fitting framework that allows users to explore new models with both particle and astrophysics constraints, and a consistent treatment of systematic uncertainties. In this paper we describe its main functionality, provide a guide to getting started quickly, and show illustrative examples for results obtained with DarkBit (both as a stand-alone tool and as a GAMBIT module). This includes a quantitative comparison between two of the main dark matter codes (DarkSUSY and micrOMEGAs), and application of DarkBit's advanced direct and indirect detection routines to a simple effective dark matter model. (orig.)
Pendeteksian Outlier pada Regresi Nonlinier dengan Metode statistik Likelihood Displacement
Directory of Open Access Journals (Sweden)
Siti Tabi'atul Hasanah
2012-11-01
Full Text Available Outlier is an observation that much different (extreme from the other observational data, or data can be interpreted that do not follow the general pattern of the model. Sometimes outliers provide information that can not be provided by other data. That's why outliers should not just be eliminated. Outliers can also be an influential observation. There are many methods that can be used to detect of outliers. In previous studies done on outlier detection of linear regression. Next will be developed detection of outliers in nonlinear regression. Nonlinear regression here is devoted to multiplicative nonlinear regression. To detect is use of statistical method likelihood displacement. Statistical methods abbreviated likelihood displacement (LD is a method to detect outliers by removing the suspected outlier data. To estimate the parameters are used to the maximum likelihood method, so we get the estimate of the maximum. By using LD method is obtained i.e likelihood displacement is thought to contain outliers. Further accuracy of LD method in detecting the outliers are shown by comparing the MSE of LD with the MSE from the regression in general. Statistic test used is Λ. Initial hypothesis was rejected when proved so is an outlier.
Insurance Applications of Active Fault Maps Showing Epistemic Uncertainty
Woo, G.
2005-12-01
Insurance loss modeling for earthquakes utilizes available maps of active faulting produced by geoscientists. All such maps are subject to uncertainty, arising from lack of knowledge of fault geometry and rupture history. Field work to undertake geological fault investigations drains human and monetary resources, and this inevitably limits the resolution of fault parameters. Some areas are more accessible than others; some may be of greater social or economic importance than others; some areas may be investigated more rapidly or diligently than others; or funding restrictions may have curtailed the extent of the fault mapping program. In contrast with the aleatory uncertainty associated with the inherent variability in the dynamics of earthquake fault rupture, uncertainty associated with lack of knowledge of fault geometry and rupture history is epistemic. The extent of this epistemic uncertainty may vary substantially from one regional or national fault map to another. However aware the local cartographer may be, this uncertainty is generally not conveyed in detail to the international map user. For example, an area may be left blank for a variety of reasons, ranging from lack of sufficient investigation of a fault to lack of convincing evidence of activity. Epistemic uncertainty in fault parameters is of concern in any probabilistic assessment of seismic hazard, not least in insurance earthquake risk applications. A logic-tree framework is appropriate for incorporating epistemic uncertainty. Some insurance contracts cover specific high-value properties or transport infrastructure, and therefore are extremely sensitive to the geometry of active faulting. Alternative Risk Transfer (ART) to the capital markets may also be considered. In order for such insurance or ART contracts to be properly priced, uncertainty should be taken into account. Accordingly, an estimate is needed for the likelihood of surface rupture capable of causing severe damage. Especially where a
Dimension-independent likelihood-informed MCMC
Cui, Tiangang
2015-10-08
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that represent the discretization of an underlying function. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterior distribution over functions. Two distinct lines of research intersect in the methods developed here. First, we introduce a general class of operator-weighted proposal distributions that are well defined on function space, such that the performance of the resulting MCMC samplers is independent of the discretization of the function. Second, by exploiting local Hessian information and any associated low-dimensional structure in the change from prior to posterior distributions, we develop an inhomogeneous discretization scheme for the Langevin stochastic differential equation that yields operator-weighted proposals adapted to the non-Gaussian structure of the posterior. The resulting dimension-independent and likelihood-informed (DILI) MCMC samplers may be useful for a large class of high-dimensional problems where the target probability measure has a density with respect to a Gaussian reference measure. Two nonlinear inverse problems are used to demonstrate the efficiency of these DILI samplers: an elliptic PDE coefficient inverse problem and path reconstruction in a conditioned diffusion.
Dimension-independent likelihood-informed MCMC
Cui, Tiangang; Law, Kody; Marzouk, Youssef M.
2015-01-01
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters that represent the discretization of an underlying function. This work introduces a family of Markov chain Monte Carlo (MCMC) samplers that can adapt to the particular structure of a posterior distribution over functions. Two distinct lines of research intersect in the methods developed here. First, we introduce a general class of operator-weighted proposal distributions that are well defined on function space, such that the performance of the resulting MCMC samplers is independent of the discretization of the function. Second, by exploiting local Hessian information and any associated low-dimensional structure in the change from prior to posterior distributions, we develop an inhomogeneous discretization scheme for the Langevin stochastic differential equation that yields operator-weighted proposals adapted to the non-Gaussian structure of the posterior. The resulting dimension-independent and likelihood-informed (DILI) MCMC samplers may be useful for a large class of high-dimensional problems where the target probability measure has a density with respect to a Gaussian reference measure. Two nonlinear inverse problems are used to demonstrate the efficiency of these DILI samplers: an elliptic PDE coefficient inverse problem and path reconstruction in a conditioned diffusion.
Exclusion probabilities and likelihood ratios with applications to mixtures.
Slooten, Klaas-Jan; Egeland, Thore
2016-01-01
The statistical evidence obtained from mixed DNA profiles can be summarised in several ways in forensic casework including the likelihood ratio (LR) and the Random Man Not Excluded (RMNE) probability. The literature has seen a discussion of the advantages and disadvantages of likelihood ratios and exclusion probabilities, and part of our aim is to bring some clarification to this debate. In a previous paper, we proved that there is a general mathematical relationship between these statistics: RMNE can be expressed as a certain average of the LR, implying that the expected value of the LR, when applied to an actual contributor to the mixture, is at least equal to the inverse of the RMNE. While the mentioned paper presented applications for kinship problems, the current paper demonstrates the relevance for mixture cases, and for this purpose, we prove some new general properties. We also demonstrate how to use the distribution of the likelihood ratio for donors of a mixture, to obtain estimates for exceedance probabilities of the LR for non-donors, of which the RMNE is a special case corresponding to L R>0. In order to derive these results, we need to view the likelihood ratio as a random variable. In this paper, we describe how such a randomization can be achieved. The RMNE is usually invoked only for mixtures without dropout. In mixtures, artefacts like dropout and drop-in are commonly encountered and we address this situation too, illustrating our results with a basic but widely implemented model, a so-called binary model. The precise definitions, modelling and interpretation of the required concepts of dropout and drop-in are not entirely obvious, and we attempt to clarify them here in a general likelihood framework for a binary model.
Li, Heling; Ren, Jinxiu; Wang, Wenwei; Yang, Bin; Shen, Hongjun
2018-02-01
Using the semi-classical (Thomas-Fermi) approximation, the thermodynamic properties of ideal Fermi gases in a harmonic potential in an n-dimensional space are studied under the generalized uncertainty principle (GUP). The mean particle number, internal energy, heat capacity and other thermodynamic variables of the Fermi system are calculated analytically. Then, analytical expressions of the mean particle number, internal energy, heat capacity, chemical potential, Fermi energy, ground state energy and amendments of the GUP are obtained at low temperatures. The influence of both the GUP and the harmonic potential on the thermodynamic properties of a copper-electron gas and other systems with higher electron densities are studied numerically at low temperatures. We find: (1) When the GUP is considered, the influence of the harmonic potential is very much larger, and the amendments produced by the GUP increase by eight to nine orders of magnitude compared to when no external potential is applied to the electron gas. (2) The larger the particle density, or the smaller the particle masses, the bigger the influence of the GUP. (3) The effect of the GUP increases with the increase in the spatial dimensions. (4) The amendments of the chemical potential, Fermi energy and ground state energy increase with an increase in temperature, while the heat capacity decreases. T F0 is the Fermi temperature of the ideal Fermi system in a harmonic potential. When the temperature is lower than a certain value (0.22 times T F0 for the copper-electron gas, and this value decreases with increasing electron density), the amendment to the internal energy is positive, however, the amendment decreases with increasing temperature. When the temperature increases to the value, the amendment is zero, and when the temperature is higher than the value, the amendment to the internal energy is negative and the absolute value of the amendment increases with increasing temperature. (5) When electron
Duerdoth, Ian
2009-01-01
The subject of uncertainties (sometimes called errors) is traditionally taught (to first-year science undergraduates) towards the end of a course on statistics that defines probability as the limit of many trials, and discusses probability distribution functions and the Gaussian distribution. We show how to introduce students to the concepts of…
DEFF Research Database (Denmark)
Heydorn, Kaj; Anglov, Thomas
2002-01-01
Methods recommended by the International Standardization Organisation and Eurachem are not satisfactory for the correct estimation of calibration uncertainty. A novel approach is introduced and tested on actual calibration data for the determination of Pb by ICP-AES. The improved calibration...
Embracing uncertainty in applied ecology.
Milner-Gulland, E J; Shea, K
2017-12-01
Applied ecologists often face uncertainty that hinders effective decision-making.Common traps that may catch the unwary are: ignoring uncertainty, acknowledging uncertainty but ploughing on, focussing on trivial uncertainties, believing your models, and unclear objectives.We integrate research insights and examples from a wide range of applied ecological fields to illustrate advances that are generally underused, but could facilitate ecologists' ability to plan and execute research to support management.Recommended approaches to avoid uncertainty traps are: embracing models, using decision theory, using models more effectively, thinking experimentally, and being realistic about uncertainty. Synthesis and applications . Applied ecologists can become more effective at informing management by using approaches that explicitly take account of uncertainty.
Asymptotic Likelihood Distribution for Correlated & Constrained Systems
Agarwal, Ujjwal
2016-01-01
It describes my work as summer student at CERN. The report discusses the asymptotic distribution of the likelihood ratio for total no. of parameters being h and 2 out of these being are constrained and correlated.
Maximum-Likelihood Detection Of Noncoherent CPM
Divsalar, Dariush; Simon, Marvin K.
1993-01-01
Simplified detectors proposed for use in maximum-likelihood-sequence detection of symbols in alphabet of size M transmitted by uncoded, full-response continuous phase modulation over radio channel with additive white Gaussian noise. Structures of receivers derived from particular interpretation of maximum-likelihood metrics. Receivers include front ends, structures of which depends only on M, analogous to those in receivers of coherent CPM. Parts of receivers following front ends have structures, complexity of which would depend on N.
Maximum Likelihood Estimation and Inference With Examples in R, SAS and ADMB
Millar, Russell B
2011-01-01
This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statis
Sampling variability in forensic likelihood-ratio computation: A simulation study
Ali, Tauseef; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Meuwly, Didier
2015-01-01
Recently, in the forensic biometric community, there is a growing interest to compute a metric called “likelihood- ratio‿ when a pair of biometric specimens is compared using a biometric recognition system. Generally, a biomet- ric recognition system outputs a score and therefore a likelihood-ratio
Some remarks on modeling uncertainties
International Nuclear Information System (INIS)
Ronen, Y.
1983-01-01
Several topics related to the question of modeling uncertainties are considered. The first topic is related to the use of the generalized bias operator method for modeling uncertainties. The method is expanded to a more general form of operators. The generalized bias operator is also used in the inverse problem and applied to determine the anisotropic scattering law. The last topic discussed is related to the question of the limit to accuracy and how to establish its value. (orig.) [de
Planck 2013 results. XV. CMB power spectra and likelihood
Planck Collaboration; Ade, P. A. R.; Aghanim, N.; Armitage-Caplan, C.; Arnaud, M.; Ashdown, M.; Atrio-Barandela, F.; Aumont, J.; Baccigalupi, C.; Banday, A. J.; Barreiro, R. B.; Bartlett, J. G.; Battaner, E.; Benabed, K.; Benoît, A.; Benoit-Lévy, A.; Bernard, J.-P.; Bersanelli, M.; Bielewicz, P.; Bobin, J.; Bock, J. J.; Bonaldi, A.; Bonavera, L.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Boulanger, F.; Bridges, M.; Bucher, M.; Burigana, C.; Butler, R. C.; Calabrese, E.; Cardoso, J.-F.; Catalano, A.; Challinor, A.; Chamballu, A.; Chiang, H. C.; Chiang, L.-Y.; Christensen, P. R.; Church, S.; Clements, D. L.; Colombi, S.; Colombo, L. P. L.; Combet, C.; Couchot, F.; Coulais, A.; Crill, B. P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R. D.; Davis, R. J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Delouis, J.-M.; Désert, F.-X.; Dickinson, C.; Diego, J. M.; Dole, H.; Donzelli, S.; Doré, O.; Douspis, M.; Dunkley, J.; Dupac, X.; Efstathiou, G.; Elsner, F.; Enßlin, T. A.; Eriksen, H. K.; Finelli, F.; Forni, O.; Frailis, M.; Fraisse, A. A.; Franceschi, E.; Gaier, T. C.; Galeotta, S.; Galli, S.; Ganga, K.; Giard, M.; Giardino, G.; Giraud-Héraud, Y.; Gjerløw, E.; González-Nuevo, J.; Górski, K. M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J. E.; Hansen, F. K.; Hanson, D.; Harrison, D.; Helou, G.; Henrot-Versillé, S.; Hernández-Monteagudo, C.; Herranz, D.; Hildebrandt, S. R.; Hivon, E.; Hobson, M.; Holmes, W. A.; Hornstrup, A.; Hovest, W.; Huffenberger, K. M.; Hurier, G.; Jaffe, A. H.; Jaffe, T. R.; Jewell, J.; Jones, W. C.; Juvela, M.; Keihänen, E.; Keskitalo, R.; Kiiveri, K.; Kisner, T. S.; Kneissl, R.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lähteenmäki, A.; Lamarre, J.-M.; Lasenby, A.; Lattanzi, M.; Laureijs, R. J.; Lawrence, C. R.; Le Jeune, M.; Leach, S.; Leahy, J. P.; Leonardi, R.; León-Tavares, J.; Lesgourgues, J.; Liguori, M.; Lilje, P. B.; Linden-Vørnle, M.; Lindholm, V.; López-Caniego, M.; Lubin, P. M.; Macías-Pérez, J. F.; Maffei, B.; Maino, D.; Mandolesi, N.; Marinucci, D.; Maris, M.; Marshall, D. J.; Martin, P. G.; Martínez-González, E.; Masi, S.; Massardi, M.; Matarrese, S.; Matthai, F.; Mazzotta, P.; Meinhold, P. R.; Melchiorri, A.; Mendes, L.; Menegoni, E.; Mennella, A.; Migliaccio, M.; Millea, M.; Mitra, S.; Miville-Deschênes, M.-A.; Molinari, D.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Murphy, J. A.; Naselsky, P.; Nati, F.; Natoli, P.; Netterfield, C. B.; Nørgaard-Nielsen, H. U.; Noviello, F.; Novikov, D.; Novikov, I.; O'Dwyer, I. J.; Orieux, F.; Osborne, S.; Oxborrow, C. A.; Paci, F.; Pagano, L.; Pajot, F.; Paladini, R.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Paykari, P.; Perdereau, O.; Perotto, L.; Perrotta, F.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Popa, L.; Poutanen, T.; Pratt, G. W.; Prézeau, G.; Prunet, S.; Puget, J.-L.; Rachen, J. P.; Rahlin, A.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Ricciardi, S.; Riller, T.; Ringeval, C.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Roudier, G.; Rowan-Robinson, M.; Rubiño-Martín, J. A.; Rusholme, B.; Sandri, M.; Sanselme, L.; Santos, D.; Savini, G.; Scott, D.; Seiffert, M. D.; Shellard, E. P. S.; Spencer, L. D.; Starck, J.-L.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sureau, F.; Sutton, D.; Suur-Uski, A.-S.; Sygnet, J.-F.; Tauber, J. A.; Tavagnacco, D.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Türler, M.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Varis, J.; Vielva, P.; Villa, F.; Vittorio, N.; Wade, L. A.; Wandelt, B. D.; Wehus, I. K.; White, M.; White, S. D. M.; Yvon, D.; Zacchei, A.; Zonca, A.
2014-11-01
This paper presents the Planck 2013 likelihood, a complete statistical description of the two-point correlation function of the CMB temperature fluctuations that accounts for all known relevant uncertainties, both instrumental and astrophysical in nature. We use this likelihood to derive our best estimate of the CMB angular power spectrum from Planck over three decades in multipole moment, ℓ, covering 2 ≤ ℓ ≤ 2500. The main source of uncertainty at ℓ ≲ 1500 is cosmic variance. Uncertainties in small-scale foreground modelling and instrumental noise dominate the error budget at higher ℓs. For ℓ impact of residual foreground and instrumental uncertainties on the final cosmological parameters. We find good internal agreement among the high-ℓ cross-spectra with residuals below a few μK2 at ℓ ≲ 1000, in agreement with estimated calibration uncertainties. We compare our results with foreground-cleaned CMB maps derived from all Planck frequencies, as well as with cross-spectra derived from the 70 GHz Planck map, and find broad agreement in terms of spectrum residuals and cosmological parameters. We further show that the best-fit ΛCDM cosmology is in excellent agreement with preliminary PlanckEE and TE polarisation spectra. We find that the standard ΛCDM cosmology is well constrained by Planck from the measurements at ℓ ≲ 1500. One specific example is the spectral index of scalar perturbations, for which we report a 5.4σ deviation from scale invariance, ns = 1. Increasing the multipole range beyond ℓ ≃ 1500 does not increase our accuracy for the ΛCDM parameters, but instead allows us to study extensions beyond the standard model. We find no indication of significant departures from the ΛCDM framework. Finally, we report a tension between the Planck best-fit ΛCDM model and the low-ℓ spectrum in the form of a power deficit of 5-10% at ℓ ≲ 40, with a statistical significance of 2.5-3σ. Without a theoretically motivated model for
Comparison of likelihood testing procedures for parallel systems with covariances
International Nuclear Information System (INIS)
Ayman Baklizi; Isa Daud; Noor Akma Ibrahim
1998-01-01
In this paper we considered investigating and comparing the behavior of the likelihood ratio, the Rao's and the Wald's statistics for testing hypotheses on the parameters of the simple linear regression model based on parallel systems with covariances. These statistics are asymptotically equivalent (Barndorff-Nielsen and Cox, 1994). However, their relative performances in finite samples are generally known. A Monte Carlo experiment is conducted to stimulate the sizes and the powers of these statistics for complete samples and in the presence of time censoring. Comparisons of the statistics are made according to the attainment of assumed size of the test and their powers at various points in the parameter space. The results show that the likelihood ratio statistics appears to have the best performance in terms of the attainment of the assumed size of the test. Power comparisons show that the Rao statistic has some advantage over the Wald statistic in almost all of the space of alternatives while likelihood ratio statistic occupies either the first or the last position in term of power. Overall, the likelihood ratio statistic appears to be more appropriate to the model under study, especially for small sample sizes
Pharmacological Fingerprints of Contextual Uncertainty.
Directory of Open Access Journals (Sweden)
Louise Marshall
2016-11-01
Full Text Available Successful interaction with the environment requires flexible updating of our beliefs about the world. By estimating the likelihood of future events, it is possible to prepare appropriate actions in advance and execute fast, accurate motor responses. According to theoretical proposals, agents track the variability arising from changing environments by computing various forms of uncertainty. Several neuromodulators have been linked to uncertainty signalling, but comprehensive empirical characterisation of their relative contributions to perceptual belief updating, and to the selection of motor responses, is lacking. Here we assess the roles of noradrenaline, acetylcholine, and dopamine within a single, unified computational framework of uncertainty. Using pharmacological interventions in a sample of 128 healthy human volunteers and a hierarchical Bayesian learning model, we characterise the influences of noradrenergic, cholinergic, and dopaminergic receptor antagonism on individual computations of uncertainty during a probabilistic serial reaction time task. We propose that noradrenaline influences learning of uncertain events arising from unexpected changes in the environment. In contrast, acetylcholine balances attribution of uncertainty to chance fluctuations within an environmental context, defined by a stable set of probabilistic associations, or to gross environmental violations following a contextual switch. Dopamine supports the use of uncertainty representations to engender fast, adaptive responses.
DEFF Research Database (Denmark)
Nguyen, Daniel Xuyen
This paper presents a model of trade that explains why firms wait to export and why many exporters fail. Firms face uncertain demands that are only realized after the firm enters the destination. The model retools the timing of uncertainty resolution found in productivity heterogeneity models....... This retooling addresses several shortcomings. First, the imperfect correlation of demands reconciles the sales variation observed in and across destinations. Second, since demands for the firm's output are correlated across destinations, a firm can use previously realized demands to forecast unknown demands...... in untested destinations. The option to forecast demands causes firms to delay exporting in order to gather more information about foreign demand. Third, since uncertainty is resolved after entry, many firms enter a destination and then exit after learning that they cannot profit. This prediction reconciles...
Maximum Likelihood and Restricted Likelihood Solutions in Multiple-Method Studies.
Rukhin, Andrew L
2011-01-01
A formulation of the problem of combining data from several sources is discussed in terms of random effects models. The unknown measurement precision is assumed not to be the same for all methods. We investigate maximum likelihood solutions in this model. By representing the likelihood equations as simultaneous polynomial equations, the exact form of the Groebner basis for their stationary points is derived when there are two methods. A parametrization of these solutions which allows their comparison is suggested. A numerical method for solving likelihood equations is outlined, and an alternative to the maximum likelihood method, the restricted maximum likelihood, is studied. In the situation when methods variances are considered to be known an upper bound on the between-method variance is obtained. The relationship between likelihood equations and moment-type equations is also discussed.
Uncertainty, joint uncertainty, and the quantum uncertainty principle
International Nuclear Information System (INIS)
Narasimhachar, Varun; Poostindouz, Alireza; Gour, Gilad
2016-01-01
Historically, the element of uncertainty in quantum mechanics has been expressed through mathematical identities called uncertainty relations, a great many of which continue to be discovered. These relations use diverse measures to quantify uncertainty (and joint uncertainty). In this paper we use operational information-theoretic principles to identify the common essence of all such measures, thereby defining measure-independent notions of uncertainty and joint uncertainty. We find that most existing entropic uncertainty relations use measures of joint uncertainty that yield themselves to a small class of operational interpretations. Our notion relaxes this restriction, revealing previously unexplored joint uncertainty measures. To illustrate the utility of our formalism, we derive an uncertainty relation based on one such new measure. We also use our formalism to gain insight into the conditions under which measure-independent uncertainty relations can be found. (paper)
Modeling gene expression measurement error: a quasi-likelihood approach
Directory of Open Access Journals (Sweden)
Strimmer Korbinian
2003-03-01
Full Text Available Abstract Background Using suitable error models for gene expression measurements is essential in the statistical analysis of microarray data. However, the true probabilistic model underlying gene expression intensity readings is generally not known. Instead, in currently used approaches some simple parametric model is assumed (usually a transformed normal distribution or the empirical distribution is estimated. However, both these strategies may not be optimal for gene expression data, as the non-parametric approach ignores known structural information whereas the fully parametric models run the risk of misspecification. A further related problem is the choice of a suitable scale for the model (e.g. observed vs. log-scale. Results Here a simple semi-parametric model for gene expression measurement error is presented. In this approach inference is based an approximate likelihood function (the extended quasi-likelihood. Only partial knowledge about the unknown true distribution is required to construct this function. In case of gene expression this information is available in the form of the postulated (e.g. quadratic variance structure of the data. As the quasi-likelihood behaves (almost like a proper likelihood, it allows for the estimation of calibration and variance parameters, and it is also straightforward to obtain corresponding approximate confidence intervals. Unlike most other frameworks, it also allows analysis on any preferred scale, i.e. both on the original linear scale as well as on a transformed scale. It can also be employed in regression approaches to model systematic (e.g. array or dye effects. Conclusions The quasi-likelihood framework provides a simple and versatile approach to analyze gene expression data that does not make any strong distributional assumptions about the underlying error model. For several simulated as well as real data sets it provides a better fit to the data than competing models. In an example it also
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan
2008-01-01
uncertainty estimation (GLUE) procedure based on Markov chain Monte Carlo sampling is applied in order to improve the performance of the methodology in estimating parameters and posterior output distributions. The description of the spatial variations of the hydrological processes is accounted for by defining......In recent years, there has been an increase in the application of distributed, physically-based and integrated hydrological models. Many questions regarding how to properly calibrate and validate distributed models and assess the uncertainty of the estimated parameters and the spatially......-site validation must complement the usual time validation. In this study, we develop, through an application, a comprehensive framework for multi-criteria calibration and uncertainty assessment of distributed physically-based, integrated hydrological models. A revised version of the generalized likelihood...
Zou, Xiao-Duan; Li, Jian-Yang; Clark, Beth Ellen; Golish, Dathon
2018-01-01
The OSIRIS-REx spacecraft, launched in September, 2016, will study the asteroid Bennu and return a sample from its surface to Earth in 2023. Bennu is a near-Earth carbonaceous asteroid which will provide insight into the formation and evolution of the solar system. OSIRIS-REx will first approach Bennu in August 2018 and will study the asteroid for approximately two years before sampling. OSIRIS-REx will develop its photometric model (including Lommel-Seelinger, ROLO, McEwen, Minnaert and Akimov) of Bennu with OCAM and OVIRS during the Detailed Survey mission phase. The model developed during this phase will be used to photometrically correct the OCAM and OVIRS data.Here we present the analysis of the error for the photometric corrections. Based on our testing data sets, we find:1. The model uncertainties is only correct when we use the covariance matrix to calculate, because the parameters are highly correlated.2. No evidence of domination of any parameter in each model.3. And both model error and the data error contribute to the final correction error comparably.4. We tested the uncertainty module on fake and real data sets, and find that model performance depends on the data coverage and data quality. These tests gave us a better understanding of how different model behave in different case.5. L-S model is more reliable than others. Maybe because the simulated data are based on L-S model. However, the test on real data (SPDIF) does show slight advantage of L-S, too. ROLO is not reliable to use when calculating bond albedo. The uncertainty of McEwen model is big in most cases. Akimov performs unphysical on SOPIE 1 data.6. Better use L-S as our default choice, this conclusion is based mainly on our test on SOPIE data and IPDIF.
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisová, Katarina
To the best of our knowledge, this is the first paper which discusses likelihood inference or a random set using a germ-grain model, where the individual grains are unobservable edge effects occur, and other complications appear. We consider the case where the grains form a disc process modelled...... is specified with respect to a given marked Poisson model (i.e. a Boolean model). We show how edge effects and other complications can be handled by considering a certain conditional likelihood. Our methodology is illustrated by analyzing Peter Diggle's heather dataset, where we discuss the results...... of simulation-based maximum likelihood inference and the effect of specifying different reference Poisson models....
Maximum likelihood estimation for integrated diffusion processes
DEFF Research Database (Denmark)
Baltazar-Larios, Fernando; Sørensen, Michael
We propose a method for obtaining maximum likelihood estimates of parameters in diffusion models when the data is a discrete time sample of the integral of the process, while no direct observations of the process itself are available. The data are, moreover, assumed to be contaminated...... EM-algorithm to obtain maximum likelihood estimates of the parameters in the diffusion model. As part of the algorithm, we use a recent simple method for approximate simulation of diffusion bridges. In simulation studies for the Ornstein-Uhlenbeck process and the CIR process the proposed method works...... by measurement errors. Integrated volatility is an example of this type of observations. Another example is ice-core data on oxygen isotopes used to investigate paleo-temperatures. The data can be viewed as incomplete observations of a model with a tractable likelihood function. Therefore we propose a simulated...
Additivity of entropic uncertainty relations
Directory of Open Access Journals (Sweden)
René Schwonnek
2018-03-01
Full Text Available We consider the uncertainty between two pairs of local projective measurements performed on a multipartite system. We show that the optimal bound in any linear uncertainty relation, formulated in terms of the Shannon entropy, is additive. This directly implies, against naive intuition, that the minimal entropic uncertainty can always be realized by fully separable states. Hence, in contradiction to proposals by other authors, no entanglement witness can be constructed solely by comparing the attainable uncertainties of entangled and separable states. However, our result gives rise to a huge simplification for computing global uncertainty bounds as they now can be deduced from local ones. Furthermore, we provide the natural generalization of the Maassen and Uffink inequality for linear uncertainty relations with arbitrary positive coefficients.
Likelihood-Based Inference in Nonlinear Error-Correction Models
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbæk, Anders
We consider a class of vector nonlinear error correction models where the transfer function (or loadings) of the stationary relation- ships is nonlinear. This includes in particular the smooth transition models. A general representation theorem is given which establishes the dynamic properties...... and a linear trend in general. Gaussian likelihood-based estimators are considered for the long- run cointegration parameters, and the short-run parameters. Asymp- totic theory is provided for these and it is discussed to what extend asymptotic normality and mixed normaity can be found. A simulation study...
Maintaining symmetry of simulated likelihood functions
DEFF Research Database (Denmark)
Andersen, Laura Mørch
This paper suggests solutions to two different types of simulation errors related to Quasi-Monte Carlo integration. Likelihood functions which depend on standard deviations of mixed parameters are symmetric in nature. This paper shows that antithetic draws preserve this symmetry and thereby...... improves precision substantially. Another source of error is that models testing away mixing dimensions must replicate the relevant dimensions of the quasi-random draws in the simulation of the restricted likelihood. These simulation errors are ignored in the standard estimation procedures used today...
Likelihood inference for unions of interacting discs
DEFF Research Database (Denmark)
Møller, Jesper; Helisova, K.
2010-01-01
This is probably the first paper which discusses likelihood inference for a random set using a germ-grain model, where the individual grains are unobservable, edge effects occur and other complications appear. We consider the case where the grains form a disc process modelled by a marked point...... process, where the germs are the centres and the marks are the associated radii of the discs. We propose to use a recent parametric class of interacting disc process models, where the minimal sufficient statistic depends on various geometric properties of the random set, and the density is specified......-based maximum likelihood inference and the effect of specifying different reference Poisson models....
Evaluating the uncertainty of input quantities in measurement models
Possolo, Antonio; Elster, Clemens
2014-06-01
The Guide to the Expression of Uncertainty in Measurement (GUM) gives guidance about how values and uncertainties should be assigned to the input quantities that appear in measurement models. This contribution offers a concrete proposal for how that guidance may be updated in light of the advances in the evaluation and expression of measurement uncertainty that were made in the course of the twenty years that have elapsed since the publication of the GUM, and also considering situations that the GUM does not yet contemplate. Our motivation is the ongoing conversation about a new edition of the GUM. While generally we favour a Bayesian approach to uncertainty evaluation, we also recognize the value that other approaches may bring to the problems considered here, and focus on methods for uncertainty evaluation and propagation that are widely applicable, including to cases that the GUM has not yet addressed. In addition to Bayesian methods, we discuss maximum-likelihood estimation, robust statistical methods, and measurement models where values of nominal properties play the same role that input quantities play in traditional models. We illustrate these general-purpose techniques in concrete examples, employing data sets that are realistic but that also are of conveniently small sizes. The supplementary material available online lists the R computer code that we have used to produce these examples (stacks.iop.org/Met/51/3/339/mmedia). Although we strive to stay close to clause 4 of the GUM, which addresses the evaluation of uncertainty for input quantities, we depart from it as we review the classes of measurement models that we believe are generally useful in contemporary measurement science. We also considerably expand and update the treatment that the GUM gives to Type B evaluations of uncertainty: reviewing the state-of-the-art, disciplined approach to the elicitation of expert knowledge, and its encapsulation in probability distributions that are usable in
Secondary Analysis under Cohort Sampling Designs Using Conditional Likelihood
Directory of Open Access Journals (Sweden)
Olli Saarela
2012-01-01
Full Text Available Under cohort sampling designs, additional covariate data are collected on cases of a specific type and a randomly selected subset of noncases, primarily for the purpose of studying associations with a time-to-event response of interest. With such data available, an interest may arise to reuse them for studying associations between the additional covariate data and a secondary non-time-to-event response variable, usually collected for the whole study cohort at the outset of the study. Following earlier literature, we refer to such a situation as secondary analysis. We outline a general conditional likelihood approach for secondary analysis under cohort sampling designs and discuss the specific situations of case-cohort and nested case-control designs. We also review alternative methods based on full likelihood and inverse probability weighting. We compare the alternative methods for secondary analysis in two simulated settings and apply them in a real-data example.
Likelihood inference for a nonstationary fractional autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Ørregård Nielsen, Morten
2010-01-01
This paper discusses model-based inference in an autoregressive model for fractional processes which allows the process to be fractional of order d or d-b. Fractional differencing involves infinitely many past values and because we are interested in nonstationary processes we model the data X1......,...,X_{T} given the initial values X_{-n}, n=0,1,..., as is usually done. The initial values are not modeled but assumed to be bounded. This represents a considerable generalization relative to all previous work where it is assumed that initial values are zero. For the statistical analysis we assume...... the conditional Gaussian likelihood and for the probability analysis we also condition on initial values but assume that the errors in the autoregressive model are i.i.d. with suitable moment conditions. We analyze the conditional likelihood and its derivatives as stochastic processes in the parameters, including...
Uncertainty analysis of hydrological modeling in a tropical area using different algorithms
Rafiei Emam, Ammar; Kappas, Martin; Fassnacht, Steven; Linh, Nguyen Hoang Khanh
2018-01-01
Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and Rfactor, coefficient of determination (R 2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor 0.91, NSE>0.89, and 0.18
Simulation-based marginal likelihood for cluster strong lensing cosmology
Killedar, M.; Borgani, S.; Fabjan, D.; Dolag, K.; Granato, G.; Meneghetti, M.; Planelles, S.; Ragone-Figueroa, C.
2018-01-01
Comparisons between observed and predicted strong lensing properties of galaxy clusters have been routinely used to claim either tension or consistency with Λ cold dark matter cosmology. However, standard approaches to such cosmological tests are unable to quantify the preference for one cosmology over another. We advocate approximating the relevant Bayes factor using a marginal likelihood that is based on the following summary statistic: the posterior probability distribution function for the parameters of the scaling relation between Einstein radii and cluster mass, α and β. We demonstrate, for the first time, a method of estimating the marginal likelihood using the X-ray selected z > 0.5 Massive Cluster Survey clusters as a case in point and employing both N-body and hydrodynamic simulations of clusters. We investigate the uncertainty in this estimate and consequential ability to compare competing cosmologies, which arises from incomplete descriptions of baryonic processes, discrepancies in cluster selection criteria, redshift distribution and dynamical state. The relation between triaxial cluster masses at various overdensities provides a promising alternative to the strong lensing test.
International Nuclear Information System (INIS)
Thomas, R.E.
1982-03-01
An evaluation is made of the suitability of analytical and statistical sampling methods for making uncertainty analyses. The adjoint method is found to be well-suited for obtaining sensitivity coefficients for computer programs involving large numbers of equations and input parameters. For this purpose the Latin Hypercube Sampling method is found to be inferior to conventional experimental designs. The Latin hypercube method can be used to estimate output probability density functions, but requires supplementary rank transformations followed by stepwise regression to obtain uncertainty information on individual input parameters. A simple Cork and Bottle problem is used to illustrate the efficiency of the adjoint method relative to certain statistical sampling methods. For linear models of the form Ax=b it is shown that a complete adjoint sensitivity analysis can be made without formulating and solving the adjoint problem. This can be done either by using a special type of statistical sampling or by reformulating the primal problem and using suitable linear programming software
Composite likelihood estimation of demographic parameters
Directory of Open Access Journals (Sweden)
Garrigan Daniel
2009-11-01
Full Text Available Abstract Background Most existing likelihood-based methods for fitting historical demographic models to DNA sequence polymorphism data to do not scale feasibly up to the level of whole-genome data sets. Computational economies can be achieved by incorporating two forms of pseudo-likelihood: composite and approximate likelihood methods. Composite likelihood enables scaling up to large data sets because it takes the product of marginal likelihoods as an estimator of the likelihood of the complete data set. This approach is especially useful when a large number of genomic regions constitutes the data set. Additionally, approximate likelihood methods can reduce the dimensionality of the data by summarizing the information in the original data by either a sufficient statistic, or a set of statistics. Both composite and approximate likelihood methods hold promise for analyzing large data sets or for use in situations where the underlying demographic model is complex and has many parameters. This paper considers a simple demographic model of allopatric divergence between two populations, in which one of the population is hypothesized to have experienced a founder event, or population bottleneck. A large resequencing data set from human populations is summarized by the joint frequency spectrum, which is a matrix of the genomic frequency spectrum of derived base frequencies in two populations. A Bayesian Metropolis-coupled Markov chain Monte Carlo (MCMCMC method for parameter estimation is developed that uses both composite and likelihood methods and is applied to the three different pairwise combinations of the human population resequence data. The accuracy of the method is also tested on data sets sampled from a simulated population model with known parameters. Results The Bayesian MCMCMC method also estimates the ratio of effective population size for the X chromosome versus that of the autosomes. The method is shown to estimate, with reasonable
Devendran, A. A.; Lakshmanan, G.
2014-11-01
Data quality for GIS processing and analysis is becoming an increased concern due to the accelerated application of GIS technology for problem solving and decision making roles. Uncertainty in the geographic representation of the real world arises as these representations are incomplete. Identification of the sources of these uncertainties and the ways in which they operate in GIS based representations become crucial in any spatial data representation and geospatial analysis applied to any field of application. This paper reviews the articles on the various components of spatial data quality and various uncertainties inherent in them and special focus is paid to two fields of application such as Urban Simulation and Hydrological Modelling. Urban growth is a complicated process involving the spatio-temporal changes of all socio-economic and physical components at different scales. Cellular Automata (CA) model is one of the simulation models, which randomly selects potential cells for urbanisation and the transition rules evaluate the properties of the cell and its neighbour. Uncertainty arising from CA modelling is assessed mainly using sensitivity analysis including Monte Carlo simulation method. Likewise, the importance of hydrological uncertainty analysis has been emphasized in recent years and there is an urgent need to incorporate uncertainty estimation into water resources assessment procedures. The Soil and Water Assessment Tool (SWAT) is a continuous time watershed model to evaluate various impacts of land use management and climate on hydrology and water quality. Hydrological model uncertainties using SWAT model are dealt primarily by Generalized Likelihood Uncertainty Estimation (GLUE) method.
Efficient Bit-to-Symbol Likelihood Mappings
Moision, Bruce E.; Nakashima, Michael A.
2010-01-01
This innovation is an efficient algorithm designed to perform bit-to-symbol and symbol-to-bit likelihood mappings that represent a significant portion of the complexity of an error-correction code decoder for high-order constellations. Recent implementation of the algorithm in hardware has yielded an 8- percent reduction in overall area relative to the prior design.
Likelihood-ratio-based biometric verification
Bazen, A.M.; Veldhuis, Raymond N.J.
2002-01-01
This paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that for single-user verification the likelihood ratio is optimal.
Likelihood Ratio-Based Biometric Verification
Bazen, A.M.; Veldhuis, Raymond N.J.
The paper presents results on optimal similarity measures for biometric verification based on fixed-length feature vectors. First, we show that the verification of a single user is equivalent to the detection problem, which implies that, for single-user verification, the likelihood ratio is optimal.
Statistical modelling of survival data with random effects h-likelihood approach
Ha, Il Do; Lee, Youngjo
2017-01-01
This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R (“frailtyHL”), while the real-world data examples together with an R package, “frailtyHL” in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to research...
Effect of Baseflow Separation on Uncertainty of Hydrological Modeling in the Xinanjiang Model
Directory of Open Access Journals (Sweden)
Kairong Lin
2014-01-01
Full Text Available Based on the idea of inputting more available useful information for evaluation to gain less uncertainty, this study focuses on how well the uncertainty can be reduced by considering the baseflow estimation information obtained from the smoothed minima method (SMM. The Xinanjiang model and the generalized likelihood uncertainty estimation (GLUE method with the shuffled complex evolution Metropolis (SCEM-UA sampling algorithm were used for hydrological modeling and uncertainty analysis, respectively. The Jiangkou basin, located in the upper of the Hanjiang River, was selected as case study. It was found that the number and standard deviation of behavioral parameter sets both decreased when the threshold value for the baseflow efficiency index increased, and the high Nash-Sutcliffe efficiency coefficients correspond well with the high baseflow efficiency coefficients. The results also showed that uncertainty interval width decreased significantly, while containing ratio did not decrease by much and the simulated runoff with the behavioral parameter sets can fit better to the observed runoff, when threshold for the baseflow efficiency index was taken into consideration. These implied that using the baseflow estimation information can reduce the uncertainty in hydrological modeling to some degree and gain more reasonable prediction bounds.
Lopiano, Kenneth K; Young, Linda J; Gotway, Carol A
2014-09-01
Spatially referenced datasets arising from multiple sources are routinely combined to assess relationships among various outcomes and covariates. The geographical units associated with the data, such as the geographical coordinates or areal-level administrative units, are often spatially misaligned, that is, observed at different locations or aggregated over different geographical units. As a result, the covariate is often predicted at the locations where the response is observed. The method used to align disparate datasets must be accounted for when subsequently modeling the aligned data. Here we consider the case where kriging is used to align datasets in point-to-point and point-to-areal misalignment problems when the response variable is non-normally distributed. If the relationship is modeled using generalized linear models, the additional uncertainty induced from using the kriging mean as a covariate introduces a Berkson error structure. In this article, we develop a pseudo-penalized quasi-likelihood algorithm to account for the additional uncertainty when estimating regression parameters and associated measures of uncertainty. The method is applied to a point-to-point example assessing the relationship between low-birth weights and PM2.5 levels after the onset of the largest wildfire in Florida history, the Bugaboo scrub fire. A point-to-areal misalignment problem is presented where the relationship between asthma events in Florida's counties and PM2.5 levels after the onset of the fire is assessed. Finally, the method is evaluated using a simulation study. Our results indicate the method performs well in terms of coverage for 95% confidence intervals and naive methods that ignore the additional uncertainty tend to underestimate the variability associated with parameter estimates. The underestimation is most profound in Poisson regression models. © 2014, The International Biometric Society.
Quantifying the uncertainty in heritability.
Furlotte, Nicholas A; Heckerman, David; Lippert, Christoph
2014-05-01
The use of mixed models to determine narrow-sense heritability and related quantities such as SNP heritability has received much recent attention. Less attention has been paid to the inherent variability in these estimates. One approach for quantifying variability in estimates of heritability is a frequentist approach, in which heritability is estimated using maximum likelihood and its variance is quantified through an asymptotic normal approximation. An alternative approach is to quantify the uncertainty in heritability through its Bayesian posterior distribution. In this paper, we develop the latter approach, make it computationally efficient and compare it to the frequentist approach. We show theoretically that, for a sufficiently large sample size and intermediate values of heritability, the two approaches provide similar results. Using the Atherosclerosis Risk in Communities cohort, we show empirically that the two approaches can give different results and that the variance/uncertainty can remain large.
Empirical Likelihood in Nonignorable Covariate-Missing Data Problems.
Xie, Yanmei; Zhang, Biao
2017-04-20
Missing covariate data occurs often in regression analysis, which frequently arises in the health and social sciences as well as in survey sampling. We study methods for the analysis of a nonignorable covariate-missing data problem in an assumed conditional mean function when some covariates are completely observed but other covariates are missing for some subjects. We adopt the semiparametric perspective of Bartlett et al. (Improving upon the efficiency of complete case analysis when covariates are MNAR. Biostatistics 2014;15:719-30) on regression analyses with nonignorable missing covariates, in which they have introduced the use of two working models, the working probability model of missingness and the working conditional score model. In this paper, we study an empirical likelihood approach to nonignorable covariate-missing data problems with the objective of effectively utilizing the two working models in the analysis of covariate-missing data. We propose a unified approach to constructing a system of unbiased estimating equations, where there are more equations than unknown parameters of interest. One useful feature of these unbiased estimating equations is that they naturally incorporate the incomplete data into the data analysis, making it possible to seek efficient estimation of the parameter of interest even when the working regression function is not specified to be the optimal regression function. We apply the general methodology of empirical likelihood to optimally combine these unbiased estimating equations. We propose three maximum empirical likelihood estimators of the underlying regression parameters and compare their efficiencies with other existing competitors. We present a simulation study to compare the finite-sample performance of various methods with respect to bias, efficiency, and robustness to model misspecification. The proposed empirical likelihood method is also illustrated by an analysis of a data set from the US National Health and
Uncertainty in prediction and in inference
Hilgevoord, J.; Uffink, J.
1991-01-01
The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close re-lationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in
Directory of Open Access Journals (Sweden)
K. J. Franz
2011-11-01
Full Text Available The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE, and the Shuffle Complex Evolution Metropolis (SCEM. Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
Factors Associated with Young Adults’ Pregnancy Likelihood
Kitsantas, Panagiota; Lindley, Lisa L.; Wu, Huichuan
2014-01-01
OBJECTIVES While progress has been made to reduce adolescent pregnancies in the United States, rates of unplanned pregnancy among young adults (18–29 years) remain high. In this study, we assessed factors associated with perceived likelihood of pregnancy (likelihood of getting pregnant/getting partner pregnant in the next year) among sexually experienced young adults who were not trying to get pregnant and had ever used contraceptives. METHODS We conducted a secondary analysis of 660 young adults, 18–29 years old in the United States, from the cross-sectional National Survey of Reproductive and Contraceptive Knowledge. Logistic regression and classification tree analyses were conducted to generate profiles of young adults most likely to report anticipating a pregnancy in the next year. RESULTS Nearly one-third (32%) of young adults indicated they believed they had at least some likelihood of becoming pregnant in the next year. Young adults who believed that avoiding pregnancy was not very important were most likely to report pregnancy likelihood (odds ratio [OR], 5.21; 95% CI, 2.80–9.69), as were young adults for whom avoiding a pregnancy was important but not satisfied with their current contraceptive method (OR, 3.93; 95% CI, 1.67–9.24), attended religious services frequently (OR, 3.0; 95% CI, 1.52–5.94), were uninsured (OR, 2.63; 95% CI, 1.31–5.26), and were likely to have unprotected sex in the next three months (OR, 1.77; 95% CI, 1.04–3.01). DISCUSSION These results may help guide future research and the development of pregnancy prevention interventions targeting sexually experienced young adults. PMID:25782849
Unbinned likelihood analysis of EGRET observations
International Nuclear Information System (INIS)
Digel, Seth W.
2000-01-01
We present a newly-developed likelihood analysis method for EGRET data that defines the likelihood function without binning the photon data or averaging the instrumental response functions. The standard likelihood analysis applied to EGRET data requires the photons to be binned spatially and in energy, and the point-spread functions to be averaged over energy and inclination angle. The full-width half maximum of the point-spread function increases by about 40% from on-axis to 30 degree sign inclination, and depending on the binning in energy can vary by more than that in a single energy bin. The new unbinned method avoids the loss of information that binning and averaging cause and can properly analyze regions where EGRET viewing periods overlap and photons with different inclination angles would otherwise be combined in the same bin. In the poster, we describe the unbinned analysis method and compare its sensitivity with binned analysis for detecting point sources in EGRET data
Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution
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Hare Krishna
2017-01-01
Full Text Available In this article, we study the geometric distribution under randomly censored data. Maximum likelihood estimators and confidence intervals based on Fisher information matrix are derived for the unknown parameters with randomly censored data. Bayes estimators are also developed using beta priors under generalized entropy and LINEX loss functions. Also, Bayesian credible and highest posterior density (HPD credible intervals are obtained for the parameters. Expected time on test and reliability characteristics are also analyzed in this article. To compare various estimates developed in the article, a Monte Carlo simulation study is carried out. Finally, for illustration purpose, a randomly censored real data set is discussed.
Uncertainty Analyses and Strategy
International Nuclear Information System (INIS)
Kevin Coppersmith
2001-01-01
The DOE identified a variety of uncertainties, arising from different sources, during its assessment of the performance of a potential geologic repository at the Yucca Mountain site. In general, the number and detail of process models developed for the Yucca Mountain site, and the complex coupling among those models, make the direct incorporation of all uncertainties difficult. The DOE has addressed these issues in a number of ways using an approach to uncertainties that is focused on producing a defensible evaluation of the performance of a potential repository. The treatment of uncertainties oriented toward defensible assessments has led to analyses and models with so-called ''conservative'' assumptions and parameter bounds, where conservative implies lower performance than might be demonstrated with a more realistic representation. The varying maturity of the analyses and models, and uneven level of data availability, result in total system level analyses with a mix of realistic and conservative estimates (for both probabilistic representations and single values). That is, some inputs have realistically represented uncertainties, and others are conservatively estimated or bounded. However, this approach is consistent with the ''reasonable assurance'' approach to compliance demonstration, which was called for in the U.S. Nuclear Regulatory Commission's (NRC) proposed 10 CFR Part 63 regulation (64 FR 8640 [DIRS 101680]). A risk analysis that includes conservatism in the inputs will result in conservative risk estimates. Therefore, the approach taken for the Total System Performance Assessment for the Site Recommendation (TSPA-SR) provides a reasonable representation of processes and conservatism for purposes of site recommendation. However, mixing unknown degrees of conservatism in models and parameter representations reduces the transparency of the analysis and makes the development of coherent and consistent probability statements about projected repository
Directory of Open Access Journals (Sweden)
Daniel Furtado Ferreira
2004-05-01
Full Text Available Studies of genetic control in plants are carried out to characterize genetic effects and detect the existence of a major gene and/or genes of minor effects (polygenes. If the trait of interest is continuous, the likelihood can be constructed based on a model with mixtures of normal densities. Once exact tests are not evident with such models, the likelihood ratio test is generally used, using the chi-square approximation. This work aimed at evaluating such test statistic using computer simulation. Data sets were simulated using generations typical in plant studies, under two conditions of null hypothesis, without a major gene, and without polygenes. The power of the test was evaluated with both types of genes present. Different sample sizes and values of heritability were considered. Results showed that, although the empirical densities of the test statistic departed significantly from a chi-square distribution, under null hypotheses, there was a reasonable control of type I error, with a significance level of 5%. The power of the test was generally high to detect polygenes and major genes. Power is low to detect a major gene only when it explains a low fraction of genetic variation.Estudos de herança genética em plantas são realizados para caracterizar os efeitos genéticos e verificar a existência de um gene de efeito maior e/ou de genes de pequeno efeito (“poligenes”. Quando a característica de interesse é contínua, a verossimilhança é baseada em modelos de misturas de densidades normais. Uma vez que não há testes exatos evidentes para julgar a existência de um gene de efeito maior, a razão de verossimilhança generalizada é em geral utilizada, considerando a aproximação de qui-quadrado. Este trabalho objetivou avaliar esta estatística de teste através de simulação em computador. Dados foram simulados, considerando particularidades de genealogia típicas de tais estudos, e duas condições sob a hipótese de nulidade
Calibration of two complex ecosystem models with different likelihood functions
Hidy, Dóra; Haszpra, László; Pintér, Krisztina; Nagy, Zoltán; Barcza, Zoltán
2014-05-01
The biosphere is a sensitive carbon reservoir. Terrestrial ecosystems were approximately carbon neutral during the past centuries, but they became net carbon sinks due to climate change induced environmental change and associated CO2 fertilization effect of the atmosphere. Model studies and measurements indicate that the biospheric carbon sink can saturate in the future due to ongoing climate change which can act as a positive feedback. Robustness of carbon cycle models is a key issue when trying to choose the appropriate model for decision support. The input parameters of the process-based models are decisive regarding the model output. At the same time there are several input parameters for which accurate values are hard to obtain directly from experiments or no local measurements are available. Due to the uncertainty associated with the unknown model parameters significant bias can be experienced if the model is used to simulate the carbon and nitrogen cycle components of different ecosystems. In order to improve model performance the unknown model parameters has to be estimated. We developed a multi-objective, two-step calibration method based on Bayesian approach in order to estimate the unknown parameters of PaSim and Biome-BGC models. Biome-BGC and PaSim are a widely used biogeochemical models that simulate the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere, and within the components of the terrestrial ecosystems (in this research the developed version of Biome-BGC is used which is referred as BBGC MuSo). Both models were calibrated regardless the simulated processes and type of model parameters. The calibration procedure is based on the comparison of measured data with simulated results via calculating a likelihood function (degree of goodness-of-fit between simulated and measured data). In our research different likelihood function formulations were used in order to examine the effect of the different model
How to live with uncertainties?
International Nuclear Information System (INIS)
Michel, R.
2012-01-01
In a short introduction, the problem of uncertainty as a general consequence of incomplete information as well as the approach to quantify uncertainty in metrology are addressed. A little history of the more than 30 years of the working group AK SIGMA is followed by an appraisal of its up-to-now achievements. Then, the potential future of the AK SIGMA is discussed based on its actual tasks and on open scientific questions and future topics. (orig.)
Lei, Ning; Chiang, Kwo-Fu; Oudrari, Hassan; Xiong, Xiaoxiong
2011-01-01
Optical sensors aboard Earth orbiting satellites such as the next generation Visible/Infrared Imager/Radiometer Suite (VIIRS) assume that the sensors radiometric response in the Reflective Solar Bands (RSB) is described by a quadratic polynomial, in relating the aperture spectral radiance to the sensor Digital Number (DN) readout. For VIIRS Flight Unit 1, the coefficients are to be determined before launch by an attenuation method, although the linear coefficient will be further determined on-orbit through observing the Solar Diffuser. In determining the quadratic polynomial coefficients by the attenuation method, a Maximum Likelihood approach is applied in carrying out the least-squares procedure. Crucial to the Maximum Likelihood least-squares procedure is the computation of the weight. The weight not only has a contribution from the noise of the sensor s digital count, with an important contribution from digitization error, but also is affected heavily by the mathematical expression used to predict the value of the dependent variable, because both the independent and the dependent variables contain random noise. In addition, model errors have a major impact on the uncertainties of the coefficients. The Maximum Likelihood approach demonstrates the inadequacy of the attenuation method model with a quadratic polynomial for the retrieved spectral radiance. We show that using the inadequate model dramatically increases the uncertainties of the coefficients. We compute the coefficient values and their uncertainties, considering both measurement and model errors.
DEFF Research Database (Denmark)
Christensen, Ole Fredslund; Frydenberg, Morten; Jensen, Jens Ledet
2005-01-01
The large deviation modified likelihood ratio statistic is studied for testing a variance component equal to a specified value. Formulas are presented in the general balanced case, whereas in the unbalanced case only the one-way random effects model is studied. Simulation studies are presented......, showing that the normal approximation to the large deviation modified likelihood ratio statistic gives confidence intervals for variance components with coverage probabilities very close to the nominal confidence coefficient....
Accelerated maximum likelihood parameter estimation for stochastic biochemical systems
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Daigle Bernie J
2012-05-01
Full Text Available Abstract Background A prerequisite for the mechanistic simulation of a biochemical system is detailed knowledge of its kinetic parameters. Despite recent experimental advances, the estimation of unknown parameter values from observed data is still a bottleneck for obtaining accurate simulation results. Many methods exist for parameter estimation in deterministic biochemical systems; methods for discrete stochastic systems are less well developed. Given the probabilistic nature of stochastic biochemical models, a natural approach is to choose parameter values that maximize the probability of the observed data with respect to the unknown parameters, a.k.a. the maximum likelihood parameter estimates (MLEs. MLE computation for all but the simplest models requires the simulation of many system trajectories that are consistent with experimental data. For models with unknown parameters, this presents a computational challenge, as the generation of consistent trajectories can be an extremely rare occurrence. Results We have developed Monte Carlo Expectation-Maximization with Modified Cross-Entropy Method (MCEM2: an accelerated method for calculating MLEs that combines advances in rare event simulation with a computationally efficient version of the Monte Carlo expectation-maximization (MCEM algorithm. Our method requires no prior knowledge regarding parameter values, and it automatically provides a multivariate parameter uncertainty estimate. We applied the method to five stochastic systems of increasing complexity, progressing from an analytically tractable pure-birth model to a computationally demanding model of yeast-polarization. Our results demonstrate that MCEM2 substantially accelerates MLE computation on all tested models when compared to a stand-alone version of MCEM. Additionally, we show how our method identifies parameter values for certain classes of models more accurately than two recently proposed computationally efficient methods
CONSTRUCTING A FLEXIBLE LIKELIHOOD FUNCTION FOR SPECTROSCOPIC INFERENCE
International Nuclear Information System (INIS)
Czekala, Ian; Andrews, Sean M.; Mandel, Kaisey S.; Green, Gregory M.; Hogg, David W.
2015-01-01
We present a modular, extensible likelihood framework for spectroscopic inference based on synthetic model spectra. The subtraction of an imperfect model from a continuously sampled spectrum introduces covariance between adjacent datapoints (pixels) into the residual spectrum. For the high signal-to-noise data with large spectral range that is commonly employed in stellar astrophysics, that covariant structure can lead to dramatically underestimated parameter uncertainties (and, in some cases, biases). We construct a likelihood function that accounts for the structure of the covariance matrix, utilizing the machinery of Gaussian process kernels. This framework specifically addresses the common problem of mismatches in model spectral line strengths (with respect to data) due to intrinsic model imperfections (e.g., in the atomic/molecular databases or opacity prescriptions) by developing a novel local covariance kernel formalism that identifies and self-consistently downweights pathological spectral line “outliers.” By fitting many spectra in a hierarchical manner, these local kernels provide a mechanism to learn about and build data-driven corrections to synthetic spectral libraries. An open-source software implementation of this approach is available at http://iancze.github.io/Starfish, including a sophisticated probabilistic scheme for spectral interpolation when using model libraries that are sparsely sampled in the stellar parameters. We demonstrate some salient features of the framework by fitting the high-resolution V-band spectrum of WASP-14, an F5 dwarf with a transiting exoplanet, and the moderate-resolution K-band spectrum of Gliese 51, an M5 field dwarf
Superfast maximum-likelihood reconstruction for quantum tomography
Shang, Jiangwei; Zhang, Zhengyun; Ng, Hui Khoon
2017-06-01
Conventional methods for computing maximum-likelihood estimators (MLE) often converge slowly in practical situations, leading to a search for simplifying methods that rely on additional assumptions for their validity. In this work, we provide a fast and reliable algorithm for maximum-likelihood reconstruction that avoids this slow convergence. Our method utilizes the state-of-the-art convex optimization scheme, an accelerated projected-gradient method, that allows one to accommodate the quantum nature of the problem in a different way than in the standard methods. We demonstrate the power of our approach by comparing its performance with other algorithms for n -qubit state tomography. In particular, an eight-qubit situation that purportedly took weeks of computation time in 2005 can now be completed in under a minute for a single set of data, with far higher accuracy than previously possible. This refutes the common claim that MLE reconstruction is slow and reduces the need for alternative methods that often come with difficult-to-verify assumptions. In fact, recent methods assuming Gaussian statistics or relying on compressed sensing ideas are demonstrably inapplicable for the situation under consideration here. Our algorithm can be applied to general optimization problems over the quantum state space; the philosophy of projected gradients can further be utilized for optimization contexts with general constraints.
Dimension-Independent Likelihood-Informed MCMC
Cui, Tiangang; Law, Kody; Marzouk, Youssef
2015-01-01
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters, which in principle can be described as functions. By exploiting low-dimensional structure in the change from prior to posterior [distributions], we introduce a suite of MCMC samplers that can adapt to the complex structure of the posterior distribution, yet are well-defined on function space. Posterior sampling in nonlinear inverse problems arising from various partial di erential equations and also a stochastic differential equation are used to demonstrate the e ciency of these dimension-independent likelihood-informed samplers.
Dimension-Independent Likelihood-Informed MCMC
Cui, Tiangang
2015-01-07
Many Bayesian inference problems require exploring the posterior distribution of high-dimensional parameters, which in principle can be described as functions. By exploiting low-dimensional structure in the change from prior to posterior [distributions], we introduce a suite of MCMC samplers that can adapt to the complex structure of the posterior distribution, yet are well-defined on function space. Posterior sampling in nonlinear inverse problems arising from various partial di erential equations and also a stochastic differential equation are used to demonstrate the e ciency of these dimension-independent likelihood-informed samplers.
Approximate maximum parsimony and ancestral maximum likelihood.
Alon, Noga; Chor, Benny; Pardi, Fabio; Rapoport, Anat
2010-01-01
We explore the maximum parsimony (MP) and ancestral maximum likelihood (AML) criteria in phylogenetic tree reconstruction. Both problems are NP-hard, so we seek approximate solutions. We formulate the two problems as Steiner tree problems under appropriate distances. The gist of our approach is the succinct characterization of Steiner trees for a small number of leaves for the two distances. This enables the use of known Steiner tree approximation algorithms. The approach leads to a 16/9 approximation ratio for AML and asymptotically to a 1.55 approximation ratio for MP.
Pan, Zhen; Anderes, Ethan; Knox, Lloyd
2018-05-01
One of the major targets for next-generation cosmic microwave background (CMB) experiments is the detection of the primordial B-mode signal. Planning is under way for Stage-IV experiments that are projected to have instrumental noise small enough to make lensing and foregrounds the dominant source of uncertainty for estimating the tensor-to-scalar ratio r from polarization maps. This makes delensing a crucial part of future CMB polarization science. In this paper we present a likelihood method for estimating the tensor-to-scalar ratio r from CMB polarization observations, which combines the benefits of a full-scale likelihood approach with the tractability of the quadratic delensing technique. This method is a pixel space, all order likelihood analysis of the quadratic delensed B modes, and it essentially builds upon the quadratic delenser by taking into account all order lensing and pixel space anomalies. Its tractability relies on a crucial factorization of the pixel space covariance matrix of the polarization observations which allows one to compute the full Gaussian approximate likelihood profile, as a function of r , at the same computational cost of a single likelihood evaluation.
Transfer Entropy as a Log-Likelihood Ratio
Barnett, Lionel; Bossomaier, Terry
2012-09-01
Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.
Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets
Litvinenko, Alexander
2017-09-03
We use available measurements to estimate the unknown parameters (variance, smoothness parameter, and covariance length) of a covariance function by maximizing the joint Gaussian log-likelihood function. To overcome cubic complexity in the linear algebra, we approximate the discretized covariance function in the hierarchical (H-) matrix format. The H-matrix format has a log-linear computational cost and storage O(kn log n), where the rank k is a small integer and n is the number of locations. The H-matrix technique allows us to work with general covariance matrices in an efficient way, since H-matrices can approximate inhomogeneous covariance functions, with a fairly general mesh that is not necessarily axes-parallel, and neither the covariance matrix itself nor its inverse have to be sparse. We demonstrate our method with Monte Carlo simulations and an application to soil moisture data. The C, C++ codes and data are freely available.
Marginal Maximum Likelihood Estimation of Item Response Models in R
Directory of Open Access Journals (Sweden)
Matthew S. Johnson
2007-02-01
Full Text Available Item response theory (IRT models are a class of statistical models used by researchers to describe the response behaviors of individuals to a set of categorically scored items. The most common IRT models can be classified as generalized linear fixed- and/or mixed-effect models. Although IRT models appear most often in the psychological testing literature, researchers in other fields have successfully utilized IRT-like models in a wide variety of applications. This paper discusses the three major methods of estimation in IRT and develops R functions utilizing the built-in capabilities of the R environment to find the marginal maximum likelihood estimates of the generalized partial credit model. The currently available R packages ltm is also discussed.
International Nuclear Information System (INIS)
Landsberg, P.T.
1990-01-01
This paper explores how the quantum mechanics uncertainty relation can be considered to result from measurements. A distinction is drawn between the uncertainties obtained by scrutinising experiments and the standard deviation type of uncertainty definition used in quantum formalism. (UK)
Directory of Open Access Journals (Sweden)
N. Eckert
2008-10-01
Full Text Available For snow avalanches, passive defense structures are generally designed by considering high return period events. In this paper, taking inspiration from other natural hazards, an alternative method based on the maximization of the economic benefit of the defense structure is proposed. A general Bayesian framework is described first. Special attention is given to the problem of taking the poor local information into account in the decision-making process. Therefore, simplifying assumptions are made. The avalanche hazard is represented by a Peak Over Threshold (POT model. The influence of the dam is quantified in terms of runout distance reduction with a simple relation derived from small-scale experiments using granular media. The costs corresponding to dam construction and the damage to the element at risk are roughly evaluated for each dam height-hazard value pair, with damage evaluation corresponding to the maximal expected loss. Both the classical and the Bayesian risk functions can then be computed analytically. The results are illustrated with a case study from the French avalanche database. A sensitivity analysis is performed and modelling assumptions are discussed in addition to possible further developments.
Freni, Gabriele; Mannina, Giorgio
In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised Likelihood Uncertainty Estimation (GLUE). One crucial point in the application of Bayesian method is the formulation of a likelihood function that is conditioned by the hypotheses made regarding model residuals. Statistical transformations, such as the use of Box-Cox equation, are generally used to ensure the homoscedasticity of residuals. However, this practice may affect the reliability of the analysis leading to a wrong uncertainty estimation. The present paper aims to explore the influence of the Box-Cox equation for environmental water quality models. To this end, five cases were considered one of which was the “real” residuals distributions (i.e. drawn from available data). The analysis was applied to the Nocella experimental catchment (Italy) which is an agricultural and semi-urbanised basin where two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. The results show that the uncertainty estimation is greatly affected by residual transformation and a wrong assumption may also affect the evaluation of model uncertainty. The use of less formal methods always provide an overestimation of modelling uncertainty with respect to Bayesian method but such effect is reduced if a wrong assumption is made regarding the
Commonplaces and social uncertainty
DEFF Research Database (Denmark)
Lassen, Inger
2008-01-01
This article explores the concept of uncertainty in four focus group discussions about genetically modified food. In the discussions, members of the general public interact with food biotechnology scientists while negotiating their attitudes towards genetic engineering. Their discussions offer...... an example of risk discourse in which the use of commonplaces seems to be a central feature (Myers 2004: 81). My analyses support earlier findings that commonplaces serve important interactional purposes (Barton 1999) and that they are used for mitigating disagreement, for closing topics and for facilitating...
Uncertainty estimation of Intensity-Duration-Frequency relationships: A regional analysis
Mélèse, Victor; Blanchet, Juliette; Molinié, Gilles
2018-03-01
We propose in this article a regional study of uncertainties in IDF curves derived from point-rainfall maxima. We develop two generalized extreme value models based on the simple scaling assumption, first in the frequentist framework and second in the Bayesian framework. Within the frequentist framework, uncertainties are obtained i) from the Gaussian density stemming from the asymptotic normality theorem of the maximum likelihood and ii) with a bootstrap procedure. Within the Bayesian framework, uncertainties are obtained from the posterior densities. We confront these two frameworks on the same database covering a large region of 100, 000 km2 in southern France with contrasted rainfall regime, in order to be able to draw conclusion that are not specific to the data. The two frameworks are applied to 405 hourly stations with data back to the 1980's, accumulated in the range 3 h-120 h. We show that i) the Bayesian framework is more robust than the frequentist one to the starting point of the estimation procedure, ii) the posterior and the bootstrap densities are able to better adjust uncertainty estimation to the data than the Gaussian density, and iii) the bootstrap density give unreasonable confidence intervals, in particular for return levels associated to large return period. Therefore our recommendation goes towards the use of the Bayesian framework to compute uncertainty.
The Uncertainties of Risk Management
DEFF Research Database (Denmark)
Vinnari, Eija; Skærbæk, Peter
2014-01-01
for expanding risk management. More generally, such uncertainties relate to the professional identities and responsibilities of operational managers as defined by the framing devices. Originality/value – The paper offers three contributions to the extant literature: first, it shows how risk management itself......Purpose – The purpose of this paper is to analyse the implementation of risk management as a tool for internal audit activities, focusing on unexpected effects or uncertainties generated during its application. Design/methodology/approach – Public and confidential documents as well as semi......-structured interviews are analysed through the lens of actor-network theory to identify the effects of risk management devices in a Finnish municipality. Findings – The authors found that risk management, rather than reducing uncertainty, itself created unexpected uncertainties that would otherwise not have emerged...
Maximum likelihood pedigree reconstruction using integer linear programming.
Cussens, James; Bartlett, Mark; Jones, Elinor M; Sheehan, Nuala A
2013-01-01
Large population biobanks of unrelated individuals have been highly successful in detecting common genetic variants affecting diseases of public health concern. However, they lack the statistical power to detect more modest gene-gene and gene-environment interaction effects or the effects of rare variants for which related individuals are ideally required. In reality, most large population studies will undoubtedly contain sets of undeclared relatives, or pedigrees. Although a crude measure of relatedness might sometimes suffice, having a good estimate of the true pedigree would be much more informative if this could be obtained efficiently. Relatives are more likely to share longer haplotypes around disease susceptibility loci and are hence biologically more informative for rare variants than unrelated cases and controls. Distant relatives are arguably more useful for detecting variants with small effects because they are less likely to share masking environmental effects. Moreover, the identification of relatives enables appropriate adjustments of statistical analyses that typically assume unrelatedness. We propose to exploit an integer linear programming optimisation approach to pedigree learning, which is adapted to find valid pedigrees by imposing appropriate constraints. Our method is not restricted to small pedigrees and is guaranteed to return a maximum likelihood pedigree. With additional constraints, we can also search for multiple high-probability pedigrees and thus account for the inherent uncertainty in any particular pedigree reconstruction. The true pedigree is found very quickly by comparison with other methods when all individuals are observed. Extensions to more complex problems seem feasible. © 2012 Wiley Periodicals, Inc.
Uncertainties in model-based outcome predictions for treatment planning
International Nuclear Information System (INIS)
Deasy, Joseph O.; Chao, K.S. Clifford; Markman, Jerry
2001-01-01
Purpose: Model-based treatment-plan-specific outcome predictions (such as normal tissue complication probability [NTCP] or the relative reduction in salivary function) are typically presented without reference to underlying uncertainties. We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions. Methods and Materials: A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties. The general framework is applicable to continuous variable predictions (e.g., prediction of long-term salivary function) and dichotomous variable predictions (e.g., tumor control probability [TCP] or NTCP). Using bootstrap resampling, a histogram of the likelihood of alternative parameter values is generated. For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list. Residual uncertainty ('noise') is accounted for by adding a random component to the computed outcome values. The residual noise distribution is estimated from the original fit between model predictions and patient data. Results: The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head-and-neck cancer patients. Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data, the salivary function model, and the three-dimensional dose distribution. For some patients there is significant uncertainty in the prediction of xerostomia, whereas for other patients the predictions are expected to be more reliable. In contrast, TCP and NTCP endpoints are dichotomous, and parameter uncertainties should be folded directly into the estimated probabilities, thereby improving the accuracy of the estimates. Using bootstrap parameter estimates, competing treatment
Uncertainty Quantification Bayesian Framework for Porous Media Flows
Demyanov, V.; Christie, M.; Erbas, D.
2005-12-01
Uncertainty quantification is an increasingly important aspect of many areas of applied science, where the challenge is to make reliable predictions about the performance of complex physical systems in the absence of complete or reliable data. Predicting flows of fluids through undersurface reservoirs is an example of a complex system where accuracy in prediction is needed (e.g. in oil industry it is essential for financial reasons). Simulation of fluid flow in oil reservoirs is usually carried out using large commercially written finite difference simulators solving conservation equations describing the multi-phase flow through the porous reservoir rocks, which is a highly computationally expensive task. This work examines a Bayesian Framework for uncertainty quantification in porous media flows that uses a stochastic sampling algorithm to generate models that match observed time series data. The framework is flexible for a wide range of general physical/statistical parametric models, which are used to describe the underlying hydro-geological process in its temporal dynamics. The approach is based on exploration of the parameter space and update of the prior beliefs about what the most likely model definitions are. Optimization problem for a highly parametric physical model usually have multiple solutions, which impact the uncertainty of the made predictions. Stochastic search algorithm (e.g. genetic algorithm) allows to identify multiple "good enough" models in the parameter space. Furthermore, inference of the generated model ensemble via MCMC based algorithm evaluates the posterior probability of the generated models and quantifies uncertainty of the predictions. Machine learning algorithm - Artificial Neural Networks - are used to speed up the identification of regions in parameter space where good matches to observed data can be found. Adaptive nature of ANN allows to develop different ways of integrating them into the Bayesian framework: as direct time
Maximum Likelihood Reconstruction for Magnetic Resonance Fingerprinting.
Zhao, Bo; Setsompop, Kawin; Ye, Huihui; Cauley, Stephen F; Wald, Lawrence L
2016-08-01
This paper introduces a statistical estimation framework for magnetic resonance (MR) fingerprinting, a recently proposed quantitative imaging paradigm. Within this framework, we present a maximum likelihood (ML) formalism to estimate multiple MR tissue parameter maps directly from highly undersampled, noisy k-space data. A novel algorithm, based on variable splitting, the alternating direction method of multipliers, and the variable projection method, is developed to solve the resulting optimization problem. Representative results from both simulations and in vivo experiments demonstrate that the proposed approach yields significantly improved accuracy in parameter estimation, compared to the conventional MR fingerprinting reconstruction. Moreover, the proposed framework provides new theoretical insights into the conventional approach. We show analytically that the conventional approach is an approximation to the ML reconstruction; more precisely, it is exactly equivalent to the first iteration of the proposed algorithm for the ML reconstruction, provided that a gridding reconstruction is used as an initialization.
Subtracting and Fitting Histograms using Profile Likelihood
D'Almeida, F M L
2008-01-01
It is known that many interesting signals expected at LHC are of unknown shape and strongly contaminated by background events. These signals will be dif cult to detect during the rst years of LHC operation due to the initial low luminosity. In this work, one presents a method of subtracting histograms based on the pro le likelihood function when the background is previously estimated by Monte Carlo events and one has low statistics. Estimators for the signal in each bin of the histogram difference are calculated so as limits for the signals with 68.3% of Con dence Level in a low statistics case when one has a exponential background and a Gaussian signal. The method can also be used to t histograms when the signal shape is known. Our results show a good performance and avoid the problem of negative values when subtracting histograms.
Modelling maximum likelihood estimation of availability
International Nuclear Information System (INIS)
Waller, R.A.; Tietjen, G.L.; Rock, G.W.
1975-01-01
Suppose the performance of a nuclear powered electrical generating power plant is continuously monitored to record the sequence of failure and repairs during sustained operation. The purpose of this study is to assess one method of estimating the performance of the power plant when the measure of performance is availability. That is, we determine the probability that the plant is operational at time t. To study the availability of a power plant, we first assume statistical models for the variables, X and Y, which denote the time-to-failure and the time-to-repair variables, respectively. Once those statistical models are specified, the availability, A(t), can be expressed as a function of some or all of their parameters. Usually those parameters are unknown in practice and so A(t) is unknown. This paper discusses the maximum likelihood estimator of A(t) when the time-to-failure model for X is an exponential density with parameter, lambda, and the time-to-repair model for Y is an exponential density with parameter, theta. Under the assumption of exponential models for X and Y, it follows that the instantaneous availability at time t is A(t)=lambda/(lambda+theta)+theta/(lambda+theta)exp[-[(1/lambda)+(1/theta)]t] with t>0. Also, the steady-state availability is A(infinity)=lambda/(lambda+theta). We use the observations from n failure-repair cycles of the power plant, say X 1 , X 2 , ..., Xsub(n), Y 1 , Y 2 , ..., Ysub(n) to present the maximum likelihood estimators of A(t) and A(infinity). The exact sampling distributions for those estimators and some statistical properties are discussed before a simulation model is used to determine 95% simulation intervals for A(t). The methodology is applied to two examples which approximate the operating history of two nuclear power plants. (author)
Likelihood Approximation With Parallel Hierarchical Matrices For Large Spatial Datasets
Litvinenko, Alexander
2017-11-01
The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Matérn covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\\\H$-) matrix format with computational cost $\\\\mathcal{O}(k^2n \\\\log^2 n/p)$ and storage $\\\\mathcal{O}(kn \\\\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.
Likelihood Approximation With Parallel Hierarchical Matrices For Large Spatial Datasets
Litvinenko, Alexander; Sun, Ying; Genton, Marc G.; Keyes, David E.
2017-01-01
The main goal of this article is to introduce the parallel hierarchical matrix library HLIBpro to the statistical community. We describe the HLIBCov package, which is an extension of the HLIBpro library for approximating large covariance matrices and maximizing likelihood functions. We show that an approximate Cholesky factorization of a dense matrix of size $2M\\times 2M$ can be computed on a modern multi-core desktop in few minutes. Further, HLIBCov is used for estimating the unknown parameters such as the covariance length, variance and smoothness parameter of a Matérn covariance function by maximizing the joint Gaussian log-likelihood function. The computational bottleneck here is expensive linear algebra arithmetics due to large and dense covariance matrices. Therefore covariance matrices are approximated in the hierarchical ($\\H$-) matrix format with computational cost $\\mathcal{O}(k^2n \\log^2 n/p)$ and storage $\\mathcal{O}(kn \\log n)$, where the rank $k$ is a small integer (typically $k<25$), $p$ the number of cores and $n$ the number of locations on a fairly general mesh. We demonstrate a synthetic example, where the true values of known parameters are known. For reproducibility we provide the C++ code, the documentation, and the synthetic data.
Safe semi-supervised learning based on weighted likelihood.
Kawakita, Masanori; Takeuchi, Jun'ichi
2014-05-01
We are interested in developing a safe semi-supervised learning that works in any situation. Semi-supervised learning postulates that n(') unlabeled data are available in addition to n labeled data. However, almost all of the previous semi-supervised methods require additional assumptions (not only unlabeled data) to make improvements on supervised learning. If such assumptions are not met, then the methods possibly perform worse than supervised learning. Sokolovska, Cappé, and Yvon (2008) proposed a semi-supervised method based on a weighted likelihood approach. They proved that this method asymptotically never performs worse than supervised learning (i.e., it is safe) without any assumption. Their method is attractive because it is easy to implement and is potentially general. Moreover, it is deeply related to a certain statistical paradox. However, the method of Sokolovska et al. (2008) assumes a very limited situation, i.e., classification, discrete covariates, n(')→∞ and a maximum likelihood estimator. In this paper, we extend their method by modifying the weight. We prove that our proposal is safe in a significantly wide range of situations as long as n≤n('). Further, we give a geometrical interpretation of the proof of safety through the relationship with the above-mentioned statistical paradox. Finally, we show that the above proposal is asymptotically safe even when n(')
Jacquin, A. P.
2012-04-01
This study analyses the effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model's discharge estimates. Prediction uncertainty bounds are derived using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation (at a single station within the catchment) and a precipitation factor FPi. Thus, these factors provide a simplified representation of the spatial variation of precipitation, specifically the shape of the functional relationship between precipitation and height. In the absence of information about appropriate values of the precipitation factors FPi, these are estimated through standard calibration procedures. The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. Monte Carlo samples of the model output are obtained by randomly varying the model parameters within their feasible ranges. In the first experiment, the precipitation factors FPi are considered unknown and thus included in the sampling process. The total number of unknown parameters in this case is 16. In the second experiment, precipitation factors FPi are estimated a priori, by means of a long term water balance between observed discharge at the catchment outlet, evapotranspiration estimates and observed precipitation. In this case, the number of unknown parameters reduces to 11. The feasible ranges assigned to the precipitation factors in the first experiment are slightly wider than the range of fixed precipitation factors used in the second experiment. The mean squared error of the Box-Cox transformed discharge during the calibration period is used for the evaluation of the
The uncertainties in estimating measurement uncertainties
International Nuclear Information System (INIS)
Clark, J.P.; Shull, A.H.
1994-01-01
All measurements include some error. Whether measurements are used for accountability, environmental programs or process support, they are of little value unless accompanied by an estimate of the measurements uncertainty. This fact is often overlooked by the individuals who need measurements to make decisions. This paper will discuss the concepts of measurement, measurements errors (accuracy or bias and precision or random error), physical and error models, measurement control programs, examples of measurement uncertainty, and uncertainty as related to measurement quality. Measurements are comparisons of unknowns to knowns, estimates of some true value plus uncertainty; and are no better than the standards to which they are compared. Direct comparisons of unknowns that match the composition of known standards will normally have small uncertainties. In the real world, measurements usually involve indirect comparisons of significantly different materials (e.g., measuring a physical property of a chemical element in a sample having a matrix that is significantly different from calibration standards matrix). Consequently, there are many sources of error involved in measurement processes that can affect the quality of a measurement and its associated uncertainty. How the uncertainty estimates are determined and what they mean is as important as the measurement. The process of calculating the uncertainty of a measurement itself has uncertainties that must be handled correctly. Examples of chemistry laboratory measurement will be reviewed in this report and recommendations made for improving measurement uncertainties
International Nuclear Information System (INIS)
Peters, H.P.; Hennen, L.
1990-01-01
The authors report on the results of three representative surveys that made a closer inquiry into perceptions and valuations of information and information sources concering Chernobyl. If turns out that the information sources are generally considered little trustworthy. This was generally attributable to the interpretation of the events being tied to attitudes in the atmonic energy issue. The greatest credit was given to television broadcasting. The authors summarize their discourse as follows: There is good reason to interpret the widespread uncertainty after Chernobyl as proof of the fact that large parts of the population are prepared and willing to assume a critical stance towards information and prefer to draw their information from various sources representing different positions. (orig.) [de
Sun, Mei; Zhang, Xiaolin; Huo, Zailin; Feng, Shaoyuan; Huang, Guanhua; Mao, Xiaomin
2016-03-01
Quantitatively ascertaining and analyzing the effects of model uncertainty on model reliability is a focal point for agricultural-hydrological models due to more uncertainties of inputs and processes. In this study, the generalized likelihood uncertainty estimation (GLUE) method with Latin hypercube sampling (LHS) was used to evaluate the uncertainty of the RZWQM-DSSAT (RZWQM2) model outputs responses and the sensitivity of 25 parameters related to soil properties, nutrient transport and crop genetics. To avoid the one-sided risk of model prediction caused by using a single calibration criterion, the combined likelihood (CL) function integrated information concerning water, nitrogen, and crop production was introduced in GLUE analysis for the predictions of the following four model output responses: the total amount of water content (T-SWC) and the nitrate nitrogen (T-NIT) within the 1-m soil profile, the seed yields of waxy maize (Y-Maize) and winter wheat (Y-Wheat). In the process of evaluating RZWQM2, measurements and meteorological data were obtained from a field experiment that involved a winter wheat and waxy maize crop rotation system conducted from 2003 to 2004 in southern Beijing. The calibration and validation results indicated that RZWQM2 model can be used to simulate the crop growth and water-nitrogen migration and transformation in wheat-maize crop rotation planting system. The results of uncertainty analysis using of GLUE method showed T-NIT was sensitive to parameters relative to nitrification coefficient, maize growth characteristics on seedling period, wheat vernalization period, and wheat photoperiod. Parameters on soil saturated hydraulic conductivity, nitrogen nitrification and denitrification, and urea hydrolysis played an important role in crop yield component. The prediction errors for RZWQM2 outputs with CL function were relatively lower and uniform compared with other likelihood functions composed of individual calibration criterion. This
Uncertainty in social dilemmas
Kwaadsteniet, Erik Willem de
2007-01-01
This dissertation focuses on social dilemmas, and more specifically, on environmental uncertainty in these dilemmas. Real-life social dilemma situations are often characterized by uncertainty. For example, fishermen mostly do not know the exact size of the fish population (i.e., resource size uncertainty). Several researchers have therefore asked themselves the question as to how such uncertainty influences people’s choice behavior. These researchers have repeatedly concluded that uncertainty...
Maximum likelihood estimation of phase-type distributions
DEFF Research Database (Denmark)
Esparza, Luz Judith R
for both univariate and multivariate cases. Methods like the EM algorithm and Markov chain Monte Carlo are applied for this purpose. Furthermore, this thesis provides explicit formulae for computing the Fisher information matrix for discrete and continuous phase-type distributions, which is needed to find......This work is concerned with the statistical inference of phase-type distributions and the analysis of distributions with rational Laplace transform, known as matrix-exponential distributions. The thesis is focused on the estimation of the maximum likelihood parameters of phase-type distributions...... confidence regions for their estimated parameters. Finally, a new general class of distributions, called bilateral matrix-exponential distributions, is defined. These distributions have the entire real line as domain and can be used, for instance, for modelling. In addition, this class of distributions...
Likelihood analysis of the minimal AMSB model
Energy Technology Data Exchange (ETDEWEB)
Bagnaschi, E.; Weiglein, G. [DESY, Hamburg (Germany); Borsato, M.; Chobanova, V.; Lucio, M.; Santos, D.M. [Universidade de Santiago de Compostela, Santiago de Compostela (Spain); Sakurai, K. [Institute for Particle Physics Phenomenology, University of Durham, Science Laboratories, Department of Physics, Durham (United Kingdom); University of Warsaw, Faculty of Physics, Institute of Theoretical Physics, Warsaw (Poland); Buchmueller, O.; Citron, M.; Costa, J.C.; Richards, A. [Imperial College, High Energy Physics Group, Blackett Laboratory, London (United Kingdom); Cavanaugh, R. [Fermi National Accelerator Laboratory, Batavia, IL (United States); University of Illinois at Chicago, Physics Department, Chicago, IL (United States); De Roeck, A. [Experimental Physics Department, CERN, Geneva (Switzerland); Antwerp University, Wilrijk (Belgium); Dolan, M.J. [School of Physics, University of Melbourne, ARC Centre of Excellence for Particle Physics at the Terascale, Melbourne (Australia); Ellis, J.R. [King' s College London, Theoretical Particle Physics and Cosmology Group, Department of Physics, London (United Kingdom); CERN, Theoretical Physics Department, Geneva (Switzerland); Flaecher, H. [University of Bristol, H.H. Wills Physics Laboratory, Bristol (United Kingdom); Heinemeyer, S. [Campus of International Excellence UAM+CSIC, Madrid (Spain); Instituto de Fisica Teorica UAM-CSIC, Madrid (Spain); Instituto de Fisica de Cantabria (CSIC-UC), Cantabria (Spain); Isidori, G. [Physik-Institut, Universitaet Zuerich, Zurich (Switzerland); Luo, F. [Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba (Japan); Olive, K.A. [School of Physics and Astronomy, University of Minnesota, William I. Fine Theoretical Physics Institute, Minneapolis, MN (United States)
2017-04-15
We perform a likelihood analysis of the minimal anomaly-mediated supersymmetry-breaking (mAMSB) model using constraints from cosmology and accelerator experiments. We find that either a wino-like or a Higgsino-like neutralino LSP, χ{sup 0}{sub 1}, may provide the cold dark matter (DM), both with similar likelihoods. The upper limit on the DM density from Planck and other experiments enforces m{sub χ{sup 0}{sub 1}}
Mannina, Giorgio; Cosenza, Alida; Viviani, Gaspare
In the last few years, the use of mathematical models in WasteWater Treatment Plant (WWTP) processes has become a common way to predict WWTP behaviour. However, mathematical models generally demand advanced input for their implementation that must be evaluated by an extensive data-gathering campaign, which cannot always be carried out. This fact, together with the intrinsic complexity of the model structure, leads to model results that may be very uncertain. Quantification of the uncertainty is imperative. However, despite the importance of uncertainty quantification, only few studies have been carried out in the wastewater treatment field, and those studies only included a few of the sources of model uncertainty. Seeking the development of the area, the paper presents the uncertainty assessment of a mathematical model simulating biological nitrogen and phosphorus removal. The uncertainty assessment was conducted according to the Generalised Likelihood Uncertainty Estimation (GLUE) methodology that has been scarcely applied in wastewater field. The model was based on activated-sludge models 1 (ASM) and 2 (ASM2). Different approaches can be used for uncertainty analysis. The GLUE methodology requires a large number of Monte Carlo simulations in which a random sampling of individual parameters drawn from probability distributions is used to determine a set of parameter values. Using this approach, model reliability was evaluated based on its capacity to globally limit the uncertainty. The method was applied to a large full-scale WWTP for which quantity and quality data was gathered. The analysis enabled to gain useful insights for WWTP modelling identifying the crucial aspects where higher uncertainty rely and where therefore, more efforts should be provided in terms of both data gathering and modelling practises.
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 4; Issue 2. Uncertainty in the Real World - Fuzzy Sets. Satish Kumar. General Article Volume 4 Issue 2 February 1999 pp 37-47. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/004/02/0037-0047 ...
Likelihood Analysis of Supersymmetric SU(5) GUTs
Bagnaschi, E.
2017-01-01
We perform a likelihood analysis of the constraints from accelerator experiments and astrophysical observations on supersymmetric (SUSY) models with SU(5) boundary conditions on soft SUSY-breaking parameters at the GUT scale. The parameter space of the models studied has 7 parameters: a universal gaugino mass $m_{1/2}$, distinct masses for the scalar partners of matter fermions in five- and ten-dimensional representations of SU(5), $m_5$ and $m_{10}$, and for the $\\mathbf{5}$ and $\\mathbf{\\bar 5}$ Higgs representations $m_{H_u}$ and $m_{H_d}$, a universal trilinear soft SUSY-breaking parameter $A_0$, and the ratio of Higgs vevs $\\tan \\beta$. In addition to previous constraints from direct sparticle searches, low-energy and flavour observables, we incorporate constraints based on preliminary results from 13 TeV LHC searches for jets + MET events and long-lived particles, as well as the latest PandaX-II and LUX searches for direct Dark Matter detection. In addition to previously-identified mechanisms for bringi...
Reducing the likelihood of long tennis matches.
Barnett, Tristan; Alan, Brown; Pollard, Graham
2006-01-01
Long matches can cause problems for tournaments. For example, the starting times of subsequent matches can be substantially delayed causing inconvenience to players, spectators, officials and television scheduling. They can even be seen as unfair in the tournament setting when the winner of a very long match, who may have negative aftereffects from such a match, plays the winner of an average or shorter length match in the next round. Long matches can also lead to injuries to the participating players. One factor that can lead to long matches is the use of the advantage set as the fifth set, as in the Australian Open, the French Open and Wimbledon. Another factor is long rallies and a greater than average number of points per game. This tends to occur more frequently on the slower surfaces such as at the French Open. The mathematical method of generating functions is used to show that the likelihood of long matches can be substantially reduced by using the tiebreak game in the fifth set, or more effectively by using a new type of game, the 50-40 game, throughout the match. Key PointsThe cumulant generating function has nice properties for calculating the parameters of distributions in a tennis matchA final tiebreaker set reduces the length of matches as currently being used in the US OpenA new 50-40 game reduces the length of matches whilst maintaining comparable probabilities for the better player to win the match.
Maximum likelihood window for time delay estimation
International Nuclear Information System (INIS)
Lee, Young Sup; Yoon, Dong Jin; Kim, Chi Yup
2004-01-01
Time delay estimation for the detection of leak location in underground pipelines is critically important. Because the exact leak location depends upon the precision of the time delay between sensor signals due to leak noise and the speed of elastic waves, the research on the estimation of time delay has been one of the key issues in leak lovating with the time arrival difference method. In this study, an optimal Maximum Likelihood window is considered to obtain a better estimation of the time delay. This method has been proved in experiments, which can provide much clearer and more precise peaks in cross-correlation functions of leak signals. The leak location error has been less than 1 % of the distance between sensors, for example the error was not greater than 3 m for 300 m long underground pipelines. Apart from the experiment, an intensive theoretical analysis in terms of signal processing has been described. The improved leak locating with the suggested method is due to the windowing effect in frequency domain, which offers a weighting in significant frequencies.
Optimized Large-scale CMB Likelihood and Quadratic Maximum Likelihood Power Spectrum Estimation
Gjerløw, E.; Colombo, L. P. L.; Eriksen, H. K.; Górski, K. M.; Gruppuso, A.; Jewell, J. B.; Plaszczynski, S.; Wehus, I. K.
2015-11-01
We revisit the problem of exact cosmic microwave background (CMB) likelihood and power spectrum estimation with the goal of minimizing computational costs through linear compression. This idea was originally proposed for CMB purposes by Tegmark et al., and here we develop it into a fully functioning computational framework for large-scale polarization analysis, adopting WMAP as a working example. We compare five different linear bases (pixel space, harmonic space, noise covariance eigenvectors, signal-to-noise covariance eigenvectors, and signal-plus-noise covariance eigenvectors) in terms of compression efficiency, and find that the computationally most efficient basis is the signal-to-noise eigenvector basis, which is closely related to the Karhunen-Loeve and Principal Component transforms, in agreement with previous suggestions. For this basis, the information in 6836 unmasked WMAP sky map pixels can be compressed into a smaller set of 3102 modes, with a maximum error increase of any single multipole of 3.8% at ℓ ≤ 32 and a maximum shift in the mean values of a joint distribution of an amplitude-tilt model of 0.006σ. This compression reduces the computational cost of a single likelihood evaluation by a factor of 5, from 38 to 7.5 CPU seconds, and it also results in a more robust likelihood by implicitly regularizing nearly degenerate modes. Finally, we use the same compression framework to formulate a numerically stable and computationally efficient variation of the Quadratic Maximum Likelihood implementation, which requires less than 3 GB of memory and 2 CPU minutes per iteration for ℓ ≤ 32, rendering low-ℓ QML CMB power spectrum analysis fully tractable on a standard laptop.
Maximum likelihood versus likelihood-free quantum system identification in the atom maser
International Nuclear Information System (INIS)
Catana, Catalin; Kypraios, Theodore; Guţă, Mădălin
2014-01-01
We consider the problem of estimating a dynamical parameter of a Markovian quantum open system (the atom maser), by performing continuous time measurements in the system's output (outgoing atoms). Two estimation methods are investigated and compared. Firstly, the maximum likelihood estimator (MLE) takes into account the full measurement data and is asymptotically optimal in terms of its mean square error. Secondly, the ‘likelihood-free’ method of approximate Bayesian computation (ABC) produces an approximation of the posterior distribution for a given set of summary statistics, by sampling trajectories at different parameter values and comparing them with the measurement data via chosen statistics. Building on previous results which showed that atom counts are poor statistics for certain values of the Rabi angle, we apply MLE to the full measurement data and estimate its Fisher information. We then select several correlation statistics such as waiting times, distribution of successive identical detections, and use them as input of the ABC algorithm. The resulting posterior distribution follows closely the data likelihood, showing that the selected statistics capture ‘most’ statistical information about the Rabi angle. (paper)
Mapping flood hazards under uncertainty through probabilistic flood inundation maps
Stephens, T.; Bledsoe, B. P.; Miller, A. J.; Lee, G.
2017-12-01
Changing precipitation, rapid urbanization, and population growth interact to create unprecedented challenges for flood mitigation and management. Standard methods for estimating risk from flood inundation maps generally involve simulations of floodplain hydraulics for an established regulatory discharge of specified frequency. Hydraulic model results are then geospatially mapped and depicted as a discrete boundary of flood extents and a binary representation of the probability of inundation (in or out) that is assumed constant over a project's lifetime. Consequently, existing methods utilized to define flood hazards and assess risk management are hindered by deterministic approaches that assume stationarity in a nonstationary world, failing to account for spatio-temporal variability of climate and land use as they translate to hydraulic models. This presentation outlines novel techniques for portraying flood hazards and the results of multiple flood inundation maps spanning hydroclimatic regions. Flood inundation maps generated through modeling of floodplain hydraulics are probabilistic reflecting uncertainty quantified through Monte-Carlo analyses of model inputs and parameters under current and future scenarios. The likelihood of inundation and range of variability in flood extents resulting from Monte-Carlo simulations are then compared with deterministic evaluations of flood hazards from current regulatory flood hazard maps. By facilitating alternative approaches of portraying flood hazards, the novel techniques described in this presentation can contribute to a shifting paradigm in flood management that acknowledges the inherent uncertainty in model estimates and the nonstationary behavior of land use and climate.
On Bayesian Testing of Additive Conjoint Measurement Axioms Using Synthetic Likelihood.
Karabatsos, George
2018-06-01
This article introduces a Bayesian method for testing the axioms of additive conjoint measurement. The method is based on an importance sampling algorithm that performs likelihood-free, approximate Bayesian inference using a synthetic likelihood to overcome the analytical intractability of this testing problem. This new method improves upon previous methods because it provides an omnibus test of the entire hierarchy of cancellation axioms, beyond double cancellation. It does so while accounting for the posterior uncertainty that is inherent in the empirical orderings that are implied by these axioms, together. The new method is illustrated through a test of the cancellation axioms on a classic survey data set, and through the analysis of simulated data.
Planck 2015 results. XI. CMB power spectra, likelihoods, and robustness of parameters
Aghanim, N.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Banday, A.J.; Barreiro, R.B.; Bartlett, J.G.; Bartolo, N.; Battaner, E.; Benabed, K.; Benoit, A.; Benoit-Levy, A.; Bernard, J.P.; Bersanelli, M.; Bielewicz, P.; Bock, J.J.; Bonaldi, A.; Bonavera, L.; Bond, J.R.; Borrill, J.; Bouchet, F.R.; Boulanger, F.; Bucher, M.; Burigana, C.; Butler, R.C.; Calabrese, E.; Cardoso, J.F.; Catalano, A.; Challinor, A.; Chiang, H.C.; Christensen, P.R.; Clements, D.L.; Colombo, L.P.L.; Combet, C.; Coulais, A.; Crill, B.P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R.D.; Davis, R.J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Desert, F.X.; Di Valentino, E.; Dickinson, C.; Diego, J.M.; Dolag, K.; Dole, H.; Donzelli, S.; Dore, O.; Douspis, M.; Ducout, A.; Dunkley, J.; Dupac, X.; Efstathiou, G.; Elsner, F.; Ensslin, T.A.; Eriksen, H.K.; Fergusson, J.; Finelli, F.; Forni, O.; Frailis, M.; Fraisse, A.A.; Franceschi, E.; Frejsel, A.; Galeotta, S.; Galli, S.; Ganga, K.; Gauthier, C.; Gerbino, M.; Giard, M.; Gjerlow, E.; Gonzalez-Nuevo, J.; Gorski, K.M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J.E.; Hamann, J.; Hansen, F.K.; Harrison, D.L.; Helou, G.; Henrot-Versille, S.; Hernandez-Monteagudo, C.; Herranz, D.; Hildebrandt, S.R.; Hivon, E.; Holmes, W.A.; Hornstrup, A.; Huffenberger, K.M.; Hurier, G.; Jaffe, A.H.; Jones, W.C.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Kiiveri, K.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lahteenmaki, A.; Lamarre, J.M.; Lasenby, A.; Lattanzi, M.; Lawrence, C.R.; Le Jeune, M.; Leonardi, R.; Lesgourgues, J.; Levrier, F.; Lewis, A.; Liguori, M.; Lilje, P.B.; Lilley, M.; Linden-Vornle, M.; Lindholm, V.; Lopez-Caniego, M.; Macias-Perez, J.F.; Maffei, B.; Maggio, G.; Maino, D.; Mandolesi, N.; Mangilli, A.; Maris, M.; Martin, P.G.; Martinez-Gonzalez, E.; Masi, S.; Matarrese, S.; Meinhold, P.R.; Melchiorri, A.; Migliaccio, M.; Millea, M.; Miville-Deschenes, M.A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Munshi, D.; Murphy, J.A.; Narimani, A.; Naselsky, P.; Nati, F.; Natoli, P.; Noviello, F.; Novikov, D.; Novikov, I.; Oxborrow, C.A.; Paci, F.; Pagano, L.; Pajot, F.; Paoletti, D.; Partridge, B.; Pasian, F.; Patanchon, G.; Pearson, T.J.; Perdereau, O.; Perotto, L.; Pettorino, V.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Pratt, G.W.; Prunet, S.; Puget, J.L.; Rachen, J.P.; Reinecke, M.; Remazeilles, M.; Renault, C.; Renzi, A.; Ristorcelli, I.; Rocha, G.; Rossetti, M.; Roudier, G.; d'Orfeuil, B.Rouille; Rubino-Martin, J.A.; Rusholme, B.; Salvati, L.; Sandri, M.; Santos, D.; Savelainen, M.; Savini, G.; Scott, D.; Serra, P.; Spencer, L.D.; Spinelli, M.; Stolyarov, V.; Stompor, R.; Sunyaev, R.; Sutton, D.; Suur-Uski, A.S.; Sygnet, J.F.; Tauber, J.A.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Trombetti, T.; Tucci, M.; Tuovinen, J.; Umana, G.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Vielva, P.; Villa, F.; Wade, L.A.; Wandelt, B.D.; Wehus, I.K.; Yvon, D.; Zacchei, A.; Zonca, A.
2016-01-01
This paper presents the Planck 2015 likelihoods, statistical descriptions of the 2-point correlation functions of CMB temperature and polarization. They use the hybrid approach employed previously: pixel-based at low multipoles, $\\ell$, and a Gaussian approximation to the distribution of cross-power spectra at higher $\\ell$. The main improvements are the use of more and better processed data and of Planck polarization data, and more detailed foreground and instrumental models. More than doubling the data allows further checks and enhanced immunity to systematics. Progress in foreground modelling enables a larger sky fraction, contributing to enhanced precision. Improvements in processing and instrumental models further reduce uncertainties. Extensive tests establish robustness and accuracy, from temperature, from polarization, and from their combination, and show that the {\\Lambda}CDM model continues to offer a very good fit. We further validate the likelihood against specific extensions to this baseline, suc...
Directory of Open Access Journals (Sweden)
Mawardi Bahri
2017-01-01
Full Text Available The continuous quaternion wavelet transform (CQWT is a generalization of the classical continuous wavelet transform within the context of quaternion algebra. First of all, we show that the directional quaternion Fourier transform (QFT uncertainty principle can be obtained using the component-wise QFT uncertainty principle. Based on this method, the directional QFT uncertainty principle using representation of polar coordinate form is easily derived. We derive a variation on uncertainty principle related to the QFT. We state that the CQWT of a quaternion function can be written in terms of the QFT and obtain a variation on uncertainty principle related to the CQWT. Finally, we apply the extended uncertainty principles and properties of the CQWT to establish logarithmic uncertainty principles related to generalized transform.
New method to incorporate Type B uncertainty into least-squares procedures in radionuclide metrology
International Nuclear Information System (INIS)
Han, Jubong; Lee, K.B.; Lee, Jong-Man; Park, Tae Soon; Oh, J.S.; Oh, Pil-Jei
2016-01-01
We discuss a new method to incorporate Type B uncertainty into least-squares procedures. The new method is based on an extension of the likelihood function from which a conventional least-squares function is derived. The extended likelihood function is the product of the original likelihood function with additional PDFs (Probability Density Functions) that characterize the Type B uncertainties. The PDFs are considered to describe one's incomplete knowledge on correction factors being called nuisance parameters. We use the extended likelihood function to make point and interval estimations of parameters in the basically same way as the least-squares function used in the conventional least-squares method is derived. Since the nuisance parameters are not of interest and should be prevented from appearing in the final result, we eliminate such nuisance parameters by using the profile likelihood. As an example, we present a case study for a linear regression analysis with a common component of Type B uncertainty. In this example we compare the analysis results obtained from using our procedure with those from conventional methods. - Highlights: • A new method proposed to incorporate Type B uncertainty into least-squares method. • The method constructed from the likelihood function and PDFs of Type B uncertainty. • A case study performed to compare results from the new and the conventional method. • Fitted parameters are consistent but with larger uncertainties in the new method.
Likelihood analysis of supersymmetric SU(5) GUTs
Energy Technology Data Exchange (ETDEWEB)
Bagnaschi, E.; Weiglein, G. [DESY, Hamburg (Germany); Costa, J.C.; Buchmueller, O.; Citron, M.; Richards, A.; De Vries, K.J. [Imperial College, High Energy Physics Group, Blackett Laboratory, London (United Kingdom); Sakurai, K. [University of Durham, Science Laboratories, Department of Physics, Institute for Particle Physics Phenomenology, Durham (United Kingdom); University of Warsaw, Faculty of Physics, Institute of Theoretical Physics, Warsaw (Poland); Borsato, M.; Chobanova, V.; Lucio, M.; Martinez Santos, D. [Universidade de Santiago de Compostela, Santiago de Compostela (Spain); Cavanaugh, R. [Fermi National Accelerator Laboratory, Batavia, IL (United States); University of Illinois at Chicago, Physics Department, Chicago, IL (United States); Roeck, A. de [CERN, Experimental Physics Department, Geneva (Switzerland); Antwerp University, Wilrijk (Belgium); Dolan, M.J. [University of Melbourne, ARC Centre of Excellence for Particle Physics at the Terascale, School of Physics, Parkville (Australia); Ellis, J.R. [King' s College London, Theoretical Particle Physics and Cosmology Group, Department of Physics, London (United Kingdom); Theoretical Physics Department, CERN, Geneva 23 (Switzerland); Flaecher, H. [University of Bristol, H.H. Wills Physics Laboratory, Bristol (United Kingdom); Heinemeyer, S. [Campus of International Excellence UAM+CSIC, Cantoblanco, Madrid (Spain); Instituto de Fisica Teorica UAM-CSIC, Madrid (Spain); Instituto de Fisica de Cantabria (CSIC-UC), Santander (Spain); Isidori, G. [Universitaet Zuerich, Physik-Institut, Zurich (Switzerland); Olive, K.A. [University of Minnesota, William I. Fine Theoretical Physics Institute, School of Physics and Astronomy, Minneapolis, MN (United States)
2017-02-15
We perform a likelihood analysis of the constraints from accelerator experiments and astrophysical observations on supersymmetric (SUSY) models with SU(5) boundary conditions on soft SUSY-breaking parameters at the GUT scale. The parameter space of the models studied has seven parameters: a universal gaugino mass m{sub 1/2}, distinct masses for the scalar partners of matter fermions in five- and ten-dimensional representations of SU(5), m{sub 5} and m{sub 10}, and for the 5 and anti 5 Higgs representations m{sub H{sub u}} and m{sub H{sub d}}, a universal trilinear soft SUSY-breaking parameter A{sub 0}, and the ratio of Higgs vevs tan β. In addition to previous constraints from direct sparticle searches, low-energy and flavour observables, we incorporate constraints based on preliminary results from 13 TeV LHC searches for jets + E{sub T} events and long-lived particles, as well as the latest PandaX-II and LUX searches for direct Dark Matter detection. In addition to previously identified mechanisms for bringing the supersymmetric relic density into the range allowed by cosmology, we identify a novel u{sub R}/c{sub R} - χ{sup 0}{sub 1} coannihilation mechanism that appears in the supersymmetric SU(5) GUT model and discuss the role of ν{sub τ} coannihilation. We find complementarity between the prospects for direct Dark Matter detection and SUSY searches at the LHC. (orig.)
Likelihood analysis of supersymmetric SU(5) GUTs
Energy Technology Data Exchange (ETDEWEB)
Bagnaschi, E. [DESY, Hamburg (Germany); Costa, J.C. [Imperial College, London (United Kingdom). Blackett Lab.; Sakurai, K. [Durham Univ. (United Kingdom). Inst. for Particle Physics Phenomonology; Warsaw Univ. (Poland). Inst. of Theoretical Physics; Collaboration: MasterCode Collaboration; and others
2016-10-15
We perform a likelihood analysis of the constraints from accelerator experiments and astrophysical observations on supersymmetric (SUSY) models with SU(5) boundary conditions on soft SUSY-breaking parameters at the GUT scale. The parameter space of the models studied has 7 parameters: a universal gaugino mass m{sub 1/2}, distinct masses for the scalar partners of matter fermions in five- and ten-dimensional representations of SU(5), m{sub 5} and m{sub 10}, and for the 5 and anti 5 Higgs representations m{sub H{sub u}} and m{sub H{sub d}}, a universal trilinear soft SUSY-breaking parameter A{sub 0}, and the ratio of Higgs vevs tan β. In addition to previous constraints from direct sparticle searches, low-energy and avour observables, we incorporate constraints based on preliminary results from 13 TeV LHC searches for jets+E{sub T} events and long-lived particles, as well as the latest PandaX-II and LUX searches for direct Dark Matter detection. In addition to previously-identified mechanisms for bringing the supersymmetric relic density into the range allowed by cosmology, we identify a novel u{sub R}/c{sub R}-χ{sup 0}{sub 1} coannihilation mechanism that appears in the supersymmetric SU(5) GUT model and discuss the role of ν{sub T} coannihilation. We find complementarity between the prospects for direct Dark Matter detection and SUSY searches at the LHC.
A maximum pseudo-likelihood approach for estimating species trees under the coalescent model
Directory of Open Access Journals (Sweden)
Edwards Scott V
2010-10-01
Full Text Available Abstract Background Several phylogenetic approaches have been developed to estimate species trees from collections of gene trees. However, maximum likelihood approaches for estimating species trees under the coalescent model are limited. Although the likelihood of a species tree under the multispecies coalescent model has already been derived by Rannala and Yang, it can be shown that the maximum likelihood estimate (MLE of the species tree (topology, branch lengths, and population sizes from gene trees under this formula does not exist. In this paper, we develop a pseudo-likelihood function of the species tree to obtain maximum pseudo-likelihood estimates (MPE of species trees, with branch lengths of the species tree in coalescent units. Results We show that the MPE of the species tree is statistically consistent as the number M of genes goes to infinity. In addition, the probability that the MPE of the species tree matches the true species tree converges to 1 at rate O(M -1. The simulation results confirm that the maximum pseudo-likelihood approach is statistically consistent even when the species tree is in the anomaly zone. We applied our method, Maximum Pseudo-likelihood for Estimating Species Trees (MP-EST to a mammal dataset. The four major clades found in the MP-EST tree are consistent with those in the Bayesian concatenation tree. The bootstrap supports for the species tree estimated by the MP-EST method are more reasonable than the posterior probability supports given by the Bayesian concatenation method in reflecting the level of uncertainty in gene trees and controversies over the relationship of four major groups of placental mammals. Conclusions MP-EST can consistently estimate the topology and branch lengths (in coalescent units of the species tree. Although the pseudo-likelihood is derived from coalescent theory, and assumes no gene flow or horizontal gene transfer (HGT, the MP-EST method is robust to a small amount of HGT in the
Decision making under uncertainty
International Nuclear Information System (INIS)
Cyert, R.M.
1989-01-01
This paper reports on ways of improving the reliability of products and systems in this country if we are to survive as a first-rate industrial power. The use of statistical techniques have, since the 1920s, been viewed as one of the methods for testing quality and estimating the level of quality in a universe of output. Statistical quality control is not relevant, generally, to improving systems in an industry like yours, but certainly the use of probability concepts is of significance. In addition, when it is recognized that part of the problem involves making decisions under uncertainty, it becomes clear that techniques such as sequential decision making and Bayesian analysis become major methodological approaches that must be utilized
Stochastic variational approach to minimum uncertainty states
Energy Technology Data Exchange (ETDEWEB)
Illuminati, F.; Viola, L. [Dipartimento di Fisica, Padova Univ. (Italy)
1995-05-21
We introduce a new variational characterization of Gaussian diffusion processes as minimum uncertainty states. We then define a variational method constrained by kinematics of diffusions and Schroedinger dynamics to seek states of local minimum uncertainty for general non-harmonic potentials. (author)
Gamma-Ray Telescope and Uncertainty Principle
Shivalingaswamy, T.; Kagali, B. A.
2012-01-01
Heisenberg's Uncertainty Principle is one of the important basic principles of quantum mechanics. In most of the books on quantum mechanics, this uncertainty principle is generally illustrated with the help of a gamma ray microscope, wherein neither the image formation criterion nor the lens properties are taken into account. Thus a better…
Inflation and Inflation Uncertainty in Turkey
dogru, bulent
2014-01-01
Abstract: In this study, the relationship between inflation and inflation uncertainty is analyzed using Granger causality tests with annual inflation series covering the time period 1923 to 2012 for Turkish Economy. Inflation uncertainty is measured by Exponential Generalized Autoregressive Conditional Heteroskedastic model. Econometric findings suggest that although in long run the Friedman's hypothesis that high inflation increases inflation ...
Operational Flexibility Responses to Environmental Uncertainties
Miller, Kent D.
1994-01-01
This study develops and tests a behavioral model of organizational changes in operational flexibility. Regression results using an international data set provide strong support for the general proposition that uncertainties associated with different environmental components--poitical, government policy, macroeconomic, competitive, input and product demand uncertainties--have different implications for firm internal, locational, and supploer flexibility. Slack acts as a buffer attenuating, a...
Uncertainty Communication. Issues and good practice
International Nuclear Information System (INIS)
Kloprogge, P.; Van der Sluijs, J.; Wardekker, A.
2007-12-01
In 2003 the Netherlands Environmental Assessment Agency (MNP) published the RIVM/MNP Guidance for Uncertainty Assessment and Communication. The Guidance assists in dealing with uncertainty in environmental assessments. Dealing with uncertainty is essential because assessment results regarding complex environmental issues are of limited value if the uncertainties have not been taken into account adequately. A careful analysis of uncertainties in an environmental assessment is required, but even more important is the effective communication of these uncertainties in the presentation of assessment results. The Guidance yields rich and differentiated insights in uncertainty, but the relevance of this uncertainty information may vary across audiences and uses of assessment results. Therefore, the reporting of uncertainties is one of the six key issues that is addressed in the Guidance. In practice, users of the Guidance felt a need for more practical assistance in the reporting of uncertainty information. This report explores the issue of uncertainty communication in more detail, and contains more detailed guidance on the communication of uncertainty. In order to make this a 'stand alone' document several questions that are mentioned in the detailed Guidance have been repeated here. This document thus has some overlap with the detailed Guidance. Part 1 gives a general introduction to the issue of communicating uncertainty information. It offers guidelines for (fine)tuning the communication to the intended audiences and context of a report, discusses how readers of a report tend to handle uncertainty information, and ends with a list of criteria that uncertainty communication needs to meet to increase its effectiveness. Part 2 helps writers to analyze the context in which communication takes place, and helps to map the audiences, and their information needs. It further helps to reflect upon anticipated uses and possible impacts of the uncertainty information on the
Estimation of stochastic frontier models with fixed-effects through Monte Carlo Maximum Likelihood
Emvalomatis, G.; Stefanou, S.E.; Oude Lansink, A.G.J.M.
2011-01-01
Estimation of nonlinear fixed-effects models is plagued by the incidental parameters problem. This paper proposes a procedure for choosing appropriate densities for integrating the incidental parameters from the likelihood function in a general context. The densities are based on priors that are
Likelihood of illegal alcohol sales at professional sport stadiums.
Toomey, Traci L; Erickson, Darin J; Lenk, Kathleen M; Kilian, Gunna R
2008-11-01
Several studies have assessed the propensity for illegal alcohol sales at licensed alcohol establishments and community festivals, but no previous studies examined the propensity for these sales at professional sport stadiums. In this study, we assessed the likelihood of alcohol sales to both underage youth and obviously intoxicated patrons at professional sports stadiums across the United States, and assessed the factors related to likelihood of both types of alcohol sales. We conducted pseudo-underage (i.e., persons age 21 or older who appear under 21) and pseudo-intoxicated (i.e., persons feigning intoxication) alcohol purchase attempts at stadiums that house professional hockey, basketball, baseball, and football teams. We conducted the purchase attempts at 16 sport stadiums located in 5 states. We measured 2 outcome variables: pseudo-underage sale (yes, no) and pseudo-intoxicated sale (yes, no), and 3 types of independent variables: (1) seller characteristics, (2) purchase attempt characteristics, and (3) event characteristics. Following univariate and bivariate analyses, we a separate series of logistic generalized mixed regression models for each outcome variable. The overall sales rates to the pseudo-underage and pseudo-intoxicated buyers were 18% and 74%, respectively. In the multivariate logistic analyses, we found that the odds of a sale to a pseudo-underage buyer in the stands was 2.9 as large as the odds of a sale at the concession booths (30% vs. 13%; p = 0.01). The odds of a sale to an obviously intoxicated buyer in the stands was 2.9 as large as the odds of a sale at the concession booths (89% vs. 73%; p = 0.02). Similar to studies assessing illegal alcohol sales at licensed alcohol establishments and community festivals, findings from this study shows the need for interventions specifically focused on illegal alcohol sales at professional sporting events.
Critical loads - assessment of uncertainty
Energy Technology Data Exchange (ETDEWEB)
Barkman, A.
1998-10-01
The effects of data uncertainty in applications of the critical loads concept were investigated on different spatial resolutions in Sweden and northern Czech Republic. Critical loads of acidity (CL) were calculated for Sweden using the biogeochemical model PROFILE. Three methods with different structural complexity were used to estimate the adverse effects of S0{sub 2} concentrations in northern Czech Republic. Data uncertainties in the calculated critical loads/levels and exceedances (EX) were assessed using Monte Carlo simulations. Uncertainties within cumulative distribution functions (CDF) were aggregated by accounting for the overlap between site specific confidence intervals. Aggregation of data uncertainties within CDFs resulted in lower CL and higher EX best estimates in comparison with percentiles represented by individual sites. Data uncertainties were consequently found to advocate larger deposition reductions to achieve non-exceedance based on low critical loads estimates on 150 x 150 km resolution. Input data were found to impair the level of differentiation between geographical units at all investigated resolutions. Aggregation of data uncertainty within CDFs involved more constrained confidence intervals for a given percentile. Differentiation as well as identification of grid cells on 150 x 150 km resolution subjected to EX was generally improved. Calculation of the probability of EX was shown to preserve the possibility to differentiate between geographical units. Re-aggregation of the 95%-ile EX on 50 x 50 km resolution generally increased the confidence interval for each percentile. Significant relationships were found between forest decline and the three methods addressing risks induced by S0{sub 2} concentrations. Modifying S0{sub 2} concentrations by accounting for the length of the vegetation period was found to constitute the most useful trade-off between structural complexity, data availability and effects of data uncertainty. Data
Instrument uncertainty predictions
International Nuclear Information System (INIS)
Coutts, D.A.
1991-07-01
The accuracy of measurements and correlations should normally be provided for most experimental activities. The uncertainty is a measure of the accuracy of a stated value or equation. The uncertainty term reflects a combination of instrument errors, modeling limitations, and phenomena understanding deficiencies. This report provides several methodologies to estimate an instrument's uncertainty when used in experimental work. Methods are shown to predict both the pretest and post-test uncertainty
The behavior of the likelihood ratio test for testing missingness
Hens, Niel; Aerts, Marc; Molenberghs, Geert; Thijs, Herbert
2003-01-01
To asses the sensitivity of conclusions to model choices in the context of selection models for non-random dropout, one can oppose the different missing mechanisms to each other; e.g. by the likelihood ratio tests. The finite sample behavior of the null distribution and the power of the likelihood ratio test is studied under a variety of missingness mechanisms. missing data; sensitivity analysis; likelihood ratio test; missing mechanisms
A likelihood ratio test for species membership based on DNA sequence data
DEFF Research Database (Denmark)
Matz, Mikhail V.; Nielsen, Rasmus
2005-01-01
DNA barcoding as an approach for species identification is rapidly increasing in popularity. However, it remains unclear which statistical procedures should accompany the technique to provide a measure of uncertainty. Here we describe a likelihood ratio test which can be used to test if a sampled...... sequence is a member of an a priori specified species. We investigate the performance of the test using coalescence simulations, as well as using the real data from butterflies and frogs representing two kinds of challenge for DNA barcoding: extremely low and extremely high levels of sequence variability....
Model Uncertainty for Bilinear Hysteretic Systems
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard; Thoft-Christensen, Palle
1984-01-01
. The statistical uncertainty -due to lack of information can e.g. be taken into account by describing the variables by predictive density functions, Veneziano [2). In general, model uncertainty is the uncertainty connected with mathematical modelling of the physical reality. When structural reliability analysis...... is related to the concept of a failure surface (or limit state surface) in the n-dimensional basic variable space then model uncertainty is at least due to the neglected variables, the modelling of the failure surface and the computational technique used. A more precise definition is given in section 2...
Penalized Maximum Likelihood Estimation for univariate normal mixture distributions
International Nuclear Information System (INIS)
Ridolfi, A.; Idier, J.
2001-01-01
Due to singularities of the likelihood function, the maximum likelihood approach for the estimation of the parameters of normal mixture models is an acknowledged ill posed optimization problem. Ill posedness is solved by penalizing the likelihood function. In the Bayesian framework, it amounts to incorporating an inverted gamma prior in the likelihood function. A penalized version of the EM algorithm is derived, which is still explicit and which intrinsically assures that the estimates are not singular. Numerical evidence of the latter property is put forward with a test
Generalization of stochastic visuomotor rotations.
Directory of Open Access Journals (Sweden)
Hugo L Fernandes
Full Text Available Generalization studies examine the influence of perturbations imposed on one movement onto other movements. The strength of generalization is traditionally interpreted as a reflection of the similarity of the underlying neural representations. Uncertainty fundamentally affects both sensory integration and learning and is at the heart of many theories of neural representation. However, little is known about how uncertainty, resulting from variability in the environment, affects generalization curves. Here we extend standard movement generalization experiments to ask how uncertainty affects the generalization of visuomotor rotations. We find that although uncertainty affects how fast subjects learn, the perturbation generalizes independently of uncertainty.
Maximum likelihood estimation for cytogenetic dose-response curves
International Nuclear Information System (INIS)
Frome, E.L; DuFrain, R.J.
1983-10-01
In vitro dose-response curves are used to describe the relation between the yield of dicentric chromosome aberrations and radiation dose for human lymphocytes. The dicentric yields follow the Poisson distribution, and the expected yield depends on both the magnitude and the temporal distribution of the dose for low LET radiation. A general dose-response model that describes this relation has been obtained by Kellerer and Rossi using the theory of dual radiation action. The yield of elementary lesions is kappa[γd + g(t, tau)d 2 ], where t is the time and d is dose. The coefficient of the d 2 term is determined by the recovery function and the temporal mode of irradiation. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting models are intrinsically nonlinear in the parameters. A general purpose maximum likelihood estimation procedure is described and illustrated with numerical examples from both experimental designs. Poisson regression analysis is used for estimation, hypothesis testing, and regression diagnostics. Results are discussed in the context of exposure assessment procedures for both acute and chronic human radiation exposure
Maximum likelihood estimation for cytogenetic dose-response curves
International Nuclear Information System (INIS)
Frome, E.L.; DuFrain, R.J.
1986-01-01
In vitro dose-response curves are used to describe the relation between chromosome aberrations and radiation dose for human lymphocytes. The lymphocytes are exposed to low-LET radiation, and the resulting dicentric chromosome aberrations follow the Poisson distribution. The expected yield depends on both the magnitude and the temporal distribution of the dose. A general dose-response model that describes this relation has been presented by Kellerer and Rossi (1972, Current Topics on Radiation Research Quarterly 8, 85-158; 1978, Radiation Research 75, 471-488) using the theory of dual radiation action. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting dose-time-response models are intrinsically nonlinear in the parameters. A general-purpose maximum likelihood estimation procedure is described, and estimation for the nonlinear models is illustrated with numerical examples from both experimental designs. Poisson regression analysis is used for estimation, hypothesis testing, and regression diagnostics. Results are discussed in the context of exposure assessment procedures for both acute and chronic human radiation exposure
Maximum likelihood estimation for cytogenetic dose-response curves
Energy Technology Data Exchange (ETDEWEB)
Frome, E.L; DuFrain, R.J.
1983-10-01
In vitro dose-response curves are used to describe the relation between the yield of dicentric chromosome aberrations and radiation dose for human lymphocytes. The dicentric yields follow the Poisson distribution, and the expected yield depends on both the magnitude and the temporal distribution of the dose for low LET radiation. A general dose-response model that describes this relation has been obtained by Kellerer and Rossi using the theory of dual radiation action. The yield of elementary lesions is kappa(..gamma..d + g(t, tau)d/sup 2/), where t is the time and d is dose. The coefficient of the d/sup 2/ term is determined by the recovery function and the temporal mode of irradiation. Two special cases of practical interest are split-dose and continuous exposure experiments, and the resulting models are intrinsically nonlinear in the parameters. A general purpose maximum likelihood estimation procedure is described and illustrated with numerical examples from both experimental designs. Poisson regression analysis is used for estimation, hypothesis testing, and regression diagnostics. Results are discussed in the context of exposure assessment procedures for both acute and chronic human radiation exposure.
Exploring the implication of climate process uncertainties within the Earth System Framework
Booth, B.; Lambert, F. H.; McNeal, D.; Harris, G.; Sexton, D.; Boulton, C.; Murphy, J.
2011-12-01
Uncertainties in the magnitude of future climate change have been a focus of a great deal of research. Much of the work with General Circulation Models has focused on the atmospheric response to changes in atmospheric composition, while other processes remain outside these frameworks. Here we introduce an ensemble of new simulations, based on an Earth System configuration of HadCM3C, designed to explored uncertainties in both physical (atmospheric, oceanic and aerosol physics) and carbon cycle processes, using perturbed parameter approaches previously used to explore atmospheric uncertainty. Framed in the context of the climate response to future changes in emissions, the resultant future projections represent significantly broader uncertainty than existing concentration driven GCM assessments. The systematic nature of the ensemble design enables interactions between components to be explored. For example, we show how metrics of physical processes (such as climate sensitivity) are also influenced carbon cycle parameters. The suggestion from this work is that carbon cycle processes represent a comparable contribution to uncertainty in future climate projections as contributions from atmospheric feedbacks more conventionally explored. The broad range of climate responses explored within these ensembles, rather than representing a reason for inaction, provide information on lower likelihood but high impact changes. For example while the majority of these simulations suggest that future Amazon forest extent is resilient to the projected climate changes, a small number simulate dramatic forest dieback. This ensemble represents a framework to examine these risks, breaking them down into physical processes (such as ocean temperature drivers of rainfall change) and vegetation processes (where uncertainties point towards requirements for new observational constraints).
Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.
2012-11-01
Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.
Directory of Open Access Journals (Sweden)
Mousong Wu
2016-02-01
Full Text Available Water and energy processes in frozen soils are important for better understanding hydrologic processes and water resources management in cold regions. To investigate the water and energy balance in seasonally frozen soils, CoupModel combined with the generalized likelihood uncertainty estimation (GLUE method was used. Simulation work on water and heat processes in frozen soil in northern China during the 2012/2013 winter was conducted. Ensemble simulations through the Monte Carlo sampling method were generated for uncertainty analysis. Behavioral simulations were selected based on combinations of multiple model performance index criteria with respect to simulated soil water and temperature at four depths (5 cm, 15 cm, 25 cm, and 35 cm. Posterior distributions for parameters related to soil hydraulic, radiation processes, and heat transport indicated that uncertainties in both input and model structures could influence model performance in modeling water and heat processes in seasonally frozen soils. Seasonal courses in water and energy partitioning were obvious during the winter. Within the day-cycle, soil evaporation/condensation and energy distributions were well captured and clarified as an important phenomenon in the dynamics of the energy balance system. The combination of the CoupModel simulations with the uncertainty-based calibration method provides a way of understanding the seasonal courses of hydrology and energy processes in cold regions with limited data. Additional measurements may be used to further reduce the uncertainty of regulating factors during the different stages of freezing–thawing.
International Nuclear Information System (INIS)
Andres, T.H.
2002-05-01
This guide applies to the estimation of uncertainty in quantities calculated by scientific, analysis and design computer programs that fall within the scope of AECL's software quality assurance (SQA) manual. The guide weaves together rational approaches from the SQA manual and three other diverse sources: (a) the CSAU (Code Scaling, Applicability, and Uncertainty) evaluation methodology; (b) the ISO Guide,for the Expression of Uncertainty in Measurement; and (c) the SVA (Systems Variability Analysis) method of risk analysis. This report describes the manner by which random and systematic uncertainties in calculated quantities can be estimated and expressed. Random uncertainty in model output can be attributed to uncertainties of inputs. The propagation of these uncertainties through a computer model can be represented in a variety of ways, including exact calculations, series approximations and Monte Carlo methods. Systematic uncertainties emerge from the development of the computer model itself, through simplifications and conservatisms, for example. These must be estimated and combined with random uncertainties to determine the combined uncertainty in a model output. This report also addresses the method by which uncertainties should be employed in code validation, in order to determine whether experiments and simulations agree, and whether or not a code satisfies the required tolerance for its application. (author)
Energy Technology Data Exchange (ETDEWEB)
Andres, T.H
2002-05-01
This guide applies to the estimation of uncertainty in quantities calculated by scientific, analysis and design computer programs that fall within the scope of AECL's software quality assurance (SQA) manual. The guide weaves together rational approaches from the SQA manual and three other diverse sources: (a) the CSAU (Code Scaling, Applicability, and Uncertainty) evaluation methodology; (b) the ISO Guide,for the Expression of Uncertainty in Measurement; and (c) the SVA (Systems Variability Analysis) method of risk analysis. This report describes the manner by which random and systematic uncertainties in calculated quantities can be estimated and expressed. Random uncertainty in model output can be attributed to uncertainties of inputs. The propagation of these uncertainties through a computer model can be represented in a variety of ways, including exact calculations, series approximations and Monte Carlo methods. Systematic uncertainties emerge from the development of the computer model itself, through simplifications and conservatisms, for example. These must be estimated and combined with random uncertainties to determine the combined uncertainty in a model output. This report also addresses the method by which uncertainties should be employed in code validation, in order to determine whether experiments and simulations agree, and whether or not a code satisfies the required tolerance for its application. (author)
Uncertainty and Cognitive Control
Directory of Open Access Journals (Sweden)
Faisal eMushtaq
2011-10-01
Full Text Available A growing trend of neuroimaging, behavioural and computational research has investigated the topic of outcome uncertainty in decision-making. Although evidence to date indicates that humans are very effective in learning to adapt to uncertain situations, the nature of the specific cognitive processes involved in the adaptation to uncertainty are still a matter of debate. In this article, we reviewed evidence suggesting that cognitive control processes are at the heart of uncertainty in decision-making contexts. Available evidence suggests that: (1 There is a strong conceptual overlap between the constructs of uncertainty and cognitive control; (2 There is a remarkable overlap between the neural networks associated with uncertainty and the brain networks subserving cognitive control; (3 The perception and estimation of uncertainty might play a key role in monitoring processes and the evaluation of the need for control; (4 Potential interactions between uncertainty and cognitive control might play a significant role in several affective disorders.
Entry and exit decisions under uncertainty
DEFF Research Database (Denmark)
Kongsted, Hans Christian
1996-01-01
This paper establishes the general deterministic limit that corresponds to Dixit's model of entry and exit decisions under uncertainty. The interlinked nature of decisions is shown to be essential also in the deterministic limit. A numerical example illustrates the result......This paper establishes the general deterministic limit that corresponds to Dixit's model of entry and exit decisions under uncertainty. The interlinked nature of decisions is shown to be essential also in the deterministic limit. A numerical example illustrates the result...
The Marketing Mix Decision Under Uncertainty
Harsharanjeet S. Jagpal; Ivan E. Brick
1982-01-01
This paper develops a marketing mix model under uncertainty using the Capital Asset Pricing Model (CAPM) valuation framework. The model is general because it treats price, advertising and personal selling simultaneously, and allows for general patterns of uncertainty. Because the manager often lacks precise quantitative information about the sales response function, the analysis focuses on the qualitative properties of the model. The methodology of comparative statics is used to determine how...
Robustness of ancestral sequence reconstruction to phylogenetic uncertainty.
Hanson-Smith, Victor; Kolaczkowski, Bryan; Thornton, Joseph W
2010-09-01
Ancestral sequence reconstruction (ASR) is widely used to formulate and test hypotheses about the sequences, functions, and structures of ancient genes. Ancestral sequences are usually inferred from an alignment of extant sequences using a maximum likelihood (ML) phylogenetic algorithm, which calculates the most likely ancestral sequence assuming a probabilistic model of sequence evolution and a specific phylogeny--typically the tree with the ML. The true phylogeny is seldom known with certainty, however. ML methods ignore this uncertainty, whereas Bayesian methods incorporate it by integrating the likelihood of each ancestral state over a distribution of possible trees. It is not known whether Bayesian approaches to phylogenetic uncertainty improve the accuracy of inferred ancestral sequences. Here, we use simulation-based experiments under both simplified and empirically derived conditions to compare the accuracy of ASR carried out using ML and Bayesian approaches. We show that incorporating phylogenetic uncertainty by integrating over topologies very rarely changes the inferred ancestral state and does not improve the accuracy of the reconstructed ancestral sequence. Ancestral state reconstructions are robust to uncertainty about the underlying tree because the conditions that produce phylogenetic uncertainty also make the ancestral state identical across plausible trees; conversely, the conditions under which different phylogenies yield different inferred ancestral states produce little or no ambiguity about the true phylogeny. Our results suggest that ML can produce accurate ASRs, even in the face of phylogenetic uncertainty. Using Bayesian integration to incorporate this uncertainty is neither necessary nor beneficial.
Efficient Detection of Repeating Sites to Accelerate Phylogenetic Likelihood Calculations.
Kobert, K; Stamatakis, A; Flouri, T
2017-03-01
The phylogenetic likelihood function (PLF) is the major computational bottleneck in several applications of evolutionary biology such as phylogenetic inference, species delimitation, model selection, and divergence times estimation. Given the alignment, a tree and the evolutionary model parameters, the likelihood function computes the conditional likelihood vectors for every node of the tree. Vector entries for which all input data are identical result in redundant likelihood operations which, in turn, yield identical conditional values. Such operations can be omitted for improving run-time and, using appropriate data structures, reducing memory usage. We present a fast, novel method for identifying and omitting such redundant operations in phylogenetic likelihood calculations, and assess the performance improvement and memory savings attained by our method. Using empirical and simulated data sets, we show that a prototype implementation of our method yields up to 12-fold speedups and uses up to 78% less memory than one of the fastest and most highly tuned implementations of the PLF currently available. Our method is generic and can seamlessly be integrated into any phylogenetic likelihood implementation. [Algorithms; maximum likelihood; phylogenetic likelihood function; phylogenetics]. © The Author(s) 2016. Published by Oxford University Press, on behalf of the Society of Systematic Biologists.
Planck intermediate results: XVI. Profile likelihoods for cosmological parameters
DEFF Research Database (Denmark)
Bartlett, J.G.; Cardoso, J.-F.; Delabrouille, J.
2014-01-01
We explore the 2013 Planck likelihood function with a high-precision multi-dimensional minimizer (Minuit). This allows a refinement of the CDM best-fit solution with respect to previously-released results, and the construction of frequentist confidence intervals using profile likelihoods. The agr...
The modified signed likelihood statistic and saddlepoint approximations
DEFF Research Database (Denmark)
Jensen, Jens Ledet
1992-01-01
SUMMARY: For a number of tests in exponential families we show that the use of a normal approximation to the modified signed likelihood ratio statistic r * is equivalent to the use of a saddlepoint approximation. This is also true in a large deviation region where the signed likelihood ratio...... statistic r is of order √ n. © 1992 Biometrika Trust....
Likelihood analysis of parity violation in the compound nucleus
International Nuclear Information System (INIS)
Bowman, D.; Sharapov, E.
1993-01-01
We discuss the determination of the root mean-squared matrix element of the parity-violating interaction between compound-nuclear states using likelihood analysis. We briefly review the relevant features of the statistical model of the compound nucleus and the formalism of likelihood analysis. We then discuss the application of likelihood analysis to data on panty-violating longitudinal asymmetries. The reliability of the extracted value of the matrix element and errors assigned to the matrix element is stressed. We treat the situations where the spins of the p-wave resonances are not known and known using experimental data and Monte Carlo techniques. We conclude that likelihood analysis provides a reliable way to determine M and its confidence interval. We briefly discuss some problems associated with the normalization of the likelihood function
Uncertainty Categorization, Modeling, and Management for Regional Water Supply Planning
Fletcher, S.; Strzepek, K. M.; AlSaati, A.; Alhassan, A.
2016-12-01
Many water planners face increased pressure on water supply systems from growing demands, variability in supply and a changing climate. Short-term variation in water availability and demand; long-term uncertainty in climate, groundwater storage, and sectoral competition for water; and varying stakeholder perspectives on the impacts of water shortages make it difficult to assess the necessity of expensive infrastructure investments. We categorize these uncertainties on two dimensions: whether they are the result of stochastic variation or epistemic uncertainty, and whether the uncertainties can be described probabilistically or are deep uncertainties whose likelihood is unknown. We develop a decision framework that combines simulation for probabilistic uncertainty, sensitivity analysis for deep uncertainty and Bayesian decision analysis for uncertainties that are reduced over time with additional information. We apply this framework to two contrasting case studies - drought preparedness in Melbourne, Australia and fossil groundwater depletion in Riyadh, Saudi Arabia - to assess the impacts of different types of uncertainty on infrastructure decisions. Melbourne's water supply system relies on surface water, which is impacted by natural variation in rainfall, and a market-based system for managing water rights. Our results show that small, flexible investment increases can mitigate shortage risk considerably at reduced cost. Riyadh, by contrast, relies primarily on desalination for municipal use and fossil groundwater for agriculture, and a centralized planner makes allocation decisions. Poor regional groundwater measurement makes it difficult to know when groundwater pumping will become uneconomical, resulting in epistemic uncertainty. However, collecting more data can reduce the uncertainty, suggesting the need for different uncertainty modeling and management strategies in Riyadh than in Melbourne. We will categorize the two systems and propose appropriate
On the performance of social network and likelihood-based expert weighting schemes
International Nuclear Information System (INIS)
Cooke, Roger M.; ElSaadany, Susie; Huang Xinzheng
2008-01-01
Using expert judgment data from the TU Delft's expert judgment database, we compare the performance of different weighting schemes, namely equal weighting, performance-based weighting from the classical model [Cooke RM. Experts in uncertainty. Oxford: Oxford University Press; 1991.], social network (SN) weighting and likelihood weighting. The picture that emerges with regard to SN weights is rather mixed. SN theory does not provide an alternative to performance-based combination of expert judgments, since the statistical accuracy of the SN decision maker is sometimes unacceptably low. On the other hand, it does outperform equal weighting in the majority of cases. The results here, though not overwhelmingly positive, do nonetheless motivate further research into social interaction methods for nominating and weighting experts. Indeed, a full expert judgment study with performance measurement requires an investment in time and effort, with a view to securing external validation. If high confidence in a comparable level of validation can be obtained by less intensive methods, this would be very welcome, and would facilitate the application of structured expert judgment in situations where the resources for a full study are not available. Likelihood weights are just as resource intensive as performance-based weights, and the evidence presented here suggests that they are inferior to performance-based weights with regard to those scoring variables which are optimized in performance weights (calibration and information). Perhaps surprisingly, they are also inferior with regard to likelihood. Their use is further discouraged by the fact that they constitute a strongly improper scoring rule
Epistemic uncertainties when estimating component failure rate
International Nuclear Information System (INIS)
Jordan Cizelj, R.; Mavko, B.; Kljenak, I.
2000-01-01
A method for specific estimation of a component failure rate, based on specific quantitative and qualitative data other than component failures, was developed and is described in the proposed paper. The basis of the method is the Bayesian updating procedure. A prior distribution is selected from a generic database, whereas likelihood is built using fuzzy logic theory. With the proposed method, the component failure rate estimation is based on a much larger quantity of information compared to the presently used classical methods. Consequently, epistemic uncertainties, which are caused by lack of knowledge about a component or phenomenon are reduced. (author)
International Nuclear Information System (INIS)
Kaul, Dean C.; Egbert, Stephen D.; Woolson, William A.
2005-01-01
In order to avoid the pitfalls that so discredited DS86 and its uncertainty estimates, and to provide DS02 uncertainties that are both defensible and credible, this report not only presents the ensemble uncertainties assembled from uncertainties in individual computational elements and radiation dose components but also describes how these relate to comparisons between observed and computed quantities at critical intervals in the computational process. These comparisons include those between observed and calculated radiation free-field components, where observations include thermal- and fast-neutron activation and gamma-ray thermoluminescence, which are relevant to the estimated systematic uncertainty for DS02. The comparisons also include those between calculated and observed survivor shielding, where the observations consist of biodosimetric measurements for individual survivors, which are relevant to the estimated random uncertainty for DS02. (J.P.N.)
Illingworth, Josephine L; Watson, Peter; Ring, Howard Anton
2014-01-01
Seizure precipitants are commonly reported in the general population of people with epilepsy. However, there has been little research in this area in people with epilepsy and intellectual disability (ID). We conducted a survey of the situations associated with increased or decreased seizure likelihood in this population. The aim of the research was to identify situations of increased seizure likelihood (SISLs) and situations of decreased seizure likelihood (SDSLs) reported by carers of people...
Brief communication: Drought likelihood for East Africa
Yang, Hui; Huntingford, Chris
2018-02-01
The East Africa drought in autumn of year 2016 caused malnutrition, illness and death. Close to 16 million people across Somalia, Ethiopia and Kenya needed food, water and medical assistance. Many factors influence drought stress and response. However, inevitably the following question is asked: are elevated greenhouse gas concentrations altering extreme rainfall deficit frequency? We investigate this with general circulation models (GCMs). After GCM bias correction to match the climatological mean of the CHIRPS data-based rainfall product, climate models project small decreases in probability of drought with the same (or worse) severity as 2016 ASO (August to October) East African event. This is by the end of the 21st century compared to the probabilities for present day. However, when further adjusting the climatological variability of GCMs to also match CHIRPS data, by additionally bias-correcting for variance, then the probability of drought occurrence will increase slightly over the same period.
Brief communication: Drought likelihood for East Africa
Directory of Open Access Journals (Sweden)
H. Yang
2018-02-01
Full Text Available The East Africa drought in autumn of year 2016 caused malnutrition, illness and death. Close to 16 million people across Somalia, Ethiopia and Kenya needed food, water and medical assistance. Many factors influence drought stress and response. However, inevitably the following question is asked: are elevated greenhouse gas concentrations altering extreme rainfall deficit frequency? We investigate this with general circulation models (GCMs. After GCM bias correction to match the climatological mean of the CHIRPS data-based rainfall product, climate models project small decreases in probability of drought with the same (or worse severity as 2016 ASO (August to October East African event. This is by the end of the 21st century compared to the probabilities for present day. However, when further adjusting the climatological variability of GCMs to also match CHIRPS data, by additionally bias-correcting for variance, then the probability of drought occurrence will increase slightly over the same period.
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
Efficient algorithms for maximum likelihood decoding in the surface code
Bravyi, Sergey; Suchara, Martin; Vargo, Alexander
2014-09-01
We describe two implementations of the optimal error correction algorithm known as the maximum likelihood decoder (MLD) for the two-dimensional surface code with a noiseless syndrome extraction. First, we show how to implement MLD exactly in time O (n2), where n is the number of code qubits. Our implementation uses a reduction from MLD to simulation of matchgate quantum circuits. This reduction however requires a special noise model with independent bit-flip and phase-flip errors. Secondly, we show how to implement MLD approximately for more general noise models using matrix product states (MPS). Our implementation has running time O (nχ3), where χ is a parameter that controls the approximation precision. The key step of our algorithm, borrowed from the density matrix renormalization-group method, is a subroutine for contracting a tensor network on the two-dimensional grid. The subroutine uses MPS with a bond dimension χ to approximate the sequence of tensors arising in the course of contraction. We benchmark the MPS-based decoder against the standard minimum weight matching decoder observing a significant reduction of the logical error probability for χ ≥4.
Uncertainty in artificial intelligence
Kanal, LN
1986-01-01
How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.
Uncertainties in hydrogen combustion
International Nuclear Information System (INIS)
Stamps, D.W.; Wong, C.C.; Nelson, L.S.
1988-01-01
Three important areas of hydrogen combustion with uncertainties are identified: high-temperature combustion, flame acceleration and deflagration-to-detonation transition, and aerosol resuspension during hydrogen combustion. The uncertainties associated with high-temperature combustion may affect at least three different accident scenarios: the in-cavity oxidation of combustible gases produced by core-concrete interactions, the direct containment heating hydrogen problem, and the possibility of local detonations. How these uncertainties may affect the sequence of various accident scenarios is discussed and recommendations are made to reduce these uncertainties. 40 references
Uncertainty relations, zero point energy and the linear canonical group
Sudarshan, E. C. G.
1993-01-01
The close relationship between the zero point energy, the uncertainty relations, coherent states, squeezed states, and correlated states for one mode is investigated. This group-theoretic perspective enables the parametrization and identification of their multimode generalization. In particular the generalized Schroedinger-Robertson uncertainty relations are analyzed. An elementary method of determining the canonical structure of the generalized correlated states is presented.
Sediment Curve Uncertainty Estimation Using GLUE and Bootstrap Methods
Directory of Open Access Journals (Sweden)
aboalhasan fathabadi
2017-02-01
Full Text Available Introduction: In order to implement watershed practices to decrease soil erosion effects it needs to estimate output sediment of watershed. Sediment rating curve is used as the most conventional tool to estimate sediment. Regarding to sampling errors and short data, there are some uncertainties in estimating sediment using sediment curve. In this research, bootstrap and the Generalized Likelihood Uncertainty Estimation (GLUE resampling techniques were used to calculate suspended sediment loads by using sediment rating curves. Materials and Methods: The total drainage area of the Sefidrood watershed is about 560000 km2. In this study uncertainty in suspended sediment rating curves was estimated in four stations including Motorkhane, Miyane Tonel Shomare 7, Stor and Glinak constructed on Ayghdamosh, Ghrangho, GHezelOzan and Shahrod rivers, respectively. Data were randomly divided into a training data set (80 percent and a test set (20 percent by Latin hypercube random sampling.Different suspended sediment rating curves equations were fitted to log-transformed values of sediment concentration and discharge and the best fit models were selected based on the lowest root mean square error (RMSE and the highest correlation of coefficient (R2. In the GLUE methodology, different parameter sets were sampled randomly from priori probability distribution. For each station using sampled parameter sets and selected suspended sediment rating curves equation suspended sediment concentration values were estimated several times (100000 to 400000 times. With respect to likelihood function and certain subjective threshold, parameter sets were divided into behavioral and non-behavioral parameter sets. Finally using behavioral parameter sets the 95% confidence intervals for suspended sediment concentration due to parameter uncertainty were estimated. In bootstrap methodology observed suspended sediment and discharge vectors were resampled with replacement B (set to
Parental family variables and likelihood of divorce.
Skalkidou, A
2000-01-01
It has long been established that divorced men and women have substantially higher standardized general mortality than same gender persons. Because the incidence of divorce is increasing in many countries, determinants of divorce rates assume great importance as indirect risk factors for several diseases and conditions that adversely affect health. We have undertaken a study in Athens, Greece, to evaluate whether sibship size, birth order, and the gender composition of spousal sibships are related to the probability of divorce. 358 high school students, aged between 15 and 17 years, satisfactorily completed anonymous questionnaires, indicating whether their natural parents have been separated or divorced, their parents' educational achievement, birth order and sibship size by gender. The study was analyzed as a twin case-control investigation, treating those divorced or separated as cases and those who were not divorced or separated as controls. A man who grew up as an only child was almost three times as likely to divorce compared to a man with siblings, and this association was highly significant (p approximately 0.004). There was no such evidence with respect to women. After controlling for sibship size, earlier born men--but not women--appeared to be at higher risk for divorce compared to those later born. There was no evidence that the gender structure of the sibship substantially affects the risk for divorce. Even though divorce is not an organic disease, it indirectly affects health as well as the social well-being. The findings of this study need to be replicated, but, if confirmed, they could contribute to our understanding of the roots of some instances of marital dysfunction.
Honti, Mark; Reichert, Peter; Scheidegger, Andreas; Stamm, Christian
2013-04-01
climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on 2 small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment method with 2 different likelihood functions. One was a time-series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was a likelihood function for the flow quantiles directly. Due to the better data coverage and smaller hydrological complexity in one of our test catchments we had better performance from the hydrological model and thus could observe that the relative importance of different uncertainty sources varied between sites, boundary conditions and flow indicators. The uncertainty of future climate was important, but not dominant. The deficiencies of the hydrological model were on the same scale, especially for the sites and flow components where model performance for the past observations was further from optimal (Nash-Sutcliffe index = 0.5 - 0.7). The overall uncertainty of predictions was well beyond the expected change signal even for the best performing site and flow indicator.
Uncertainty in social dilemmas
Kwaadsteniet, Erik Willem de
2007-01-01
This dissertation focuses on social dilemmas, and more specifically, on environmental uncertainty in these dilemmas. Real-life social dilemma situations are often characterized by uncertainty. For example, fishermen mostly do not know the exact size of the fish population (i.e., resource size
Uncertainty and Climate Change
Berliner, L. Mark
2003-01-01
Anthropogenic, or human-induced, climate change is a critical issue in science and in the affairs of humankind. Though the target of substantial research, the conclusions of climate change studies remain subject to numerous uncertainties. This article presents a very brief review of the basic arguments regarding anthropogenic climate change with particular emphasis on uncertainty.
Deterministic uncertainty analysis
International Nuclear Information System (INIS)
Worley, B.A.
1987-01-01
Uncertainties of computer results are of primary interest in applications such as high-level waste (HLW) repository performance assessment in which experimental validation is not possible or practical. This work presents an alternate deterministic approach for calculating uncertainties that has the potential to significantly reduce the number of computer runs required for conventional statistical analysis. 7 refs., 1 fig
International Nuclear Information System (INIS)
Depres, B.; Dossantos-Uzarralde, P.
2009-01-01
More than 150 researchers and engineers from universities and the industrial world met to discuss on the new methodologies developed around assessing uncertainty. About 20 papers were presented and the main topics were: methods to study the propagation of uncertainties, sensitivity analysis, nuclear data covariances or multi-parameter optimisation. This report gathers the contributions of CEA researchers and engineers
Uncertainty estimation of the velocity model for stations of the TrigNet GPS network
Hackl, M.; Malservisi, R.; Hugentobler, U.
2010-12-01
Satellite based geodetic techniques - above all GPS - provide an outstanding tool to measure crustal motions. They are widely used to derive geodetic velocity models that are applied in geodynamics to determine rotations of tectonic blocks, to localize active geological features, and to estimate rheological properties of the crust and the underlying asthenosphere. However, it is not a trivial task to derive GPS velocities and their uncertainties from positioning time series. In general time series are assumed to be represented by linear models (sometimes offsets, annual, and semi-annual signals are included) and noise. It has been shown that error models accounting only for white noise tend to underestimate the uncertainties of rates derived from long time series and that different colored noise components (flicker noise, random walk, etc.) need to be considered. However, a thorough error analysis including power spectra analyses and maximum likelihood estimates is computationally expensive and is usually not carried out for every site, but the uncertainties are scaled by latitude dependent factors. Analyses of the South Africa continuous GPS network TrigNet indicate that the scaled uncertainties overestimate the velocity errors. So we applied a method similar to the Allan Variance that is commonly used in the estimation of clock uncertainties and is able to account for time dependent probability density functions (colored noise) to the TrigNet time series. Comparisons with synthetic data show that the noise can be represented quite well by a power law model in combination with a seasonal signal in agreement with previous studies, which allows for a reliable estimation of the velocity error. Finally, we compared these estimates to the results obtained by spectral analyses using CATS. Small differences may originate from non-normal distribution of the noise.
Uncertainty estimation of the velocity model for the TrigNet GPS network
Hackl, Matthias; Malservisi, Rocco; Hugentobler, Urs; Wonnacott, Richard
2010-05-01
Satellite based geodetic techniques - above all GPS - provide an outstanding tool to measure crustal motions. They are widely used to derive geodetic velocity models that are applied in geodynamics to determine rotations of tectonic blocks, to localize active geological features, and to estimate rheological properties of the crust and the underlying asthenosphere. However, it is not a trivial task to derive GPS velocities and their uncertainties from positioning time series. In general time series are assumed to be represented by linear models (sometimes offsets, annual, and semi-annual signals are included) and noise. It has been shown that models accounting only for white noise tend to underestimate the uncertainties of rates derived from long time series and that different colored noise components (flicker noise, random walk, etc.) need to be considered. However, a thorough error analysis including power spectra analyses and maximum likelihood estimates is quite demanding and are usually not carried out for every site, but the uncertainties are scaled by latitude dependent factors. Analyses of the South Africa continuous GPS network TrigNet indicate that the scaled uncertainties overestimate the velocity errors. So we applied a method similar to the Allan Variance that is commonly used in the estimation of clock uncertainties and is able to account for time dependent probability density functions (colored noise) to the TrigNet time series. Finally, we compared these estimates to the results obtained by spectral analyses using CATS. Comparisons with synthetic data show that the noise can be represented quite well by a power law model in combination with a seasonal signal in agreement with previous studies.
Algorithms of maximum likelihood data clustering with applications
Giada, Lorenzo; Marsili, Matteo
2002-12-01
We address the problem of data clustering by introducing an unsupervised, parameter-free approach based on maximum likelihood principle. Starting from the observation that data sets belonging to the same cluster share a common information, we construct an expression for the likelihood of any possible cluster structure. The likelihood in turn depends only on the Pearson's coefficient of the data. We discuss clustering algorithms that provide a fast and reliable approximation to maximum likelihood configurations. Compared to standard clustering methods, our approach has the advantages that (i) it is parameter free, (ii) the number of clusters need not be fixed in advance and (iii) the interpretation of the results is transparent. In order to test our approach and compare it with standard clustering algorithms, we analyze two very different data sets: time series of financial market returns and gene expression data. We find that different maximization algorithms produce similar cluster structures whereas the outcome of standard algorithms has a much wider variability.
Maximum likelihood estimation of finite mixture model for economic data
Phoong, Seuk-Yen; Ismail, Mohd Tahir
2014-06-01
Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.
Attitude towards, and likelihood of, complaining in the banking ...
African Journals Online (AJOL)
aims to determine customers' attitudes towards complaining as well as their likelihood of voicing a .... is particularly powerful and impacts greatly on customer satisfaction and retention. ...... 'Cross-national analysis of hotel customers' attitudes ...
Narrow band interference cancelation in OFDM: Astructured maximum likelihood approach
Sohail, Muhammad Sadiq; Al-Naffouri, Tareq Y.; Al-Ghadhban, Samir N.
2012-01-01
This paper presents a maximum likelihood (ML) approach to mitigate the effect of narrow band interference (NBI) in a zero padded orthogonal frequency division multiplexing (ZP-OFDM) system. The NBI is assumed to be time variant and asynchronous
Uncertainty propagation in probabilistic risk assessment: A comparative study
International Nuclear Information System (INIS)
Ahmed, S.; Metcalf, D.R.; Pegram, J.W.
1982-01-01
Three uncertainty propagation techniques, namely method of moments, discrete probability distribution (DPD), and Monte Carlo simulation, generally used in probabilistic risk assessment, are compared and conclusions drawn in terms of the accuracy of the results. For small uncertainty in the basic event unavailabilities, the three methods give similar results. For large uncertainty, the method of moments is in error, and the appropriate method is to propagate uncertainty in the discrete form either by DPD method without sampling or by Monte Carlo. (orig.)
Physical Uncertainty Bounds (PUB)
Energy Technology Data Exchange (ETDEWEB)
Vaughan, Diane Elizabeth [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Preston, Dean L. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-03-19
This paper introduces and motivates the need for a new methodology for determining upper bounds on the uncertainties in simulations of engineered systems due to limited fidelity in the composite continuum-level physics models needed to simulate the systems. We show that traditional uncertainty quantification methods provide, at best, a lower bound on this uncertainty. We propose to obtain bounds on the simulation uncertainties by first determining bounds on the physical quantities or processes relevant to system performance. By bounding these physics processes, as opposed to carrying out statistical analyses of the parameter sets of specific physics models or simply switching out the available physics models, one can obtain upper bounds on the uncertainties in simulated quantities of interest.
Measurement uncertainty and probability
Willink, Robin
2013-01-01
A measurement result is incomplete without a statement of its 'uncertainty' or 'margin of error'. But what does this statement actually tell us? By examining the practical meaning of probability, this book discusses what is meant by a '95 percent interval of measurement uncertainty', and how such an interval can be calculated. The book argues that the concept of an unknown 'target value' is essential if probability is to be used as a tool for evaluating measurement uncertainty. It uses statistical concepts, such as a conditional confidence interval, to present 'extended' classical methods for evaluating measurement uncertainty. The use of the Monte Carlo principle for the simulation of experiments is described. Useful for researchers and graduate students, the book also discusses other philosophies relating to the evaluation of measurement uncertainty. It employs clear notation and language to avoid the confusion that exists in this controversial field of science.
Michalik, Thomas; Multsch, Sebastian; Frede, Hans-Georg; Breuer, Lutz
2016-04-01
Water for agriculture is strongly limited in arid and semi-arid regions and often of low quality in terms of salinity. The application of saline waters for irrigation increases the salt load in the rooting zone and has to be managed by leaching to maintain a healthy soil, i.e. to wash out salts by additional irrigation. Dynamic simulation models are helpful tools to calculate the root zone water fluxes and soil salinity content in order to investigate best management practices. However, there is little information on structural and parameter uncertainty for simulations regarding the water and salt balance of saline irrigation. Hence, we established a multi-model system with four different models (AquaCrop, RZWQM, SWAP, Hydrus1D/UNSATCHEM) to analyze the structural and parameter uncertainty by using the Global Likelihood and Uncertainty Estimation (GLUE) method. Hydrus1D/UNSATCHEM and SWAP were set up with multiple sets of different implemented functions (e.g. matric and osmotic stress for root water uptake) which results in a broad range of different model structures. The simulations were evaluated against soil water and salinity content observations. The posterior distribution of the GLUE analysis gives behavioral parameters sets and reveals uncertainty intervals for parameter uncertainty. Throughout all of the model sets, most parameters accounting for the soil water balance show a low uncertainty, only one or two out of five to six parameters in each model set displays a high uncertainty (e.g. pore-size distribution index in SWAP and Hydrus1D/UNSATCHEM). The differences between the models and model setups reveal the structural uncertainty. The highest structural uncertainty is observed for deep percolation fluxes between the model sets of Hydrus1D/UNSATCHEM (~200 mm) and RZWQM (~500 mm) that are more than twice as high for the latter. The model sets show a high variation in uncertainty intervals for deep percolation as well, with an interquartile range (IQR) of
Xue, Lianqing; Yang, Fan; Yang, Changbing; Wei, Guanghui; Li, Wenqian; He, Xinlin
2018-01-11
Understanding the mechanism of complicated hydrological processes is important for sustainable management of water resources in an arid area. This paper carried out the simulations of water movement for the Manas River Basin (MRB) using the improved semi-distributed Topographic hydrologic model (TOPMODEL) with a snowmelt model and topographic index algorithm. A new algorithm is proposed to calculate the curve of topographic index using internal tangent circle on a conical surface. Based on the traditional model, the improved indicator of temperature considered solar radiation is used to calculate the amount of snowmelt. The uncertainty of parameters for the TOPMODEL model was analyzed using the generalized likelihood uncertainty estimation (GLUE) method. The proposed model shows that the distribution of the topographic index is concentrated in high mountains, and the accuracy of runoff simulation has certain enhancement by considering radiation. Our results revealed that the performance of the improved TOPMODEL is acceptable and comparable to runoff simulation in the MRB. The uncertainty of the simulations resulted from the parameters and structures of model, climatic and anthropogenic factors. This study is expected to serve as a valuable complement for widely application of TOPMODEL and identify the mechanism of hydrological processes in arid area.
On the likelihood function of Gaussian max-stable processes
Genton, M. G.; Ma, Y.; Sang, H.
2011-01-01
We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study. © 2011 Biometrika Trust.
On the likelihood function of Gaussian max-stable processes
Genton, M. G.
2011-05-24
We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by ℝd at p≤d+1 sites, d≥1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in ℝ2 by means of a Monte Carlo simulation study. © 2011 Biometrika Trust.
Tapered composite likelihood for spatial max-stable models
Sang, Huiyan
2014-05-01
Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.
Dissociating response conflict and error likelihood in anterior cingulate cortex.
Yeung, Nick; Nieuwenhuis, Sander
2009-11-18
Neuroimaging studies consistently report activity in anterior cingulate cortex (ACC) in conditions of high cognitive demand, leading to the view that ACC plays a crucial role in the control of cognitive processes. According to one prominent theory, the sensitivity of ACC to task difficulty reflects its role in monitoring for the occurrence of competition, or "conflict," between responses to signal the need for increased cognitive control. However, a contrasting theory proposes that ACC is the recipient rather than source of monitoring signals, and that ACC activity observed in relation to task demand reflects the role of this region in learning about the likelihood of errors. Response conflict and error likelihood are typically confounded, making the theories difficult to distinguish empirically. The present research therefore used detailed computational simulations to derive contrasting predictions regarding ACC activity and error rate as a function of response speed. The simulations demonstrated a clear dissociation between conflict and error likelihood: fast response trials are associated with low conflict but high error likelihood, whereas slow response trials show the opposite pattern. Using the N2 component as an index of ACC activity, an EEG study demonstrated that when conflict and error likelihood are dissociated in this way, ACC activity tracks conflict and is negatively correlated with error likelihood. These findings support the conflict-monitoring theory and suggest that, in speeded decision tasks, ACC activity reflects current task demands rather than the retrospective coding of past performance.
Tapered composite likelihood for spatial max-stable models
Sang, Huiyan; Genton, Marc G.
2014-01-01
Spatial extreme value analysis is useful to environmental studies, in which extreme value phenomena are of interest and meaningful spatial patterns can be discerned. Max-stable process models are able to describe such phenomena. This class of models is asymptotically justified to characterize the spatial dependence among extremes. However, likelihood inference is challenging for such models because their corresponding joint likelihood is unavailable and only bivariate or trivariate distributions are known. In this paper, we propose a tapered composite likelihood approach by utilizing lower dimensional marginal likelihoods for inference on parameters of various max-stable process models. We consider a weighting strategy based on a "taper range" to exclude distant pairs or triples. The "optimal taper range" is selected to maximize various measures of the Godambe information associated with the tapered composite likelihood function. This method substantially reduces the computational cost and improves the efficiency over equally weighted composite likelihood estimators. We illustrate its utility with simulation experiments and an analysis of rainfall data in Switzerland.
Uncertainty in hydrological change modelling
DEFF Research Database (Denmark)
Seaby, Lauren Paige
applied at the grid scale. Flux and state hydrological outputs which integrate responses over time and space showed more sensitivity to precipitation mean spatial biases and less so on extremes. In the investigated catchments, the projected change of groundwater levels and basin discharge between current......Hydrological change modelling methodologies generally use climate models outputs to force hydrological simulations under changed conditions. There are nested sources of uncertainty throughout this methodology, including choice of climate model and subsequent bias correction methods. This Ph.......D. study evaluates the uncertainty of the impact of climate change in hydrological simulations given multiple climate models and bias correction methods of varying complexity. Three distribution based scaling methods (DBS) were developed and benchmarked against a more simplistic and commonly used delta...
International Nuclear Information System (INIS)
Lu, W L; Jiang, X; Liu, X J; Xu, Z G
2008-01-01
Compliance uncertainty is one of the most important elements in the next-generation geometrical product specifications and verification (GPS). It consists of specification uncertainty, method uncertainty and implementation uncertainty, which are three of the four fundamental uncertainties in the next-generation GPS. This paper analyzes the key factors that influence compliance uncertainty and then proposes a procedure to manage the compliance uncertainty. A general model on evaluation of compliance uncertainty has been devised and a specific formula for diameter characteristic has been derived based on this general model. The case study was conducted and it revealed that the completeness of currently dominant diameter characteristic specification needs to be improved
Uncertainties in Safety Analysis. A literature review
International Nuclear Information System (INIS)
Ekberg, C.
1995-05-01
The purpose of the presented work has been to give a short summary of the origins of many uncertainties arising in the designing and performance assessment of a repository for spent nuclear fuel. Some different methods to treat these uncertainties is also included. The methods and conclusions are in many cases general in the sense that they are applicable to many other disciplines where simulations are used. As a conclusion it may be noted that uncertainties of different origin have been discussed and debated, but one large group, e.g. computer simulations, where the methods to make a more explicit investigation exists, have not been investigated in a satisfying way. 50 refs
Uncertainties in Safety Analysis. A literature review
Energy Technology Data Exchange (ETDEWEB)
Ekberg, C [Chalmers Univ. of Technology, Goeteborg (Sweden). Dept. of Nuclear Chemistry
1995-05-01
The purpose of the presented work has been to give a short summary of the origins of many uncertainties arising in the designing and performance assessment of a repository for spent nuclear fuel. Some different methods to treat these uncertainties is also included. The methods and conclusions are in many cases general in the sense that they are applicable to many other disciplines where simulations are used. As a conclusion it may be noted that uncertainties of different origin have been discussed and debated, but one large group, e.g. computer simulations, where the methods to make a more explicit investigation exists, have not been investigated in a satisfying way. 50 refs.
McGuire, Connor; Kristman, Vicki L; Shaw, William; Williams-Whitt, Kelly; Reguly, Paula; Soklaridis, Sophie
2015-09-01
To determine the association between supervisors' leadership style and autonomy and supervisors' likelihood of supporting job accommodations for back-injured workers. A cross-sectional study of supervisors from Canadian and US employers was conducted using a web-based, self-report questionnaire that included a case vignette of a back-injured worker. Autonomy and two dimensions of leadership style (considerate and initiating structure) were included as exposures. The outcome, supervisors' likeliness to support job accommodation, was measured with the Job Accommodation Scale (JAS). We conducted univariate analyses of all variables and bivariate analyses of the JAS score with each exposure and potential confounding factor. We used multivariable generalized linear models to control for confounding factors. A total of 796 supervisors participated. Considerate leadership style (β = .012; 95% CI .009-.016) and autonomy (β = .066; 95% CI .025-.11) were positively associated with supervisors' likelihood to accommodate after adjusting for appropriate confounding factors. An initiating structure leadership style was not significantly associated with supervisors' likelihood to accommodate (β = .0018; 95% CI -.0026 to .0061) after adjusting for appropriate confounders. Autonomy and a considerate leadership style were positively associated with supervisors' likelihood to accommodate a back-injured worker. Providing supervisors with more autonomy over decisions of accommodation and developing their considerate leadership style may aid in increasing work accommodation for back-injured workers and preventing prolonged work disability.
McGuire, Connor; Kristman, Vicki L; Williams-Whitt, Kelly; Reguly, Paula; Shaw, William; Soklaridis, Sophie
2015-01-01
PURPOSE To determine the association between supervisors’ leadership style and autonomy and supervisors’ likelihood of supporting job accommodations for back-injured workers. METHODS A cross-sectional study of supervisors from Canadian and US employers was conducted using a web-based, self-report questionnaire that included a case vignette of a back-injured worker. Autonomy and two dimensions of leadership style (considerate and initiating structure) were included as exposures. The outcome, supervisors’ likeliness to support job accommodation, was measured with the Job Accommodation Scale. We conducted univariate analyses of all variables and bivariate analyses of the JAS score with each exposure and potential confounding factor. We used multivariable generalized linear models to control for confounding factors. RESULTS A total of 796 supervisors participated. Considerate leadership style (β= .012; 95% CI: .009–.016) and autonomy (β= .066; 95% CI: .025–.11) were positively associated with supervisors’ likelihood to accommodate after adjusting for appropriate confounding factors. An initiating structure leadership style was not significantly associated with supervisors’ likelihood to accommodate (β = .0018; 95% CI: −.0026–.0061) after adjusting for appropriate confounders. CONCLUSIONS Autonomy and a considerate leadership style were positively associated with supervisors’ likelihood to accommodate a back-injured worker. Providing supervisors with more autonomy over decisions of accommodation and developing their considerate leadership style may aid in increasing work accommodation for back-injured workers and preventing prolonged work disability. PMID:25595332
Validation of software for calculating the likelihood ratio for parentage and kinship.
Drábek, J
2009-03-01
Although the likelihood ratio is a well-known statistical technique, commercial off-the-shelf (COTS) software products for its calculation are not sufficiently validated to suit general requirements for the competence of testing and calibration laboratories (EN/ISO/IEC 17025:2005 norm) per se. The software in question can be considered critical as it directly weighs the forensic evidence allowing judges to decide on guilt or innocence or to identify person or kin (i.e.: in mass fatalities). For these reasons, accredited laboratories shall validate likelihood ratio software in accordance with the above norm. To validate software for calculating the likelihood ratio in parentage/kinship scenarios I assessed available vendors, chose two programs (Paternity Index and familias) for testing, and finally validated them using tests derived from elaboration of the available guidelines for the field of forensics, biomedicine, and software engineering. MS Excel calculation using known likelihood ratio formulas or peer-reviewed results of difficult paternity cases were used as a reference. Using seven testing cases, it was found that both programs satisfied the requirements for basic paternity cases. However, only a combination of two software programs fulfills the criteria needed for our purpose in the whole spectrum of functions under validation with the exceptions of providing algebraic formulas in cases of mutation and/or silent allele.
Validation of DNA-based identification software by computation of pedigree likelihood ratios.
Slooten, K
2011-08-01
Disaster victim identification (DVI) can be aided by DNA-evidence, by comparing the DNA-profiles of unidentified individuals with those of surviving relatives. The DNA-evidence is used optimally when such a comparison is done by calculating the appropriate likelihood ratios. Though conceptually simple, the calculations can be quite involved, especially with large pedigrees, precise mutation models etc. In this article we describe a series of test cases designed to check if software designed to calculate such likelihood ratios computes them correctly. The cases include both simple and more complicated pedigrees, among which inbred ones. We show how to calculate the likelihood ratio numerically and algebraically, including a general mutation model and possibility of allelic dropout. In Appendix A we show how to derive such algebraic expressions mathematically. We have set up these cases to validate new software, called Bonaparte, which performs pedigree likelihood ratio calculations in a DVI context. Bonaparte has been developed by SNN Nijmegen (The Netherlands) for the Netherlands Forensic Institute (NFI). It is available free of charge for non-commercial purposes (see www.dnadvi.nl for details). Commercial licenses can also be obtained. The software uses Bayesian networks and the junction tree algorithm to perform its calculations. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.
A Game Theoretical Approach to Hacktivism: Is Attack Likelihood a Product of Risks and Payoffs?
Bodford, Jessica E; Kwan, Virginia S Y
2018-02-01
The current study examines hacktivism (i.e., hacking to convey a moral, ethical, or social justice message) through a general game theoretic framework-that is, as a product of costs and benefits. Given the inherent risk of carrying out a hacktivist attack (e.g., legal action, imprisonment), it would be rational for the user to weigh these risks against perceived benefits of carrying out the attack. As such, we examined computer science students' estimations of risks, payoffs, and attack likelihood through a game theoretic design. Furthermore, this study aims at constructing a descriptive profile of potential hacktivists, exploring two predicted covariates of attack decision making, namely, peer prevalence of hacking and sex differences. Contrary to expectations, results suggest that participants' estimations of attack likelihood stemmed solely from expected payoffs, rather than subjective risks. Peer prevalence significantly predicted increased payoffs and attack likelihood, suggesting an underlying descriptive norm in social networks. Notably, we observed no sex differences in the decision to attack, nor in the factors predicting attack likelihood. Implications for policymakers and the understanding and prevention of hacktivism are discussed, as are the possible ramifications of widely communicated payoffs over potential risks in hacking communities.
Assessment of uncertainties in Neutron Multiplicity Counting
International Nuclear Information System (INIS)
Peerani, P.; Marin Ferrer, M.
2008-01-01
This paper describes a methodology for a complete and correct assessment of the errors coming from the uncertainty of each individual component on the final result. A general methodology accounting for all the main sources of error (both type-A and type-B) will be outlined. In order to better illustrate the method, a practical example applying it to the uncertainty estimation for a special case of multiplicity counter, the SNMC developed at JRC, will be given
Uncertainty and complementarity in axiomatic quantum mechanics
International Nuclear Information System (INIS)
Lahti, P.J.
1980-01-01
An investigation of the uncertainty principle and the complementarity principle is carried through. The physical content of these principles and their representation in the conventional Hilbert space formulation of quantum mechanics forms a natural starting point. Thereafter is presented more general axiomatic framework for quantum mechanics, namely, a probability function formulation of the theory. Two extra axioms are stated, reflecting the ideas of the uncertainty principle and the complementarity principle, respectively. The quantal features of these axioms are explicated. (author)
Uncertainty Propagation in OMFIT
Smith, Sterling; Meneghini, Orso; Sung, Choongki
2017-10-01
A rigorous comparison of power balance fluxes and turbulent model fluxes requires the propagation of uncertainties in the kinetic profiles and their derivatives. Making extensive use of the python uncertainties package, the OMFIT framework has been used to propagate covariant uncertainties to provide an uncertainty in the power balance calculation from the ONETWO code, as well as through the turbulent fluxes calculated by the TGLF code. The covariant uncertainties arise from fitting 1D (constant on flux surface) density and temperature profiles and associated random errors with parameterized functions such as a modified tanh. The power balance and model fluxes can then be compared with quantification of the uncertainties. No effort is made at propagating systematic errors. A case study will be shown for the effects of resonant magnetic perturbations on the kinetic profiles and fluxes at the top of the pedestal. A separate attempt at modeling the random errors with Monte Carlo sampling will be compared to the method of propagating the fitting function parameter covariant uncertainties. Work supported by US DOE under DE-FC02-04ER54698, DE-FG2-95ER-54309, DE-SC 0012656.
Verification of uncertainty budgets
DEFF Research Database (Denmark)
Heydorn, Kaj; Madsen, B.S.
2005-01-01
, and therefore it is essential that the applicability of the overall uncertainty budget to actual measurement results be verified on the basis of current experimental data. This should be carried out by replicate analysis of samples taken in accordance with the definition of the measurand, but representing...... the full range of matrices and concentrations for which the budget is assumed to be valid. In this way the assumptions made in the uncertainty budget can be experimentally verified, both as regards sources of variability that are assumed negligible, and dominant uncertainty components. Agreement between...
Treatment of uncertainties in the IPCC: a philosophical analysis
Jebeile, J.; Drouet, I.
2014-12-01
The IPCC produces scientific reports out of findings on climate and climate change. Because the findings are uncertain in many respects, the production of reports requires aggregating assessments of uncertainties of different kinds. This difficult task is currently regulated by the Guidance note for lead authors of the IPCC fifth assessment report on consistent treatment of uncertainties. The note recommends that two metrics—i.e. confidence and likelihood— be used for communicating the degree of certainty in findings. Confidence is expressed qualitatively "based on the type, amount, quality, and consistency of evidence […] and the degree of agreement", while likelihood is expressed probabilistically "based on statistical analysis of observations or model results, or expert judgment". Therefore, depending on the evidence evaluated, authors have the choice to present either an assigned level of confidence or a quantified measure of likelihood. But aggregating assessments of uncertainties of these two different kinds express distinct and conflicting methodologies. So the question arises whether the treatment of uncertainties in the IPCC is rationally justified. In order to answer the question, it is worth comparing the IPCC procedures with the formal normative theories of epistemic rationality which have been developed by philosophers. These theories—which include contributions to the philosophy of probability and to bayesian probabilistic confirmation theory—are relevant for our purpose because they are commonly used to assess the rationality of common collective jugement formation based on uncertain knowledge. In this paper we make the comparison and pursue the following objectives: i/we determine whether the IPCC confidence and likelihood can be compared with the notions of uncertainty targeted by or underlying the formal normative theories of epistemic rationality; ii/we investigate whether the formal normative theories of epistemic rationality justify
Evaluating prediction uncertainty
International Nuclear Information System (INIS)
McKay, M.D.
1995-03-01
The probability distribution of a model prediction is presented as a proper basis for evaluating the uncertainty in a model prediction that arises from uncertainty in input values. Determination of important model inputs and subsets of inputs is made through comparison of the prediction distribution with conditional prediction probability distributions. Replicated Latin hypercube sampling and variance ratios are used in estimation of the distributions and in construction of importance indicators. The assumption of a linear relation between model output and inputs is not necessary for the indicators to be effective. A sequential methodology which includes an independent validation step is applied in two analysis applications to select subsets of input variables which are the dominant causes of uncertainty in the model predictions. Comparison with results from methods which assume linearity shows how those methods may fail. Finally, suggestions for treating structural uncertainty for submodels are presented
International Nuclear Information System (INIS)
Limperopoulos, G.J.
1995-01-01
This report presents an oil project valuation under uncertainty by means of two well-known financial techniques: The Capital Asset Pricing Model (CAPM) and The Black-Scholes Option Pricing Formula. CAPM gives a linear positive relationship between expected rate of return and risk but does not take into consideration the aspect of flexibility which is crucial for an irreversible investment as an oil price is. Introduction of investment decision flexibility by using real options can increase the oil project value substantially. Some simple tests for the importance of uncertainty in stock market for oil investments are performed. Uncertainty in stock returns is correlated with aggregate product market uncertainty according to Pindyck (1991). The results of the tests are not satisfactory due to the short data series but introducing two other explanatory variables the interest rate and Gross Domestic Product make the situation better. 36 refs., 18 figs., 6 tabs
Uncertainties and climatic change
International Nuclear Information System (INIS)
De Gier, A.M.; Opschoor, J.B.; Van de Donk, W.B.H.J.; Hooimeijer, P.; Jepma, J.; Lelieveld, J.; Oerlemans, J.; Petersen, A.
2008-01-01
Which processes in the climate system are misunderstood? How are scientists dealing with uncertainty about climate change? What will be done with the conclusions of the recently published synthesis report of the IPCC? These and other questions were answered during the meeting 'Uncertainties and climate change' that was held on Monday 26 November 2007 at the KNAW in Amsterdam. This report is a compilation of all the presentations and provides some conclusions resulting from the discussions during this meeting. [mk] [nl
Lemaire, Maurice
2014-01-01
Science is a quest for certainty, but lack of certainty is the driving force behind all of its endeavors. This book, specifically, examines the uncertainty of technological and industrial science. Uncertainty and Mechanics studies the concepts of mechanical design in an uncertain setting and explains engineering techniques for inventing cost-effective products. Though it references practical applications, this is a book about ideas and potential advances in mechanical science.
Uncertainty: lotteries and risk
Ávalos, Eloy
2011-01-01
In this paper we develop the theory of uncertainty in a context where the risks assumed by the individual are measurable and manageable. We primarily use the definition of lottery to formulate the axioms of the individual's preferences, and its representation through the utility function von Neumann - Morgenstern. We study the expected utility theorem and its properties, the paradoxes of choice under uncertainty and finally the measures of risk aversion with monetary lotteries.
Uncertainty calculations made easier
International Nuclear Information System (INIS)
Hogenbirk, A.
1994-07-01
The results are presented of a neutron cross section sensitivity/uncertainty analysis performed in a complicated 2D model of the NET shielding blanket design inside the ITER torus design, surrounded by the cryostat/biological shield as planned for ITER. The calculations were performed with a code system developed at ECN Petten, with which sensitivity/uncertainty calculations become relatively simple. In order to check the deterministic neutron transport calculations (performed with DORT), calculations were also performed with the Monte Carlo code MCNP. Care was taken to model the 2.0 cm wide gaps between two blanket segments, as the neutron flux behind the vacuum vessel is largely determined by neutrons streaming through these gaps. The resulting neutron flux spectra are in excellent agreement up to the end of the cryostat. It is noted, that at this position the attenuation of the neutron flux is about 1 l orders of magnitude. The uncertainty in the energy integrated flux at the beginning of the vacuum vessel and at the beginning of the cryostat was determined in the calculations. The uncertainty appears to be strongly dependent on the exact geometry: if the gaps are filled with stainless steel, the neutron spectrum changes strongly, which results in an uncertainty of 70% in the energy integrated flux at the beginning of the cryostat in the no-gap-geometry, compared to an uncertainty of only 5% in the gap-geometry. Therefore, it is essential to take into account the exact geometry in sensitivity/uncertainty calculations. Furthermore, this study shows that an improvement of the covariance data is urgently needed in order to obtain reliable estimates of the uncertainties in response parameters in neutron transport calculations. (orig./GL)
Constraint likelihood analysis for a network of gravitational wave detectors
International Nuclear Information System (INIS)
Klimenko, S.; Rakhmanov, M.; Mitselmakher, G.; Mohanty, S.
2005-01-01
We propose a coherent method for detection and reconstruction of gravitational wave signals with a network of interferometric detectors. The method is derived by using the likelihood ratio functional for unknown signal waveforms. In the likelihood analysis, the global maximum of the likelihood ratio over the space of waveforms is used as the detection statistic. We identify a problem with this approach. In the case of an aligned pair of detectors, the detection statistic depends on the cross correlation between the detectors as expected, but this dependence disappears even for infinitesimally small misalignments. We solve the problem by applying constraints on the likelihood functional and obtain a new class of statistics. The resulting method can be applied to data from a network consisting of any number of detectors with arbitrary detector orientations. The method allows us reconstruction of the source coordinates and the waveforms of two polarization components of a gravitational wave. We study the performance of the method with numerical simulations and find the reconstruction of the source coordinates to be more accurate than in the standard likelihood method
Role of information theoretic uncertainty relations in quantum theory
Energy Technology Data Exchange (ETDEWEB)
Jizba, Petr, E-mail: p.jizba@fjfi.cvut.cz [FNSPE, Czech Technical University in Prague, Břehová 7, 115 19 Praha 1 (Czech Republic); ITP, Freie Universität Berlin, Arnimallee 14, D-14195 Berlin (Germany); Dunningham, Jacob A., E-mail: J.Dunningham@sussex.ac.uk [Department of Physics and Astronomy, University of Sussex, Falmer, Brighton, BN1 9QH (United Kingdom); Joo, Jaewoo, E-mail: j.joo@surrey.ac.uk [Advanced Technology Institute and Department of Physics, University of Surrey, Guildford, GU2 7XH (United Kingdom)
2015-04-15
Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson–Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again, improvement over both the Robertson–Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed.
Role of information theoretic uncertainty relations in quantum theory
International Nuclear Information System (INIS)
Jizba, Petr; Dunningham, Jacob A.; Joo, Jaewoo
2015-01-01
Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson–Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again, improvement over both the Robertson–Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed
Treatment of uncertainties in the geologic disposal of radioactive waste
International Nuclear Information System (INIS)
Cranwell, R.M.
1985-01-01
Uncertainty in the analysis of geologic waste disposal is generally considered to have three primary components: (1) computer code/model uncertainty, (2) model parameter uncertainty, and (3) scenario uncertainty. Computer code/model uncertainty arises from problems associated with determination of appropriate parameters for use in model construction, mathematical formulatin of models, and numerical techniques used in conjunction with the mathematical formulation of models. Model parameter uncertainty arises from problems associated with selection of appropriate values for model input, data interpretation and possible misuse of data, and variation of data. Scenario uncertainty arises from problems associated with the ''completeness' of scenarios, the definition of parameters which describe scenarios, and the rate or probability of scenario occurrence. The preceding sources of uncertainty are discussed below
Chemical model reduction under uncertainty
Malpica Galassi, Riccardo
2017-03-06
A general strategy for analysis and reduction of uncertain chemical kinetic models is presented, and its utility is illustrated in the context of ignition of hydrocarbon fuel–air mixtures. The strategy is based on a deterministic analysis and reduction method which employs computational singular perturbation analysis to generate simplified kinetic mechanisms, starting from a detailed reference mechanism. We model uncertain quantities in the reference mechanism, namely the Arrhenius rate parameters, as random variables with prescribed uncertainty factors. We propagate this uncertainty to obtain the probability of inclusion of each reaction in the simplified mechanism. We propose probabilistic error measures to compare predictions from the uncertain reference and simplified models, based on the comparison of the uncertain dynamics of the state variables, where the mixture entropy is chosen as progress variable. We employ the construction for the simplification of an uncertain mechanism in an n-butane–air mixture homogeneous ignition case, where a 176-species, 1111-reactions detailed kinetic model for the oxidation of n-butane is used with uncertainty factors assigned to each Arrhenius rate pre-exponential coefficient. This illustration is employed to highlight the utility of the construction, and the performance of a family of simplified models produced depending on chosen thresholds on importance and marginal probabilities of the reactions.
Directory of Open Access Journals (Sweden)
Fu Yuhua
2015-03-01
Full Text Available The most famous contribution of Heisenberg is uncertainty principle. But the original uncertainty principle is improper. Considering all the possible situations (including the case that people can create laws and applying Neutrosophy and Quad-stage Method, this paper presents "certainty-uncertainty principles" with general form and variable dimension fractal form. According to the classification of Neutrosophy, "certainty-uncertainty principles" can be divided into three principles in different conditions: "certainty principle", namely a particle’s position and momentum can be known simultaneously; "uncertainty principle", namely a particle’s position and momentum cannot be known simultaneously; and neutral (fuzzy "indeterminacy principle", namely whether or not a particle’s position and momentum can be known simultaneously is undetermined. The special cases of "certain ty-uncertainty principles" include the original uncertainty principle and Ozawa inequality. In addition, in accordance with the original uncertainty principle, discussing high-speed particle’s speed and track with Newton mechanics is unreasonable; but according to "certaintyuncertainty principles", Newton mechanics can be used to discuss the problem of gravitational defection of a photon orbit around the Sun (it gives the same result of deflection angle as given by general relativity. Finally, for the reason that in physics the principles, laws and the like that are regardless of the principle (law of conservation of energy may be invalid; therefore "certaintyuncertainty principles" should be restricted (or constrained by principle (law of conservation of energy, and thus it can satisfy the principle (law of conservation of energy.
Quantum Uncertainty and Fundamental Interactions
Directory of Open Access Journals (Sweden)
Tosto S.
2013-04-01
Full Text Available The paper proposes a simplified theoretical approach to infer some essential concepts on the fundamental interactions between charged particles and their relative strengths at comparable energies by exploiting the quantum uncertainty only. The worth of the present approach relies on the way of obtaining the results, rather than on the results themselves: concepts today acknowledged as fingerprints of the electroweak and strong interactions appear indeed rooted in the same theoretical frame including also the basic principles of special and general relativity along with the gravity force.
Justification for recommended uncertainties
International Nuclear Information System (INIS)
Pronyaev, V.G.; Badikov, S.A.; Carlson, A.D.
2007-01-01
The uncertainties obtained in an earlier standards evaluation were considered to be unrealistically low by experts of the US Cross Section Evaluation Working Group (CSEWG). Therefore, the CSEWG Standards Subcommittee replaced the covariance matrices of evaluated uncertainties by expanded percentage errors that were assigned to the data over wide energy groups. There are a number of reasons that might lead to low uncertainties of the evaluated data: Underestimation of the correlations existing between the results of different measurements; The presence of unrecognized systematic uncertainties in the experimental data can lead to biases in the evaluated data as well as to underestimations of the resulting uncertainties; Uncertainties for correlated data cannot only be characterized by percentage uncertainties or variances. Covariances between evaluated value at 0.2 MeV and other points obtained in model (RAC R matrix and PADE2 analytical expansion) and non-model (GMA) fits of the 6 Li(n,t) TEST1 data and the correlation coefficients are presented and covariances between the evaluated value at 0.045 MeV and other points (along the line or column of the matrix) as obtained in EDA and RAC R matrix fits of the data available for reactions that pass through the formation of the 7 Li system are discussed. The GMA fit with the GMA database is shown for comparison. The following diagrams are discussed: Percentage uncertainties of the evaluated cross section for the 6 Li(n,t) reaction and the for the 235 U(n,f) reaction; estimation given by CSEWG experts; GMA result with full GMA database, including experimental data for the 6 Li(n,t), 6 Li(n,n) and 6 Li(n,total) reactions; uncertainties in the GMA combined fit for the standards; EDA and RAC R matrix results, respectively. Uncertainties of absolute and 252 Cf fission spectrum averaged cross section measurements, and deviations between measured and evaluated values for 235 U(n,f) cross-sections in the neutron energy range 1
Profile-likelihood Confidence Intervals in Item Response Theory Models.
Chalmers, R Philip; Pek, Jolynn; Liu, Yang
2017-01-01
Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.
Lognormal Approximations of Fault Tree Uncertainty Distributions.
El-Shanawany, Ashraf Ben; Ardron, Keith H; Walker, Simon P
2018-01-26
Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed-form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling-based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed-form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks's method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models. © 2018 Society for Risk Analysis.
INTEGRATION OF SYSTEM COMPONENTS AND UNCERTAINTY ANALYSIS - HANFORD EXAMPLES
International Nuclear Information System (INIS)
Wood, M.I.
2009-01-01
(sm b ullet) Deterministic 'One Off' analyses as basis for evaluating sensitivity and uncertainty relative to reference case (sm b ullet) Spatial coverage identical to reference case (sm b ullet) Two types of analysis assumptions - Minimax parameter values around reference case conditions - 'What If' cases that change reference case condition and associated parameter values (sm b ullet) No conclusions about likelihood of estimated result other than' qualitative expectation that actual outcome should tend toward reference case estimate
arXiv FlavBit: A GAMBIT module for computing flavour observables and likelihoods
Bernlochner, Florian U.; Dal, Lars A.; Farmer, Ben; Jackson, Paul; Kvellestad, Anders; Mahmoudi, Farvah; Putze, Antje; Rogan, Christopher; Scott, Pat; Serra, Nicola; Weniger, Christoph; White, Martin
2017-11-21
Flavour physics observables are excellent probes of new physics up to very high energy scales. Here we present FlavBit, the dedicated flavour physics module of the global-fitting package GAMBIT. FlavBit includes custom implementations of various likelihood routines for a wide range of flavour observables, including detailed uncertainties and correlations associated with LHCb measurements of rare, leptonic and semileptonic decays of B and D mesons, kaons and pions. It provides a generalised interface to external theory codes such as SuperIso, allowing users to calculate flavour observables in and beyond the Standard Model, and then test them in detail against all relevant experimental data. We describe FlavBit and its constituent physics in some detail, then give examples from supersymmetry and effective field theory illustrating how it can be used both as a standalone library for flavour physics, and within GAMBIT.
FlavBit. A GAMBIT module for computing flavour observables and likelihoods
Energy Technology Data Exchange (ETDEWEB)
Bernlochner, Florian U. [Physikalisches Institut der Rheinischen Friedrich-Wilhelms-Universitaet Bonn (Germany); Chrzaszcz, Marcin [Universitaet Zuerich, Physik-Institut, Zurich (Switzerland); Polish Academy of Sciences, H. Niewodniczanski Institute of Nuclear Physics, Krakow (Poland); Dal, Lars A. [University of Oslo, Department of Physics, Oslo (Norway); Farmer, Ben [Oskar Klein Centre for Cosmoparticle Physics, AlbaNova University Centre, Stockholm (Sweden); Stockholm University, Department of Physics, Stockholm (Sweden); Jackson, Paul; White, Martin [University of Adelaide, Department of Physics, Adelaide, SA (Australia); Australian Research Council Centre of Excellence for Particle Physics at the Tera-scale (Australia); Kvellestad, Anders [NORDITA, Stockholm (Sweden); Mahmoudi, Farvah [Univ Lyon, Univ Lyon 1, ENS de Lyon, CNRS, Centre de Recherche Astrophysique de Lyon UMR5574, Saint-Genis-Laval (France); CERN, Theoretical Physics Department, Geneva (Switzerland); Putze, Antje [LAPTh, Universite de Savoie, CNRS, Annecy-le-Vieux (France); Rogan, Christopher [Harvard University, Department of Physics, Cambridge, MA (United States); Scott, Pat [Imperial College London, Department of Physics, Blackett Laboratory, London (United Kingdom); Serra, Nicola [Universitaet Zuerich, Physik-Institut, Zurich (Switzerland); Weniger, Christoph [University of Amsterdam, GRAPPA, Institute of Physics, Amsterdam (Netherlands); Collaboration: The GAMBIT Flavour Workgroup
2017-11-15
Flavour physics observables are excellent probes of new physics up to very high energy scales. Here we present FlavBit, the dedicated flavour physics module of the global-fitting package GAMBIT. FlavBit includes custom implementations of various likelihood routines for a wide range of flavour observables, including detailed uncertainties and correlations associated with LHCb measurements of rare, leptonic and semileptonic decays of B and D mesons, kaons and pions. It provides a generalised interface to external theory codes such as SuperIso, allowing users to calculate flavour observables in and beyond the Standard Model, and then test them in detail against all relevant experimental data. We describe FlavBit and its constituent physics in some detail, then give examples from supersymmetry and effective field theory illustrating how it can be used both as a standalone library for flavour physics, and within GAMBIT. (orig.)
Modelling of risk events with uncertain likelihoods and impacts in large infrastructure projects
DEFF Research Database (Denmark)
Schjær-Jacobsen, Hans
2010-01-01
to prevent future budget overruns. One of the central ideas is to introduce improved risk management processes and the present paper addresses this particular issue. A relevant cost function in terms of unit prices and quantities is developed and an event impact matrix with uncertain impacts from independent......This paper presents contributions to the mathematical core of risk and uncertainty management in compliance with the principles of New Budgeting laid out in 2008 by the Danish Ministry of Transport to be used in large infrastructure projects. Basically, the new principles are proposed in order...... uncertain risk events is used to calculate the total uncertain risk budget. Cost impacts from the individual risk events on the individual project activities are kept precisely track of in order to comply with the requirements of New Budgeting. Additionally, uncertain likelihoods for the occurrence of risk...
Likelihood ratio sequential sampling models of recognition memory.
Osth, Adam F; Dennis, Simon; Heathcote, Andrew
2017-02-01
The mirror effect - a phenomenon whereby a manipulation produces opposite effects on hit and false alarm rates - is benchmark regularity of recognition memory. A likelihood ratio decision process, basing recognition on the relative likelihood that a stimulus is a target or a lure, naturally predicts the mirror effect, and so has been widely adopted in quantitative models of recognition memory. Glanzer, Hilford, and Maloney (2009) demonstrated that likelihood ratio models, assuming Gaussian memory strength, are also capable of explaining regularities observed in receiver-operating characteristics (ROCs), such as greater target than lure variance. Despite its central place in theorising about recognition memory, however, this class of models has not been tested using response time (RT) distributions. In this article, we develop a linear approximation to the likelihood ratio transformation, which we show predicts the same regularities as the exact transformation. This development enabled us to develop a tractable model of recognition-memory RT based on the diffusion decision model (DDM), with inputs (drift rates) provided by an approximate likelihood ratio transformation. We compared this "LR-DDM" to a standard DDM where all targets and lures receive their own drift rate parameters. Both were implemented as hierarchical Bayesian models and applied to four datasets. Model selection taking into account parsimony favored the LR-DDM, which requires fewer parameters than the standard DDM but still fits the data well. These results support log-likelihood based models as providing an elegant explanation of the regularities of recognition memory, not only in terms of choices made but also in terms of the times it takes to make them. Copyright © 2016 Elsevier Inc. All rights reserved.
Likelihood-based inference for cointegration with nonlinear error-correction
DEFF Research Database (Denmark)
Kristensen, Dennis; Rahbek, Anders Christian
2010-01-01
We consider a class of nonlinear vector error correction models where the transfer function (or loadings) of the stationary relationships is nonlinear. This includes in particular the smooth transition models. A general representation theorem is given which establishes the dynamic properties...... and a linear trend in general. Gaussian likelihood-based estimators are considered for the long-run cointegration parameters, and the short-run parameters. Asymptotic theory is provided for these and it is discussed to what extend asymptotic normality and mixed normality can be found. A simulation study...
Unbinned likelihood maximisation framework for neutrino clustering in Python
Energy Technology Data Exchange (ETDEWEB)
Coenders, Stefan [Technische Universitaet Muenchen, Boltzmannstr. 2, 85748 Garching (Germany)
2016-07-01
Albeit having detected an astrophysical neutrino flux with IceCube, sources of astrophysical neutrinos remain hidden up to now. A detection of a neutrino point source is a smoking gun for hadronic processes and acceleration of cosmic rays. The search for neutrino sources has many degrees of freedom, for example steady versus transient, point-like versus extended sources, et cetera. Here, we introduce a Python framework designed for unbinned likelihood maximisations as used in searches for neutrino point sources by IceCube. Implementing source scenarios in a modular way, likelihood searches on various kinds can be implemented in a user-friendly way, without sacrificing speed and memory management.
Nearly Efficient Likelihood Ratio Tests of the Unit Root Hypothesis
DEFF Research Database (Denmark)
Jansson, Michael; Nielsen, Morten Ørregaard
Seemingly absent from the arsenal of currently available "nearly efficient" testing procedures for the unit root hypothesis, i.e. tests whose local asymptotic power functions are indistinguishable from the Gaussian power envelope, is a test admitting a (quasi-)likelihood ratio interpretation. We...... show that the likelihood ratio unit root test derived in a Gaussian AR(1) model with standard normal innovations is nearly efficient in that model. Moreover, these desirable properties carry over to more complicated models allowing for serially correlated and/or non-Gaussian innovations....
A note on estimating errors from the likelihood function
International Nuclear Information System (INIS)
Barlow, Roger
2005-01-01
The points at which the log likelihood falls by 12 from its maximum value are often used to give the 'errors' on a result, i.e. the 68% central confidence interval. The validity of this is examined for two simple cases: a lifetime measurement and a Poisson measurement. Results are compared with the exact Neyman construction and with the simple Bartlett approximation. It is shown that the accuracy of the log likelihood method is poor, and the Bartlett construction explains why it is flawed
LDR: A Package for Likelihood-Based Sufficient Dimension Reduction
Directory of Open Access Journals (Sweden)
R. Dennis Cook
2011-03-01
Full Text Available We introduce a new mlab software package that implements several recently proposed likelihood-based methods for sufficient dimension reduction. Current capabilities include estimation of reduced subspaces with a fixed dimension d, as well as estimation of d by use of likelihood-ratio testing, permutation testing and information criteria. The methods are suitable for preprocessing data for both regression and classification. Implementations of related estimators are also available. Although the software is more oriented to command-line operation, a graphical user interface is also provided for prototype computations.
Quantifying uncertainty in LCA-modelling of waste management systems
DEFF Research Database (Denmark)
Clavreul, Julie; Guyonnet, D.; Christensen, Thomas Højlund
2012-01-01
Uncertainty analysis in LCA studies has been subject to major progress over the last years. In the context of waste management, various methods have been implemented but a systematic method for uncertainty analysis of waste-LCA studies is lacking. The objective of this paper is (1) to present...... the sources of uncertainty specifically inherent to waste-LCA studies, (2) to select and apply several methods for uncertainty analysis and (3) to develop a general framework for quantitative uncertainty assessment of LCA of waste management systems. The suggested method is a sequence of four steps combining...
Accuracy of maximum likelihood estimates of a two-state model in single-molecule FRET
Energy Technology Data Exchange (ETDEWEB)
Gopich, Irina V. [Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland 20892 (United States)
2015-01-21
Photon sequences from single-molecule Förster resonance energy transfer (FRET) experiments can be analyzed using a maximum likelihood method. Parameters of the underlying kinetic model (FRET efficiencies of the states and transition rates between conformational states) are obtained by maximizing the appropriate likelihood function. In addition, the errors (uncertainties) of the extracted parameters can be obtained from the curvature of the likelihood function at the maximum. We study the standard deviations of the parameters of a two-state model obtained from photon sequences with recorded colors and arrival times. The standard deviations can be obtained analytically in a special case when the FRET efficiencies of the states are 0 and 1 and in the limiting cases of fast and slow conformational dynamics. These results are compared with the results of numerical simulations. The accuracy and, therefore, the ability to predict model parameters depend on how fast the transition rates are compared to the photon count rate. In the limit of slow transitions, the key parameters that determine the accuracy are the number of transitions between the states and the number of independent photon sequences. In the fast transition limit, the accuracy is determined by the small fraction of photons that are correlated with their neighbors. The relative standard deviation of the relaxation rate has a “chevron” shape as a function of the transition rate in the log-log scale. The location of the minimum of this function dramatically depends on how well the FRET efficiencies of the states are separated.
Clarification of the use of chi-square and likelihood functions in fits to histograms
International Nuclear Information System (INIS)
Baker, S.; Cousins, R.D.
1984-01-01
We consider the problem of fitting curves to histograms in which the data obey multinomial or Poisson statistics. Techniques commonly used by physicists are examined in light of standard results found in the statistics literature. We review the relationship between multinomial and Poisson distributions, and clarify a sufficient condition for equality of the area under the fitted curve and the number of events on the histogram. Following the statisticians, we use the likelihood ratio test to construct a general Z 2 statistic, Zsub(lambda) 2 , which yields parameter and error estimates identical to those of the method of maximum likelihood. The Zsub(lambda) 2 statistic is further useful for testing goodness-of-fit since the value of its minimum asymptotically obeys a classical chi-square distribution. One should be aware, however, of the potential for statistical bias, especially when the number of events is small. (orig.)
Uncertainty in prediction and in inference
International Nuclear Information System (INIS)
Hilgevoord, J.; Uffink, J.
1991-01-01
The concepts of uncertainty in prediction and inference are introduced and illustrated using the diffraction of light as an example. The close relationship between the concepts of uncertainty in inference and resolving power is noted. A general quantitative measure of uncertainty in inference can be obtained by means of the so-called statistical distance between probability distributions. When applied to quantum mechanics, this distance leads to a measure of the distinguishability of quantum states, which essentially is the absolute value of the matrix element between the states. The importance of this result to the quantum mechanical uncertainty principle is noted. The second part of the paper provides a derivation of the statistical distance on the basis of the so-called method of support
Policy Uncertainty and the US Ethanol Industry
Directory of Open Access Journals (Sweden)
Jason P. H. Jones
2017-11-01
Full Text Available The Renewable Fuel Standard (RFS2, as implemented, has introduced uncertainty into US ethanol producers and the supporting commodity market. First, the fixed mandate for what is mainly cornstarch-based ethanol has increased feedstock price volatility and exerts a general effect across the agricultural sector. Second, the large discrepancy between the original Energy Independence and Security Act (EISA intentions and the actual RFS2 implementation for some fuel classes has increased the investment uncertainty facing investors in biofuel production, distribution, and consumption. Here we discuss and analyze the sources of uncertainty and evaluate the effect of potential RFS2 adjustments as they influence these uncertainties. This includes the use of a flexible, production dependent mandate on corn starch ethanol. We find that a flexible mandate on cornstarch ethanol relaxed during drought could significantly reduce commodity price spikes and alleviate the decline of livestock production in cases of feedstock production shortfalls, but it would increase the risk for ethanol investors.
Uncertainties in predicting solar panel power output
Anspaugh, B.
1974-01-01
The problem of calculating solar panel power output at launch and during a space mission is considered. The major sources of uncertainty and error in predicting the post launch electrical performance of the panel are considered. A general discussion of error analysis is given. Examples of uncertainty calculations are included. A general method of calculating the effect on the panel of various degrading environments is presented, with references supplied for specific methods. A technique for sizing a solar panel for a required mission power profile is developed.
Evaluation of Dynamic Coastal Response to Sea-level Rise Modifies Inundation Likelihood
Lentz, Erika E.; Thieler, E. Robert; Plant, Nathaniel G.; Stippa, Sawyer R.; Horton, Radley M.; Gesch, Dean B.
2016-01-01
Sea-level rise (SLR) poses a range of threats to natural and built environments, making assessments of SLR-induced hazards essential for informed decision making. We develop a probabilistic model that evaluates the likelihood that an area will inundate (flood) or dynamically respond (adapt) to SLR. The broad-area applicability of the approach is demonstrated by producing 30x30m resolution predictions for more than 38,000 sq km of diverse coastal landscape in the northeastern United States. Probabilistic SLR projections, coastal elevation and vertical land movement are used to estimate likely future inundation levels. Then, conditioned on future inundation levels and the current land-cover type, we evaluate the likelihood of dynamic response versus inundation. We find that nearly 70% of this coastal landscape has some capacity to respond dynamically to SLR, and we show that inundation models over-predict land likely to submerge. This approach is well suited to guiding coastal resource management decisions that weigh future SLR impacts and uncertainty against ecological targets and economic constraints.
Directory of Open Access Journals (Sweden)
Chang-bae Moon
2011-01-01
Full Text Available Although there have been many researches on mobile robot localization, it is still difficult to obtain reliable localization performance in a human co-existing real environment. Reliability of localization is highly dependent upon developer's experiences because uncertainty is caused by a variety of reasons. We have developed a range sensor based integrated localization scheme for various indoor service robots. Through the experience, we found out that there are several significant experimental issues. In this paper, we provide useful solutions for following questions which are frequently faced with in practical applications: 1 How to design an observation likelihood model? 2 How to detect the localization failure? 3 How to recover from the localization failure? We present design guidelines of observation likelihood model. Localization failure detection and recovery schemes are presented by focusing on abrupt wheel slippage. Experiments were carried out in a typical office building environment. The proposed scheme to identify the localizer status is useful in practical environments. Moreover, the semi-global localization is a computationally efficient recovery scheme from localization failure. The results of experiments and analysis clearly present the usefulness of proposed solutions.
Directory of Open Access Journals (Sweden)
Chang-bae Moon
2010-12-01
Full Text Available Although there have been many researches on mobile robot localization, it is still difficult to obtain reliable localization performance in a human co-existing real environment. Reliability of localization is highly dependent upon developer's experiences because uncertainty is caused by a variety of reasons. We have developed a range sensor based integrated localization scheme for various indoor service robots. Through the experience, we found out that there are several significant experimental issues. In this paper, we provide useful solutions for following questions which are frequently faced with in practical applications: 1 How to design an observation likelihood model? 2 How to detect the localization failure? 3 How to recover from the localization failure? We present design guidelines of observation likelihood model. Localization failure detection and recovery schemes are presented by focusing on abrupt wheel slippage. Experiments were carried out in a typical office building environment. The proposed scheme to identify the localizer status is useful in practical environments. Moreover, the semi-global localization is a computationally efficient recovery scheme from localization failure. The results of experiments and analysis clearly present the usefulness of proposed solutions.
Directory of Open Access Journals (Sweden)
Zhang Zhang
2009-06-01
Full Text Available A major analytical challenge in computational biology is the detection and description of clusters of specified site types, such as polymorphic or substituted sites within DNA or protein sequences. Progress has been stymied by a lack of suitable methods to detect clusters and to estimate the extent of clustering in discrete linear sequences, particularly when there is no a priori specification of cluster size or cluster count. Here we derive and demonstrate a maximum likelihood method of hierarchical clustering. Our method incorporates a tripartite divide-and-conquer strategy that models sequence heterogeneity, delineates clusters, and yields a profile of the level of clustering associated with each site. The clustering model may be evaluated via model selection using the Akaike Information Criterion, the corrected Akaike Information Criterion, and the Bayesian Information Criterion. Furthermore, model averaging using weighted model likelihoods may be applied to incorporate model uncertainty into the profile of heterogeneity across sites. We evaluated our method by examining its performance on a number of simulated datasets as well as on empirical polymorphism data from diverse natural alleles of the Drosophila alcohol dehydrogenase gene. Our method yielded greater power for the detection of clustered sites across a breadth of parameter ranges, and achieved better accuracy and precision of estimation of clusters, than did the existing empirical cumulative distribution function statistics.
Sustainability likelihood of remediation options for metal-contaminated soil/sediment.
Chen, Season S; Taylor, Jessica S; Baek, Kitae; Khan, Eakalak; Tsang, Daniel C W; Ok, Yong Sik
2017-05-01
Multi-criteria analysis and detailed impact analysis were carried out to assess the sustainability of four remedial alternatives for metal-contaminated soil/sediment at former timber treatment sites and harbour sediment with different scales. The sustainability was evaluated in the aspects of human health and safety, environment, stakeholder concern, and land use, under four different scenarios with varying weighting factors. The Monte Carlo simulation was performed to reveal the likelihood of accomplishing sustainable remediation with different treatment options at different sites. The results showed that in-situ remedial technologies were more sustainable than ex-situ ones, where in-situ containment demonstrated both the most sustainable result and the highest probability to achieve sustainability amongst the four remedial alternatives in this study, reflecting the lesser extent of off-site and on-site impacts. Concerns associated with ex-situ options were adverse impacts tied to all four aspects and caused by excavation, extraction, and off-site disposal. The results of this study suggested the importance of considering the uncertainties resulting from the remedial options (i.e., stochastic analysis) in addition to the overall sustainability scores (i.e., deterministic analysis). The developed framework and model simulation could serve as an assessment for the sustainability likelihood of remedial options to ensure sustainable remediation of contaminated sites. Copyright © 2017 Elsevier Ltd. All rights reserved.
Uncertainty and validation. Effect of model complexity on uncertainty estimates
International Nuclear Information System (INIS)
Elert, M.
1996-09-01
deterministic case, and the uncertainty bands did not always overlap. This suggest that there are considerable model uncertainties present, which were not considered in this study. Concerning possible constraints in the application domain of different models, the results of this exercise suggest that if only the evolution of the root zone concentration is to be predicted, all of the studied models give comparable results. However, if also the flux to the groundwater is to be predicted, then a considerably increased amount of detail is needed concerning the model and the parameterization. This applies to the hydrological as well as the transport modelling. The difference in model predictions and the magnitude of uncertainty was quite small for some of the end-points predicted, while for others it could span many orders of magnitude. Of special importance were end-points where delay in the soil was involved, e.g. release to the groundwater. In such cases the influence of radioactive decay gave rise to strongly non-linear effects. The work in the subgroup has provided many valuable insights on the effects of model simplifications, e.g. discretization in the model, averaging of the time varying input parameters and the assignment of uncertainties to parameters. The conclusions that have been drawn concerning these are primarily valid for the studied scenario. However, we believe that they to a large extent also are generally applicable. The subgroup have had many opportunities to study the pitfalls involved in model comparison. The intention was to provide a well defined scenario for the subgroup, but despite several iterations misunderstandings and ambiguities remained. The participants have been forced to scrutinize their models to try to explain differences in the predictions and most, if not all, of the participants have improved their models as a result of this
Yu, Soonyoung; Unger, Andre J A; Parker, Beth; Kim, Taehee
2012-06-15
In this study, we defined risk capital as the contingency fee or insurance premium that a brownfields redeveloper needs to set aside from the sale of each house in case they need to repurchase it at a later date because the indoor air has been detrimentally affected by subsurface contamination. The likelihood that indoor air concentrations will exceed a regulatory level subject to subsurface heterogeneity and source zone location uncertainty is simulated by a physics-based hydrogeological model using Monte Carlo realizations, yielding the probability of failure. The cost of failure is the future value of the house indexed to the stochastic US National Housing index. The risk capital is essentially the probability of failure times the cost of failure with a surcharge to compensate the developer against hydrogeological and financial uncertainty, with the surcharge acting as safety loading reflecting the developers' level of risk aversion. We review five methodologies taken from the actuarial and financial literature to price the risk capital for a highly stylized brownfield redevelopment project, with each method specifically adapted to accommodate our notion of the probability of failure. The objective of this paper is to develop an actuarially consistent approach for combining the hydrogeological and financial uncertainty into a contingency fee that the brownfields developer should reserve (i.e. the risk capital) in order to hedge their risk exposure during the project. Results indicate that the price of the risk capital is much more sensitive to hydrogeological rather than financial uncertainty. We use the Capital Asset Pricing Model to estimate the risk-adjusted discount rate to depreciate all costs to present value for the brownfield redevelopment project. A key outcome of this work is that the presentation of our risk capital valuation methodology is sufficiently generalized for application to a wide variety of engineering projects. Copyright © 2012 Elsevier
Dealing with exploration uncertainties
International Nuclear Information System (INIS)
Capen, E.
1992-01-01
Exploration for oil and gas should fulfill the most adventurous in their quest for excitement and surprise. This paper tries to cover that tall order. The authors will touch on the magnitude of the uncertainty (which is far greater than in most other businesses), the effects of not knowing target sizes very well, how to build uncertainty into analyses naturally, how to tie reserves and chance estimates to economics, and how to look at the portfolio effect of an exploration program. With no apologies, the authors will be using a different language for some readers - the language of uncertainty, which means probability and statistics. These tools allow one to combine largely subjective exploration information with the more analytical data from the engineering and economic side
Understanding the properties of diagnostic tests - Part 2: Likelihood ratios.
Ranganathan, Priya; Aggarwal, Rakesh
2018-01-01
Diagnostic tests are used to identify subjects with and without disease. In a previous article in this series, we examined some attributes of diagnostic tests - sensitivity, specificity, and predictive values. In this second article, we look at likelihood ratios, which are useful for the interpretation of diagnostic test results in everyday clinical practice.
Maximum likelihood estimation of the attenuated ultrasound pulse
DEFF Research Database (Denmark)
Rasmussen, Klaus Bolding
1994-01-01
The attenuated ultrasound pulse is divided into two parts: a stationary basic pulse and a nonstationary attenuation pulse. A standard ARMA model is used for the basic pulse, and a nonstandard ARMA model is derived for the attenuation pulse. The maximum likelihood estimator of the attenuated...
Robust Gaussian Process Regression with a Student-t Likelihood
Jylänki, P.P.; Vanhatalo, J.; Vehtari, A.
2011-01-01
This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model, which has a non-log-concave likelihood. The challenge with the Student-t model is the analytically intractable inference which is why several approximative methods have
MAXIMUM-LIKELIHOOD-ESTIMATION OF THE ENTROPY OF AN ATTRACTOR
SCHOUTEN, JC; TAKENS, F; VANDENBLEEK, CM
In this paper, a maximum-likelihood estimate of the (Kolmogorov) entropy of an attractor is proposed that can be obtained directly from a time series. Also, the relative standard deviation of the entropy estimate is derived; it is dependent on the entropy and on the number of samples used in the
A simplification of the likelihood ratio test statistic for testing ...
African Journals Online (AJOL)
The traditional likelihood ratio test statistic for testing hypothesis about goodness of fit of multinomial probabilities in one, two and multi – dimensional contingency table was simplified. Advantageously, using the simplified version of the statistic to test the null hypothesis is easier and faster because calculating the expected ...
Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation
DEFF Research Database (Denmark)
Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik
2017-01-01
The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated...
LIKELIHOOD ESTIMATION OF PARAMETERS USING SIMULTANEOUSLY MONITORED PROCESSES
DEFF Research Database (Denmark)
Friis-Hansen, Peter; Ditlevsen, Ove Dalager
2004-01-01
The topic is maximum likelihood inference from several simultaneously monitored response processes of a structure to obtain knowledge about the parameters of other not monitored but important response processes when the structure is subject to some Gaussian load field in space and time. The consi....... The considered example is a ship sailing with a given speed through a Gaussian wave field....
Likelihood-based Dynamic Factor Analysis for Measurement and Forecasting
Jungbacker, B.M.J.P.; Koopman, S.J.
2015-01-01
We present new results for the likelihood-based analysis of the dynamic factor model. The latent factors are modelled by linear dynamic stochastic processes. The idiosyncratic disturbance series are specified as autoregressive processes with mutually correlated innovations. The new results lead to
Composite likelihood and two-stage estimation in family studies
DEFF Research Database (Denmark)
Andersen, Elisabeth Anne Wreford
2004-01-01
In this paper register based family studies provide the motivation for linking a two-stage estimation procedure in copula models for multivariate failure time data with a composite likelihood approach. The asymptotic properties of the estimators in both parametric and semi-parametric models are d...
Reconceptualizing Social Influence in Counseling: The Elaboration Likelihood Model.
McNeill, Brian W.; Stoltenberg, Cal D.
1989-01-01
Presents Elaboration Likelihood Model (ELM) of persuasion (a reconceptualization of the social influence process) as alternative model of attitude change. Contends ELM unifies conflicting social psychology results and can potentially account for inconsistent research findings in counseling psychology. Provides guidelines on integrating…
Counseling Pretreatment and the Elaboration Likelihood Model of Attitude Change.
Heesacker, Martin
1986-01-01
Results of the application of the Elaboration Likelihood Model (ELM) to a counseling context revealed that more favorable attitudes toward counseling occurred as subjects' ego involvement increased and as intervention quality improved. Counselor credibility affected the degree to which subjects' attitudes reflected argument quality differences.…
Cases in which ancestral maximum likelihood will be confusingly misleading.
Handelman, Tomer; Chor, Benny
2017-05-07
Ancestral maximum likelihood (AML) is a phylogenetic tree reconstruction criteria that "lies between" maximum parsimony (MP) and maximum likelihood (ML). ML has long been known to be statistically consistent. On the other hand, Felsenstein (1978) showed that MP is statistically inconsistent, and even positively misleading: There are cases where the parsimony criteria, applied to data generated according to one tree topology, will be optimized on a different tree topology. The question of weather AML is statistically consistent or not has been open for a long time. Mossel et al. (2009) have shown that AML can "shrink" short tree edges, resulting in a star tree with no internal resolution, which yields a better AML score than the original (resolved) model. This result implies that AML is statistically inconsistent, but not that it is positively misleading, because the star tree is compatible with any other topology. We show that AML is confusingly misleading: For some simple, four taxa (resolved) tree, the ancestral likelihood optimization criteria is maximized on an incorrect (resolved) tree topology, as well as on a star tree (both with specific edge lengths), while the tree with the original, correct topology, has strictly lower ancestral likelihood. Interestingly, the two short edges in the incorrect, resolved tree topology are of length zero, and are not adjacent, so this resolved tree is in fact a simple path. While for MP, the underlying phenomenon can be described as long edge attraction, it turns out that here we have long edge repulsion. Copyright © 2017. Published by Elsevier Ltd.
Multilevel maximum likelihood estimation with application to covariance matrices
Czech Academy of Sciences Publication Activity Database
Turčičová, Marie; Mandel, J.; Eben, Kryštof
Published online: 23 January ( 2018 ) ISSN 0361-0926 R&D Projects: GA ČR GA13-34856S Institutional support: RVO:67985807 Keywords : Fisher information * High dimension * Hierarchical maximum likelihood * Nested parameter spaces * Spectral diagonal covariance model * Sparse inverse covariance model Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.311, year: 2016
Uncertainty in artificial intelligence
Levitt, TS; Lemmer, JF; Shachter, RD
1990-01-01
Clearly illustrated in this volume is the current relationship between Uncertainty and AI.It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally i
Sensitivity and uncertainty analysis
Cacuci, Dan G; Navon, Ionel Michael
2005-01-01
As computer-assisted modeling and analysis of physical processes have continued to grow and diversify, sensitivity and uncertainty analyses have become indispensable scientific tools. Sensitivity and Uncertainty Analysis. Volume I: Theory focused on the mathematical underpinnings of two important methods for such analyses: the Adjoint Sensitivity Analysis Procedure and the Global Adjoint Sensitivity Analysis Procedure. This volume concentrates on the practical aspects of performing these analyses for large-scale systems. The applications addressed include two-phase flow problems, a radiative c
Elusive drought: uncertainty in observed trends and short- and long-term CMIP5 projections
Directory of Open Access Journals (Sweden)
B. Orlowsky
2013-05-01
Full Text Available Recent years have seen a number of severe droughts in different regions around the world, causing agricultural and economic losses, famines and migration. Despite their devastating consequences, the Standardised Precipitation Index (SPI of these events lies within the general range of observation-based SPI time series and simulations from the 5th phase of the Coupled Model Intercomparison Project (CMIP5. In terms of magnitude, regional trends of SPI over the last decades remain mostly inconclusive in observation-based datasets and CMIP5 simulations, but Soil Moisture Anomalies (SMAs in CMIP5 simulations hint at increased drought in a few regions (e.g., the Mediterranean, Central America/Mexico, the Amazon, North-East Brazil and South Africa. Also for the future, projections of changes in the magnitude of meteorological (SPI and soil moisture (SMA drought in CMIP5 display large spreads over all time frames, generally impeding trend detection. However, projections of changes in the frequencies of future drought events display more robust signal-to-noise ratios, with detectable trends towards more frequent drought before the end of the 21st century in the Mediterranean, South Africa and Central America/Mexico. Other present-day hot spots are projected to become less drought-prone, or display non-significant changes in drought occurrence. A separation of different sources of uncertainty in projections of meteorological and soil moisture drought reveals that for the near term, internal climate variability is the dominant source, while the formulation of Global Climate Models (GCMs generally becomes the dominant source of spread by the end of the 21st century, especially for soil moisture drought. In comparison, the uncertainty from Green-House Gas (GHG concentrations scenarios is negligible for most regions. These findings stand in contrast to respective analyses for a heat wave index, for which GHG concentrations scenarios constitute the main source
Is the investment-uncertainty relationship nonlinear? An empirical analysis for the Netherlands
Bo, H; Lensin, R
We examine the investment-uncertainty relationship for a panel of Dutch non-financial firms. The system generalized method of moments (GMM) estimates suggest that the effect of uncertainty on investment is nonlinear: for low levels of uncertainty an increase in uncertainty has a positive effect on
Probabilistic accounting of uncertainty in forecasts of species distributions under climate change
Seth J. Wenger; Nicholas A. Som; Daniel C. Dauwalter; Daniel J. Isaak; Helen M. Neville; Charles H. Luce; Jason B. Dunham; Michael K. Young; Kurt D. Fausch; Bruce E. Rieman
2013-01-01
Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing...
Dilaton cosmology and the modified uncertainty principle
International Nuclear Information System (INIS)
Majumder, Barun
2011-01-01
Very recently Ali et al. (2009) proposed a new generalized uncertainty principle (with a linear term in Plank length which is consistent with doubly special relativity and string theory. The classical and quantum effects of this generalized uncertainty principle (termed as modified uncertainty principle or MUP) are investigated on the phase space of a dilatonic cosmological model with an exponential dilaton potential in a flat Friedmann-Robertson-Walker background. Interestingly, as a consequence of MUP, we found that it is possible to get a late time acceleration for this model. For the quantum mechanical description in both commutative and MUP framework, we found the analytical solutions of the Wheeler-DeWitt equation for the early universe and compare our results. We have used an approximation method in the case of MUP.
Development of a Dynamic Lidar Uncertainty Framework
Energy Technology Data Exchange (ETDEWEB)
Newman, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Clifton, Andrew [WindForS; Bonin, Timothy [CIRES/NOAA ESRL; Choukulkar, Aditya [CIRES/NOAA ESRL; Brewer, W. Alan [NOAA ESRL; Delgado, Ruben [University of Maryland Baltimore County
2017-08-07
As wind turbine sizes increase and wind energy expands to more complex and remote sites, remote-sensing devices such as lidars are expected to play a key role in wind resource assessment and power performance testing. The switch to remote-sensing devices represents a paradigm shift in the way the wind industry typically obtains and interprets measurement data for wind energy. For example, the measurement techniques and sources of uncertainty for a remote-sensing device are vastly different from those associated with a cup anemometer on a meteorological tower. Current IEC standards for quantifying remote sensing device uncertainty for power performance testing consider uncertainty due to mounting, calibration, and classification of the remote sensing device, among other parameters. Values of the uncertainty are typically given as a function of the mean wind speed measured by a reference device and are generally fixed, leading to climatic uncertainty values that apply to the entire measurement campaign. However, real-world experience and a consideration of the fundamentals of the measurement process have shown that lidar performance is highly dependent on atmospheric conditions, such as wind shear, turbulence, and aerosol content. At present, these conditions are not directly incorporated into the estimated uncertainty of a lidar device. In this presentation, we describe the development of a new dynamic lidar uncertainty framework that adapts to current flow conditions and more accurately represents the actual uncertainty inherent in lidar measurements under different conditions. In this new framework, sources of uncertainty are identified for estimation of the line-of-sight wind speed and reconstruction of the three-dimensional wind field. These sources are then related to physical processes caused by the atmosphere and lidar operating conditions. The framework is applied to lidar data from a field measurement site to assess the ability of the framework to predict
Uncertainties in repository modeling
Energy Technology Data Exchange (ETDEWEB)
Wilson, J.R.
1996-12-31
The distant future is ver difficult to predict. Unfortunately, our regulators are being enchouraged to extend ther regulatory period form the standard 10,000 years to 1 million years. Such overconfidence is not justified due to uncertainties in dating, calibration, and modeling.
Uncertainties in repository modeling
International Nuclear Information System (INIS)
Wilson, J.R.
1996-01-01
The distant future is ver difficult to predict. Unfortunately, our regulators are being enchouraged to extend ther regulatory period form the standard 10,000 years to 1 million years. Such overconfidence is not justified due to uncertainties in dating, calibration, and modeling
International Nuclear Information System (INIS)
Haefele, W.; Renn, O.; Erdmann, G.
1990-01-01
The notion of 'risk' is discussed in its social and technological contexts, leading to an investigation of the terms factuality, hypotheticality, uncertainty, and vagueness, and to the problems of acceptance and acceptability especially in the context of political decision finding. (DG) [de
International conference on Facets of Uncertainties and Applications
Skowron, Andrzej; Maiti, Manoranjan; Kar, Samarjit
2015-01-01
Since the emergence of the formal concept of probability theory in the seventeenth century, uncertainty has been perceived solely in terms of probability theory. However, this apparently unique link between uncertainty and probability theory has come under investigation a few decades back. Uncertainties are nowadays accepted to be of various kinds. Uncertainty in general could refer to different sense like not certainly known, questionable, problematic, vague, not definite or determined, ambiguous, liable to change, not reliable. In Indian languages, particularly in Sanskrit-based languages, there are other higher levels of uncertainties. It has been shown that several mathematical concepts such as the theory of fuzzy sets, theory of rough sets, evidence theory, possibility theory, theory of complex systems and complex network, theory of fuzzy measures and uncertainty theory can also successfully model uncertainty.
Likelihood Inference of Nonlinear Models Based on a Class of Flexible Skewed Distributions
Directory of Open Access Journals (Sweden)
Xuedong Chen
2014-01-01
Full Text Available This paper deals with the issue of the likelihood inference for nonlinear models with a flexible skew-t-normal (FSTN distribution, which is proposed within a general framework of flexible skew-symmetric (FSS distributions by combining with skew-t-normal (STN distribution. In comparison with the common skewed distributions such as skew normal (SN, and skew-t (ST as well as scale mixtures of skew normal (SMSN, the FSTN distribution can accommodate more flexibility and robustness in the presence of skewed, heavy-tailed, especially multimodal outcomes. However, for this distribution, a usual approach of maximum likelihood estimates based on EM algorithm becomes unavailable and an alternative way is to return to the original Newton-Raphson type method. In order to improve the estimation as well as the way for confidence estimation and hypothesis test for the parameters of interest, a modified Newton-Raphson iterative algorithm is presented in this paper, based on profile likelihood for nonlinear regression models with FSTN distribution, and, then, the confidence interval and hypothesis test are also developed. Furthermore, a real example and simulation are conducted to demonstrate the usefulness and the superiority of our approach.
Computation of the Likelihood in Biallelic Diffusion Models Using Orthogonal Polynomials
Directory of Open Access Journals (Sweden)
Claus Vogl
2014-11-01
Full Text Available In population genetics, parameters describing forces such as mutation, migration and drift are generally inferred from molecular data. Lately, approximate methods based on simulations and summary statistics have been widely applied for such inference, even though these methods waste information. In contrast, probabilistic methods of inference can be shown to be optimal, if their assumptions are met. In genomic regions where recombination rates are high relative to mutation rates, polymorphic nucleotide sites can be assumed to evolve independently from each other. The distribution of allele frequencies at a large number of such sites has been called “allele-frequency spectrum” or “site-frequency spectrum” (SFS. Conditional on the allelic proportions, the likelihoods of such data can be modeled as binomial. A simple model representing the evolution of allelic proportions is the biallelic mutation-drift or mutation-directional selection-drift diffusion model. With series of orthogonal polynomials, specifically Jacobi and Gegenbauer polynomials, or the related spheroidal wave function, the diffusion equations can be solved efficiently. In the neutral case, the product of the binomial likelihoods with the sum of such polynomials leads to finite series of polynomials, i.e., relatively simple equations, from which the exact likelihoods can be calculated. In this article, the use of orthogonal polynomials for inferring population genetic parameters is investigated.
Courtney, H; Kirkland, J; Viguerie, P
1997-01-01
At the heart of the traditional approach to strategy lies the assumption that by applying a set of powerful analytic tools, executives can predict the future of any business accurately enough to allow them to choose a clear strategic direction. But what happens when the environment is so uncertain that no amount of analysis will allow us to predict the future? What makes for a good strategy in highly uncertain business environments? The authors, consultants at McKinsey & Company, argue that uncertainty requires a new way of thinking about strategy. All too often, they say, executives take a binary view: either they underestimate uncertainty to come up with the forecasts required by their companies' planning or capital-budging processes, or they overestimate it, abandon all analysis, and go with their gut instinct. The authors outline a new approach that begins by making a crucial distinction among four discrete levels of uncertainty that any company might face. They then explain how a set of generic strategies--shaping the market, adapting to it, or reserving the right to play at a later time--can be used in each of the four levels. And they illustrate how these strategies can be implemented through a combination of three basic types of actions: big bets, options, and no-regrets moves. The framework can help managers determine which analytic tools can inform decision making under uncertainty--and which cannot. At a broader level, it offers executives a discipline for thinking rigorously and systematically about uncertainty and its implications for strategy.
Uncertainties and reliability theories for reactor safety
International Nuclear Information System (INIS)
Veneziano, D.
1975-01-01
What makes the safety problem of nuclear reactors particularly challenging is the demand for high levels of reliability and the limitation of statistical information. The latter is an unfortunate circumstance, which forces deductive theories of reliability to use models and parameter values with weak factual support. The uncertainty about probabilistic models and parameters which are inferred from limited statistical evidence can be quantified and incorporated rationally into inductive theories of reliability. In such theories, the starting point is the information actually available, as opposed to an estimated probabilistic model. But, while the necessity of introducing inductive uncertainty into reliability theories has been recognized by many authors, no satisfactory inductive theory is presently available. The paper presents: a classification of uncertainties and of reliability models for reactor safety; a general methodology to include these uncertainties into reliability analysis; a discussion about the relative advantages and the limitations of various reliability theories (specifically, of inductive and deductive, parametric and nonparametric, second-moment and full-distribution theories). For example, it is shown that second-moment theories, which were originally suggested to cope with the scarcity of data, and which have been proposed recently for the safety analysis of secondary containment vessels, are the least capable of incorporating statistical uncertainty. The focus is on reliability models for external threats (seismic accelerations and tornadoes). As an application example, the effect of statistical uncertainty on seismic risk is studied using parametric full-distribution models
Optimization Under Uncertainty for Wake Steering Strategies
Energy Technology Data Exchange (ETDEWEB)
Quick, Julian [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Annoni, Jennifer [National Renewable Energy Laboratory (NREL), Golden, CO (United States); King, Ryan N [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Dykes, Katherine L [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Fleming, Paul A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Ning, Andrew [Brigham Young University
2017-08-03
Offsetting turbines' yaw orientations from incoming wind is a powerful tool that may be leveraged to reduce undesirable wake effects on downstream turbines. First, we examine a simple two-turbine case to gain intuition as to how inflow direction uncertainty affects the optimal solution. The turbines are modeled with unidirectional inflow such that one turbine directly wakes the other, using ten rotor diameter spacing. We perform optimization under uncertainty (OUU) via a parameter sweep of the front turbine. The OUU solution generally prefers less steering. We then do this optimization for a 60-turbine wind farm with unidirectional inflow, varying the degree of inflow uncertainty and approaching this OUU problem by nesting a polynomial chaos expansion uncertainty quantification routine within an outer optimization. We examined how different levels of uncertainty in the inflow direction effect the ratio of the expected values of deterministic and OUU solutions for steering strategies in the large wind farm, assuming the directional uncertainty used to reach said OUU solution (this ratio is defined as the value of the stochastic solution or VSS).
Not Normal: the uncertainties of scientific measurements
Bailey, David C.
2017-01-01
Judging the significance and reproducibility of quantitative research requires a good understanding of relevant uncertainties, but it is often unclear how well these have been evaluated and what they imply. Reported scientific uncertainties were studied by analysing 41 000 measurements of 3200 quantities from medicine, nuclear and particle physics, and interlaboratory comparisons ranging from chemistry to toxicology. Outliers are common, with 5σ disagreements up to five orders of magnitude more frequent than naively expected. Uncertainty-normalized differences between multiple measurements of the same quantity are consistent with heavy-tailed Student's t-distributions that are often almost Cauchy, far from a Gaussian Normal bell curve. Medical research uncertainties are generally as well evaluated as those in physics, but physics uncertainty improves more rapidly, making feasible simple significance criteria such as the 5σ discovery convention in particle physics. Contributions to measurement uncertainty from mistakes and unknown problems are not completely unpredictable. Such errors appear to have power-law distributions consistent with how designed complex systems fail, and how unknown systematic errors are constrained by researchers. This better understanding may help improve analysis and meta-analysis of data, and help scientists and the public have more realistic expectations of what scientific results imply.
Revisiting organizational interpretation and three types of uncertainty
DEFF Research Database (Denmark)
Sund, Kristian J.
2015-01-01
that might help explain and untangle some of the conflicting empirical results found in the extant literature. The paper illustrates how the literature could benefit from re-conceptualizing the perceived environmental uncertainty construct to take into account different types of uncertainty. Practical....... Design/methodology/approach – This conceptual paper extends existing conceptual work by distinguishing between general and issue-specific scanning and linking the interpretation process to three different types of perceived uncertainty: state, effect and response uncertainty. Findings – It is proposed...... on existing work by linking the interpretation process to three different types of uncertainty (state, effect and response uncertainty) with several novel and testable propositions. The paper also differentiates clearly general (regular) scanning from issue-specific (irregular) scanning. Finally, the paper...
Risk Aversion, Price Uncertainty and Irreversible Investments
van den Goorbergh, R.W.J.; Huisman, K.J.M.; Kort, P.M.
2003-01-01
This paper generalizes the theory of irreversible investment under uncertainty by allowing for risk averse investors in the absence of com-plete markets.Until now this theory has only been developed in the cases of risk neutrality, or risk aversion in combination with complete markets.Within a
Quantification of Uncertainty in Thermal Building Simulation
DEFF Research Database (Denmark)
Brohus, Henrik; Haghighat, F.; Frier, Christian
In order to quantify uncertainty in thermal building simulation stochastic modelling is applied on a building model. An application of stochastic differential equations is presented in Part 1 comprising a general heat balance for an arbitrary number of loads and zones in a building to determine...
Managing Uncertainty in the Real World
Indian Academy of Sciences (India)
Home; Journals; Resonance – Journal of Science Education; Volume 4; Issue 4. Managing Uncertainty in the Real World - Fuzzy Systems. Satish Kumar. General Article ... Author Affiliations. Satish Kumar1. Department of Physics and Computer Science, Dayalbagh Educational Institute Dayalbagh, Agra 282005, India.
Uncertainty and Complementarity in Axiomatic Quantum Mechanics
Lahti, Pekka J.
1980-11-01
In this work an investigation of the uncertainty principle and the complementarity principle is carried through. A study of the physical content of these principles and their representation in the conventional Hilbert space formulation of quantum mechanics forms a natural starting point for this analysis. Thereafter is presented more general axiomatic framework for quantum mechanics, namely, a probability function formulation of the theory. In this general framework two extra axioms are stated, reflecting the ideas of the uncertainty principle and the complementarity principle, respectively. The quantal features of these axioms are explicated. The sufficiency of the state system guarantees that the observables satisfying the uncertainty principle are unbounded and noncompatible. The complementarity principle implies a non-Boolean proposition structure for the theory. Moreover, nonconstant complementary observables are always noncompatible. The uncertainty principle and the complementarity principle, as formulated in this work, are mutually independent. Some order is thus brought into the confused discussion about the interrelations of these two important principles. A comparison of the present formulations of the uncertainty principle and the complementarity principle with the Jauch formulation of the superposition principle is also given. The mutual independence of the three fundamental principles of the quantum theory is hereby revealed.
A composite likelihood approach for spatially correlated survival data
Paik, Jane; Ying, Zhiliang
2013-01-01
The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory. PMID:24223450
A composite likelihood approach for spatially correlated survival data.
Paik, Jane; Ying, Zhiliang
2013-01-01
The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.
Maximum Likelihood Compton Polarimetry with the Compton Spectrometer and Imager
Energy Technology Data Exchange (ETDEWEB)
Lowell, A. W.; Boggs, S. E; Chiu, C. L.; Kierans, C. A.; Sleator, C.; Tomsick, J. A.; Zoglauer, A. C. [Space Sciences Laboratory, University of California, Berkeley (United States); Chang, H.-K.; Tseng, C.-H.; Yang, C.-Y. [Institute of Astronomy, National Tsing Hua University, Taiwan (China); Jean, P.; Ballmoos, P. von [IRAP Toulouse (France); Lin, C.-H. [Institute of Physics, Academia Sinica, Taiwan (China); Amman, M. [Lawrence Berkeley National Laboratory (United States)
2017-10-20
Astrophysical polarization measurements in the soft gamma-ray band are becoming more feasible as detectors with high position and energy resolution are deployed. Previous work has shown that the minimum detectable polarization (MDP) of an ideal Compton polarimeter can be improved by ∼21% when an unbinned, maximum likelihood method (MLM) is used instead of the standard approach of fitting a sinusoid to a histogram of azimuthal scattering angles. Here we outline a procedure for implementing this maximum likelihood approach for real, nonideal polarimeters. As an example, we use the recent observation of GRB 160530A with the Compton Spectrometer and Imager. We find that the MDP for this observation is reduced by 20% when the MLM is used instead of the standard method.
Physical constraints on the likelihood of life on exoplanets
Lingam, Manasvi; Loeb, Abraham
2018-04-01
One of the most fundamental questions in exoplanetology is to determine whether a given planet is habitable. We estimate the relative likelihood of a planet's propensity towards habitability by considering key physical characteristics such as the role of temperature on ecological and evolutionary processes, and atmospheric losses via hydrodynamic escape and stellar wind erosion. From our analysis, we demonstrate that Earth-sized exoplanets in the habitable zone around M-dwarfs seemingly display much lower prospects of being habitable relative to Earth, owing to the higher incident ultraviolet fluxes and closer distances to the host star. We illustrate our results by specifically computing the likelihood (of supporting life) for the recently discovered exoplanets, Proxima b and TRAPPIST-1e, which we find to be several orders of magnitude smaller than that of Earth.
THESEUS: maximum likelihood superpositioning and analysis of macromolecular structures.
Theobald, Douglas L; Wuttke, Deborah S
2006-09-01
THESEUS is a command line program for performing maximum likelihood (ML) superpositions and analysis of macromolecular structures. While conventional superpositioning methods use ordinary least-squares (LS) as the optimization criterion, ML superpositions provide substantially improved accuracy by down-weighting variable structural regions and by correcting for correlations among atoms. ML superpositioning is robust and insensitive to the specific atoms included in the analysis, and thus it does not require subjective pruning of selected variable atomic coordinates. Output includes both likelihood-based and frequentist statistics for accurate evaluation of the adequacy of a superposition and for reliable analysis of structural similarities and differences. THESEUS performs principal components analysis for analyzing the complex correlations found among atoms within a structural ensemble. ANSI C source code and selected binaries for various computing platforms are available under the GNU open source license from http://monkshood.colorado.edu/theseus/ or http://www.theseus3d.org.
Ben Abdessalem, A.; Jenson, F.; Calmon, P.
2016-02-01
This contribution provides an example of the possible advantages of adopting a Bayesian inversion approach to uncertainty quantification in nondestructive inspection methods. In such problem, the uncertainty associated to the random parameters is not always known and needs to be characterised from scattering signal measurements. The uncertainties may then correctly propagated in order to determine a reliable probability of detection curve. To this end, we establish a general Bayesian framework based on a non-parametric maximum likelihood function formulation and some priors from expert knowledge. However, the presented inverse problem is time-consuming and computationally intensive. To cope with this difficulty, we replace the real model by a surrogate one in order to speed-up the model evaluation and to make the problem to be computationally feasible for implementation. The least squares support vector regression is adopted as metamodelling technique due to its robustness to deal with non-linear problems. We illustrate the usefulness of this methodology through the control of tube with enclosed defect using ultrasonic inspection method.
Deformation of log-likelihood loss function for multiclass boosting.
Kanamori, Takafumi
2010-09-01
The purpose of this paper is to study loss functions in multiclass classification. In classification problems, the decision function is estimated by minimizing an empirical loss function, and then, the output label is predicted by using the estimated decision function. We propose a class of loss functions which is obtained by a deformation of the log-likelihood loss function. There are four main reasons why we focus on the deformed log-likelihood loss function: (1) this is a class of loss functions which has not been deeply investigated so far, (2) in terms of computation, a boosting algorithm with a pseudo-loss is available to minimize the proposed loss function, (3) the proposed loss functions provide a clear correspondence between the decision functions and conditional probabilities of output labels, (4) the proposed loss functions satisfy the statistical consistency of the classification error rate which is a desirable property in classification problems. Based on (3), we show that the deformed log-likelihood loss provides a model of mislabeling which is useful as a statistical model of medical diagnostics. We also propose a robust loss function against outliers in multiclass classification based on our approach. The robust loss function is a natural extension of the existing robust loss function for binary classification. A model of mislabeling and a robust loss function are useful to cope with noisy data. Some numerical studies are presented to show the robustness of the proposed loss function. A mathematical characterization of the deformed log-likelihood loss function is also presented. Copyright 2010 Elsevier Ltd. All rights reserved.
Menyoal Elaboration Likelihood Model (ELM) dan Teori Retorika
Yudi Perbawaningsih
2012-01-01
Abstract: Persuasion is a communication process to establish or change attitudes, which can be understood through theory of Rhetoric and theory of Elaboration Likelihood Model (ELM). This study elaborates these theories in a Public Lecture series which to persuade the students in choosing their concentration of study. The result shows that in term of persuasion effectiveness it is not quite relevant to separate the message and its source. The quality of source is determined by the quality of ...
Corporate governance effect on financial distress likelihood: Evidence from Spain
Directory of Open Access Journals (Sweden)
Montserrat Manzaneque
2016-01-01
Full Text Available The paper explores some mechanisms of corporate governance (ownership and board characteristics in Spanish listed companies and their impact on the likelihood of financial distress. An empirical study was conducted between 2007 and 2012 using a matched-pairs research design with 308 observations, with half of them classified as distressed and non-distressed. Based on the previous study by Pindado, Rodrigues, and De la Torre (2008, a broader concept of bankruptcy is used to define business failure. Employing several conditional logistic models, as well as to other previous studies on bankruptcy, the results confirm that in difficult situations prior to bankruptcy, the impact of board ownership and proportion of independent directors on business failure likelihood are similar to those exerted in more extreme situations. These results go one step further, to offer a negative relationship between board size and the likelihood of financial distress. This result is interpreted as a form of creating diversity and to improve the access to the information and resources, especially in contexts where the ownership is highly concentrated and large shareholders have a great power to influence the board structure. However, the results confirm that ownership concentration does not have a significant impact on financial distress likelihood in the Spanish context. It is argued that large shareholders are passive as regards an enhanced monitoring of management and, alternatively, they do not have enough incentives to hold back the financial distress. These findings have important implications in the Spanish context, where several changes in the regulatory listing requirements have been carried out with respect to corporate governance, and where there is no empirical evidence regarding this respect.
Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation
Rajiv D. Banker
1993-01-01
This paper provides a formal statistical basis for the efficiency evaluation techniques of data envelopment analysis (DEA). DEA estimators of the best practice monotone increasing and concave production function are shown to be also maximum likelihood estimators if the deviation of actual output from the efficient output is regarded as a stochastic variable with a monotone decreasing probability density function. While the best practice frontier estimator is biased below the theoretical front...
Multiple Improvements of Multiple Imputation Likelihood Ratio Tests
Chan, Kin Wai; Meng, Xiao-Li
2017-01-01
Multiple imputation (MI) inference handles missing data by first properly imputing the missing values $m$ times, and then combining the $m$ analysis results from applying a complete-data procedure to each of the completed datasets. However, the existing method for combining likelihood ratio tests has multiple defects: (i) the combined test statistic can be negative in practice when the reference null distribution is a standard $F$ distribution; (ii) it is not invariant to re-parametrization; ...
Maximum likelihood convolutional decoding (MCD) performance due to system losses
Webster, L.
1976-01-01
A model for predicting the computational performance of a maximum likelihood convolutional decoder (MCD) operating in a noisy carrier reference environment is described. This model is used to develop a subroutine that will be utilized by the Telemetry Analysis Program to compute the MCD bit error rate. When this computational model is averaged over noisy reference phase errors using a high-rate interpolation scheme, the results are found to agree quite favorably with experimental measurements.
Menyoal Elaboration Likelihood Model (ELM) Dan Teori Retorika
Perbawaningsih, Yudi
2012-01-01
: Persuasion is a communication process to establish or change attitudes, which can be understood through theory of Rhetoric and theory of Elaboration Likelihood Model (ELM). This study elaborates these theories in a Public Lecture series which to persuade the students in choosing their concentration of study. The result shows that in term of persuasion effectiveness it is not quite relevant to separate the message and its source. The quality of source is determined by the quality of the mess...
Penggunaan Elaboration Likelihood Model dalam Menganalisis Penerimaan Teknologi Informasi
vitrian, vitrian2
2010-01-01
This article discusses some technology acceptance models in an organization. Thorough analysis of how technology is acceptable help managers make any planning to implement new teachnology and make sure that new technology could enhance organization's performance. Elaboration Likelihood Model (ELM) is the one which sheds light on some behavioral factors in acceptance of information technology. The basic tenet of ELM states that human behavior in principle can be influenced through central r...
Statistical Bias in Maximum Likelihood Estimators of Item Parameters.
1982-04-01
34 a> E r’r~e r ,C Ie I# ne,..,.rVi rnd Id.,flfv b1 - bindk numb.r) I; ,t-i i-cd I ’ tiie bias in the maximum likelihood ,st i- i;, ’ t iIeiIrs in...NTC, IL 60088 Psychometric Laboratory University of North Carolina I ERIC Facility-Acquisitions Davie Hall 013A 4833 Rugby Avenue Chapel Hill, NC
Democracy, Autocracy and the Likelihood of International Conflict
Tangerås, Thomas
2008-01-01
This is a game-theoretic analysis of the link between regime type and international conflict. The democratic electorate can credibly punish the leader for bad conflict outcomes, whereas the autocratic selectorate cannot. For the fear of being thrown out of office, democratic leaders are (i) more selective about the wars they initiate and (ii) on average win more of the wars they start. Foreign policy behaviour is found to display strategic complementarities. The likelihood of interstate war, ...
de Queiroz, K; Poe, S
2001-06-01
Advocates of cladistic parsimony methods have invoked the philosophy of Karl Popper in an attempt to argue for the superiority of those methods over phylogenetic methods based on Ronald Fisher's statistical principle of likelihood. We argue that the concept of likelihood in general, and its application to problems of phylogenetic inference in particular, are highly compatible with Popper's philosophy. Examination of Popper's writings reveals that his concept of corroboration is, in fact, based on likelihood. Moreover, because probabilistic assumptions are necessary for calculating the probabilities that define Popper's corroboration, likelihood methods of phylogenetic inference--with their explicit probabilistic basis--are easily reconciled with his concept. In contrast, cladistic parsimony methods, at least as described by certain advocates of those methods, are less easily reconciled with Popper's concept of corroboration. If those methods are interpreted as lacking probabilistic assumptions, then they are incompatible with corroboration. Conversely, if parsimony methods are to be considered compatible with corroboration, then they must be interpreted as carrying implicit probabilistic assumptions. Thus, the non-probabilistic interpretation of cladistic parsimony favored by some advocates of those methods is contradicted by an attempt by the same authors to justify parsimony methods in terms of Popper's concept of corroboration. In addition to being compatible with Popperian corroboration, the likelihood approach to phylogenetic inference permits researchers to test the assumptions of their analytical methods (models) in a way that is consistent with Popper's ideas about the provisional nature of background knowledge.
Moment Conditions Selection Based on Adaptive Penalized Empirical Likelihood
Directory of Open Access Journals (Sweden)
Yunquan Song
2014-01-01
Full Text Available Empirical likelihood is a very popular method and has been widely used in the fields of artificial intelligence (AI and data mining as tablets and mobile application and social media dominate the technology landscape. This paper proposes an empirical likelihood shrinkage method to efficiently estimate unknown parameters and select correct moment conditions simultaneously, when the model is defined by moment restrictions in which some are possibly misspecified. We show that our method enjoys oracle-like properties; that is, it consistently selects the correct moment conditions and at the same time its estimator is as efficient as the empirical likelihood estimator obtained by all correct moment conditions. Moreover, unlike the GMM, our proposed method allows us to carry out confidence regions for the parameters included in the model without estimating the covariances of the estimators. For empirical implementation, we provide some data-driven procedures for selecting the tuning parameter of the penalty function. The simulation results show that the method works remarkably well in terms of correct moment selection and the finite sample properties of the estimators. Also, a real-life example is carried out to illustrate the new methodology.
Approximate maximum likelihood estimation for population genetic inference.
Bertl, Johanna; Ewing, Gregory; Kosiol, Carolin; Futschik, Andreas
2017-11-27
In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with growing computational power, sampling-based methods became popular. However, these methods such as Approximate Bayesian Computation (ABC) can be inefficient in high-dimensional problems. This led to the development of more sophisticated iterative estimation methods like particle filters. Here, we propose an alternative approach that is based on stochastic approximation. By moving along a simulated gradient or ascent direction, the algorithm produces a sequence of estimates that eventually converges to the maximum likelihood estimate, given a set of observed summary statistics. This strategy does not sample much from low-likelihood regions of the parameter space, and is fast, even when many summary statistics are involved. We put considerable efforts into providing tuning guidelines that improve the robustness and lead to good performance on problems with high-dimensional summary statistics and a low signal-to-noise ratio. We then investigate the performance of our resulting approach and study its properties in simulations. Finally, we re-estimate parameters describing the demographic history of Bornean and Sumatran orang-utans.
Caching and interpolated likelihoods: accelerating cosmological Monte Carlo Markov chains
Energy Technology Data Exchange (ETDEWEB)
Bouland, Adam; Easther, Richard; Rosenfeld, Katherine, E-mail: adam.bouland@aya.yale.edu, E-mail: richard.easther@yale.edu, E-mail: krosenfeld@cfa.harvard.edu [Department of Physics, Yale University, New Haven CT 06520 (United States)
2011-05-01
We describe a novel approach to accelerating Monte Carlo Markov Chains. Our focus is cosmological parameter estimation, but the algorithm is applicable to any problem for which the likelihood surface is a smooth function of the free parameters and computationally expensive to evaluate. We generate a high-order interpolating polynomial for the log-likelihood using the first points gathered by the Markov chains as a training set. This polynomial then accurately computes the majority of the likelihoods needed in the latter parts of the chains. We implement a simple version of this algorithm as a patch (InterpMC) to CosmoMC and show that it accelerates parameter estimatation by a factor of between two and four for well-converged chains. The current code is primarily intended as a ''proof of concept'', and we argue that there is considerable room for further performance gains. Unlike other approaches to accelerating parameter fits, we make no use of precomputed training sets or special choices of variables, and InterpMC is almost entirely transparent to the user.
Caching and interpolated likelihoods: accelerating cosmological Monte Carlo Markov chains
International Nuclear Information System (INIS)
Bouland, Adam; Easther, Richard; Rosenfeld, Katherine
2011-01-01
We describe a novel approach to accelerating Monte Carlo Markov Chains. Our focus is cosmological parameter estimation, but the algorithm is applicable to any problem for which the likelihood surface is a smooth function of the free parameters and computationally expensive to evaluate. We generate a high-order interpolating polynomial for the log-likelihood using the first points gathered by the Markov chains as a training set. This polynomial then accurately computes the majority of the likelihoods needed in the latter parts of the chains. We implement a simple version of this algorithm as a patch (InterpMC) to CosmoMC and show that it accelerates parameter estimatation by a factor of between two and four for well-converged chains. The current code is primarily intended as a ''proof of concept'', and we argue that there is considerable room for further performance gains. Unlike other approaches to accelerating parameter fits, we make no use of precomputed training sets or special choices of variables, and InterpMC is almost entirely transparent to the user
Maximum likelihood as a common computational framework in tomotherapy
International Nuclear Information System (INIS)
Olivera, G.H.; Shepard, D.M.; Reckwerdt, P.J.; Ruchala, K.; Zachman, J.; Fitchard, E.E.; Mackie, T.R.
1998-01-01
Tomotherapy is a dose delivery technique using helical or axial intensity modulated beams. One of the strengths of the tomotherapy concept is that it can incorporate a number of processes into a single piece of equipment. These processes include treatment optimization planning, dose reconstruction and kilovoltage/megavoltage image reconstruction. A common computational technique that could be used for all of these processes would be very appealing. The maximum likelihood estimator, originally developed for emission tomography, can serve as a useful tool in imaging and radiotherapy. We believe that this approach can play an important role in the processes of optimization planning, dose reconstruction and kilovoltage and/or megavoltage image reconstruction. These processes involve computations that require comparable physical methods. They are also based on equivalent assumptions, and they have similar mathematical solutions. As a result, the maximum likelihood approach is able to provide a common framework for all three of these computational problems. We will demonstrate how maximum likelihood methods can be applied to optimization planning, dose reconstruction and megavoltage image reconstruction in tomotherapy. Results for planning optimization, dose reconstruction and megavoltage image reconstruction will be presented. Strengths and weaknesses of the methodology are analysed. Future directions for this work are also suggested. (author)
A Fast Algorithm for Maximum Likelihood Estimation of Harmonic Chirp Parameters
DEFF Research Database (Denmark)
Jensen, Tobias Lindstrøm; Nielsen, Jesper Kjær; Jensen, Jesper Rindom
2017-01-01
. A statistically efficient estimator for extracting the parameters of the harmonic chirp model in additive white Gaussian noise is the maximum likelihood (ML) estimator which recently has been demonstrated to be robust to noise and accurate --- even when the model order is unknown. The main drawback of the ML......The analysis of (approximately) periodic signals is an important element in numerous applications. One generalization of standard periodic signals often occurring in practice are harmonic chirp signals where the instantaneous frequency increases/decreases linearly as a function of time...
International Nuclear Information System (INIS)
Croce, R P; Demma, Th; Longo, M; Marano, S; Matta, V; Pierro, V; Pinto, I M
2003-01-01
The cumulative distribution of the supremum of a set (bank) of correlators is investigated in the context of maximum likelihood detection of gravitational wave chirps from coalescing binaries with unknown parameters. Accurate (lower-bound) approximants are introduced based on a suitable generalization of previous results by Mohanty. Asymptotic properties (in the limit where the number of correlators goes to infinity) are highlighted. The validity of numerical simulations made on small-size banks is extended to banks of any size, via a Gaussian correlation inequality
Directory of Open Access Journals (Sweden)
Maja Olsbjerg
2015-10-01
Full Text Available Item response theory models are often applied when a number items are used to measure a unidimensional latent variable. Originally proposed and used within educational research, they are also used when focus is on physical functioning or psychological wellbeing. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for repeated measurements. This paper describes a SAS macro that fits two-dimensional polytomous Rasch models using a specification of the model that is sufficiently flexible to accommodate longitudinal Rasch models. The macro estimates item parameters using marginal maximum likelihood estimation. A graphical presentation of item characteristic curves is included.
Inverse Problems and Uncertainty Quantification
Litvinenko, Alexander
2014-01-06
In a Bayesian setting, inverse problems and uncertainty quantification (UQ) - the propagation of uncertainty through a computational (forward) modelare strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. This is especially the case as together with a functional or spectral approach for the forward UQ there is no need for time- consuming and slowly convergent Monte Carlo sampling. The developed sampling- free non-linear Bayesian update is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisa- tion to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and quadratic Bayesian update on the small but taxing example of the chaotic Lorenz 84 model, where we experiment with the influence of different observation or measurement operators on the update.
Inverse Problems and Uncertainty Quantification
Litvinenko, Alexander; Matthies, Hermann G.
2014-01-01
In a Bayesian setting, inverse problems and uncertainty quantification (UQ) - the propagation of uncertainty through a computational (forward) modelare strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. This is especially the case as together with a functional or spectral approach for the forward UQ there is no need for time- consuming and slowly convergent Monte Carlo sampling. The developed sampling- free non-linear Bayesian update is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisa- tion to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and quadratic Bayesian update on the small but taxing example of the chaotic Lorenz 84 model, where we experiment with the influence of different observation or measurement operators on the update.
Inverse problems and uncertainty quantification
Litvinenko, Alexander
2013-12-18
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)— the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. This is especially the case as together with a functional or spectral approach for the forward UQ there is no need for time- consuming and slowly convergent Monte Carlo sampling. The developed sampling- free non-linear Bayesian update is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisa- tion to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and quadratic Bayesian update on the small but taxing example of the chaotic Lorenz 84 model, where we experiment with the influence of different observation or measurement operators on the update.
Confronting uncertainty in wildlife management: performance of grizzly bear management.
Artelle, Kyle A; Anderson, Sean C; Cooper, Andrew B; Paquet, Paul C; Reynolds, John D; Darimont, Chris T
2013-01-01
Scientific management of wildlife requires confronting the complexities of natural and social systems. Uncertainty poses a central problem. Whereas the importance of considering uncertainty has been widely discussed, studies of the effects of unaddressed uncertainty on real management systems have been rare. We examined the effects of outcome uncertainty and components of biological uncertainty on hunt management performance, illustrated with grizzly bears (Ursus arctos horribilis) in British Columbia, Canada. We found that both forms of uncertainty can have serious impacts on management performance. Outcome uncertainty alone--discrepancy between expected and realized mortality levels--led to excess mortality in 19% of cases (population-years) examined. Accounting for uncertainty around estimated biological parameters (i.e., biological uncertainty) revealed that excess mortality might have occurred in up to 70% of cases. We offer a general method for identifying targets for exploited species that incorporates uncertainty and maintains the probability of exceeding mortality limits below specified thresholds. Setting targets in our focal system using this method at thresholds of 25% and 5% probability of overmortality would require average target mortality reductions of 47% and 81%, respectively. Application of our transparent and generalizable framework to this or other systems could improve management performance in the presence of uncertainty.
Confronting uncertainty in wildlife management: performance of grizzly bear management.
Directory of Open Access Journals (Sweden)
Kyle A Artelle
Full Text Available Scientific management of wildlife requires confronting the complexities of natural and social systems. Uncertainty poses a central problem. Whereas the importance of considering uncertainty has been widely discussed, studies of the effects of unaddressed uncertainty on real management systems have been rare. We examined the effects of outcome uncertainty and components of biological uncertainty on hunt management performance, illustrated with grizzly bears (Ursus arctos horribilis in British Columbia, Canada. We found that both forms of uncertainty can have serious impacts on management performance. Outcome uncertainty alone--discrepancy between expected and realized mortality levels--led to excess mortality in 19% of cases (population-years examined. Accounting for uncertainty around estimated biological parameters (i.e., biological uncertainty revealed that excess mortality might have occurred in up to 70% of cases. We offer a general method for identifying targets for exploited species that incorporates uncertainty and maintains the probability of exceeding mortality limits below specified thresholds. Setting targets in our focal system using this method at thresholds of 25% and 5% probability of overmortality would require average target mortality reductions of 47% and 81%, respectively. Application of our transparent and generalizable framework to this or other systems could improve management performance in the presence of uncertainty.
Impacts of Korea's Exchange Rate Uncertainty on Exports
Directory of Open Access Journals (Sweden)
Kwon Sik Kim
2003-12-01
Full Text Available This paper examines the effects of two types of uncertainty related to the real effective exchange rate (REER in Korea for export trends. To decompose uncertainties into two types of component, I propose an advanced generalized Markov switching model, as developed by Hamilton (1989 and then expanded by Kim and Kim (1996. The proposed model is useful in uncovering two sources of uncertainty: the permanent component of REER and the purely transitory component. I think that the two types of uncertainties have a different effect on export trends in Korea. The transitory component of REER has no effect on the export trend at 5-percent significance, but the permanent component has an effect at this level. In addition, the degree of uncertainty, consisting of low, medium and high uncertainty in the permanent component, and low, medium and high uncertainty in transitory component of REER, also has different effects on export trends in Korea. Only high uncertainty in permanent components effects export trends. The results show that when the policy authority intends to prevent the shrinkage of exports due to the deepening of uncertainties in the foreign exchange market, the economic impacts of its intervention could appear differently according to the characteristics and degree of the uncertainties. Therefore, they imply that its economic measures, which could not grasp the sources of uncertainties properly, may even bring economic costs.
Uncertainty in adaptive capacity
International Nuclear Information System (INIS)
Neil Adger, W.; Vincent, K.
2005-01-01
The capacity to adapt is a critical element of the process of adaptation: it is the vector of resources that represent the asset base from which adaptation actions can be made. Adaptive capacity can in theory be identified and measured at various scales, from the individual to the nation. The assessment of uncertainty within such measures comes from the contested knowledge domain and theories surrounding the nature of the determinants of adaptive capacity and the human action of adaptation. While generic adaptive capacity at the national level, for example, is often postulated as being dependent on health, governance and political rights, and literacy, and economic well-being, the determinants of these variables at national levels are not widely understood. We outline the nature of this uncertainty for the major elements of adaptive capacity and illustrate these issues with the example of a social vulnerability index for countries in Africa. (authors)
International Nuclear Information System (INIS)
Laval, Katia; Laval, Guy
2013-01-01
Like meteorology, climatology is not an exact science: climate change forecasts necessarily include a share of uncertainty. It is precisely this uncertainty which is brandished and exploited by the opponents to the global warming theory to put into question the estimations of its future consequences. Is it legitimate to predict the future using the past climate data (well documented up to 100000 years BP) or the climates of other planets, taking into account the impreciseness of the measurements and the intrinsic complexity of the Earth's machinery? How is it possible to model a so huge and interwoven system for which any exact description has become impossible? Why water and precipitations play such an important role in local and global forecasts, and how should they be treated? This book written by two physicists answers with simpleness these delicate questions in order to give anyone the possibility to build his own opinion about global warming and the need to act rapidly
Interpretation of uncertainty relations for three or more observables
International Nuclear Information System (INIS)
Shirokov, M.I.
2003-01-01
Conventional quantum uncertainty relations (URs) contain dispersions of two observables. Generalized URs are known which contain three or more dispersions. They are derived here starting with suitable generalized Cauchy inequalities. It is shown what new information the generalized URs provide. Similar interpretation is given to generalized Cauchy inequalities
International Nuclear Information System (INIS)
Martens, Hans.
1991-01-01
The subject of this thesis is the uncertainty principle (UP). The UP is one of the most characteristic points of differences between quantum and classical mechanics. The starting point of this thesis is the work of Niels Bohr. Besides the discussion the work is also analyzed. For the discussion of the different aspects of the UP the formalism of Davies and Ludwig is used instead of the more commonly used formalism of Neumann and Dirac. (author). 214 refs.; 23 figs
Uncertainty in artificial intelligence
Shachter, RD; Henrion, M; Lemmer, JF
1990-01-01
This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty.A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language und
Decision Making Under Uncertainty
2010-11-01
A sound approach to rational decision making requires a decision maker to establish decision objectives, identify alternatives, and evaluate those...often violate the axioms of rationality when making decisions under uncertainty. The systematic description of such observations may lead to the...which leads to “anchoring” on the initial value. The fact that individuals have been shown to deviate from rationality when making decisions
Economic uncertainty principle?
Alexander Harin
2006-01-01
The economic principle of (hidden) uncertainty is presented. New probability formulas are offered. Examples of solutions of three types of fundamental problems are reviewed.; Principe d'incertitude économique? Le principe économique d'incertitude (cachée) est présenté. De nouvelles formules de chances sont offertes. Les exemples de solutions des trois types de problèmes fondamentaux sont reconsidérés.
Citizen Candidates Under Uncertainty
Eguia, Jon X.
2005-01-01
In this paper we make two contributions to the growing literature on "citizen-candidate" models of representative democracy. First, we add uncertainty about the total vote count. We show that in a society with a large electorate, where the outcome of the election is uncertain and where winning candidates receive a large reward from holding office, there will be a two-candidate equilibrium and no equilibria with a single candidate. Second, we introduce a new concept of equilibrium, which we te...
Calibration Under Uncertainty.
Energy Technology Data Exchange (ETDEWEB)
Swiler, Laura Painton; Trucano, Timothy Guy
2005-03-01
This report is a white paper summarizing the literature and different approaches to the problem of calibrating computer model parameters in the face of model uncertainty. Model calibration is often formulated as finding the parameters that minimize the squared difference between the model-computed data (the predicted data) and the actual experimental data. This approach does not allow for explicit treatment of uncertainty or error in the model itself: the model is considered the %22true%22 deterministic representation of reality. While this approach does have utility, it is far from an accurate mathematical treatment of the true model calibration problem in which both the computed data and experimental data have error bars. This year, we examined methods to perform calibration accounting for the error in both the computer model and the data, as well as improving our understanding of its meaning for model predictability. We call this approach Calibration under Uncertainty (CUU). This talk presents our current thinking on CUU. We outline some current approaches in the literature, and discuss the Bayesian approach to CUU in detail.
Participation under Uncertainty
International Nuclear Information System (INIS)
Boudourides, Moses A.
2003-01-01
This essay reviews a number of theoretical perspectives about uncertainty and participation in the present-day knowledge-based society. After discussing the on-going reconfigurations of science, technology and society, we examine how appropriate for policy studies are various theories of social complexity. Post-normal science is such an example of a complexity-motivated approach, which justifies civic participation as a policy response to an increasing uncertainty. But there are different categories and models of uncertainties implying a variety of configurations of policy processes. A particular role in all of them is played by expertise whose democratization is an often-claimed imperative nowadays. Moreover, we discuss how different participatory arrangements are shaped into instruments of policy-making and framing regulatory processes. As participation necessitates and triggers deliberation, we proceed to examine the role and the barriers of deliberativeness. Finally, we conclude by referring to some critical views about the ultimate assumptions of recent European policy frameworks and the conceptions of civic participation and politicization that they invoke
Uncertainty analysis techniques
International Nuclear Information System (INIS)
Marivoet, J.; Saltelli, A.; Cadelli, N.
1987-01-01
The origin of the uncertainty affecting Performance Assessments, as well as their propagation to dose and risk results is discussed. The analysis is focused essentially on the uncertainties introduced by the input parameters, the values of which may range over some orders of magnitude and may be given as probability distribution function. The paper briefly reviews the existing sampling techniques used for Monte Carlo simulations and the methods for characterizing the output curves, determining their convergence and confidence limits. Annual doses, expectation values of the doses and risks are computed for a particular case of a possible repository in clay, in order to illustrate the significance of such output characteristics as the mean, the logarithmic mean and the median as well as their ratios. The report concludes that provisionally, due to its better robustness, such estimation as the 90th percentile may be substituted to the arithmetic mean for comparison of the estimated doses with acceptance criteria. In any case, the results obtained through Uncertainty Analyses must be interpreted with caution as long as input data distribution functions are not derived from experiments reasonably reproducing the situation in a well characterized repository and site
Deterministic uncertainty analysis
International Nuclear Information System (INIS)
Worley, B.A.
1987-12-01
This paper presents a deterministic uncertainty analysis (DUA) method for calculating uncertainties that has the potential to significantly reduce the number of computer runs compared to conventional statistical analysis. The method is based upon the availability of derivative and sensitivity data such as that calculated using the well known direct or adjoint sensitivity analysis techniques. Formation of response surfaces using derivative data and the propagation of input probability distributions are discussed relative to their role in the DUA method. A sample problem that models the flow of water through a borehole is used as a basis to compare the cumulative distribution function of the flow rate as calculated by the standard statistical methods and the DUA method. Propogation of uncertainties by the DUA method is compared for ten cases in which the number of reference model runs was varied from one to ten. The DUA method gives a more accurate representation of the true cumulative distribution of the flow rate based upon as few as two model executions compared to fifty model executions using a statistical approach. 16 refs., 4 figs., 5 tabs
Optical Model and Cross Section Uncertainties
Energy Technology Data Exchange (ETDEWEB)
Herman,M.W.; Pigni, M.T.; Dietrich, F.S.; Oblozinsky, P.
2009-10-05
Distinct minima and maxima in the neutron total cross section uncertainties were observed in model calculations using spherical optical potential. We found this oscillating structure to be a general feature of quantum mechanical wave scattering. Specifically, we analyzed neutron interaction with 56Fe from 1 keV up to 65 MeV, and investigated physical origin of the minima.We discuss their potential importance for practical applications as well as the implications for the uncertainties in total and absorption cross sections.
Field-theoretical space-uncertainty description
International Nuclear Information System (INIS)
Papp, E.; Micu, C.A.
1980-01-01
An approach has been given to define both the nonzero minimum value of the space-uncertainty evaluation and of the upper rest-mass bound of the involved particles. In this respect there are analysed the space-uncertainties wich emerge both from the regularised quantum field-theory and high-energy behaviour. In such conditions there are involved particles wich are effectively nonpoint ones. It can be then concluded that the dualism broglien between waves and nonpoint particles is actually involved, now in more general terms
Majorization uncertainty relations for mixed quantum states
Puchała, Zbigniew; Rudnicki, Łukasz; Krawiec, Aleksandra; Życzkowski, Karol
2018-04-01
Majorization uncertainty relations are generalized for an arbitrary mixed quantum state ρ of a finite size N. In particular, a lower bound for the sum of two entropies characterizing the probability distributions corresponding to measurements with respect to two arbitrary orthogonal bases is derived in terms of the spectrum of ρ and the entries of a unitary matrix U relating both bases. The results obtained can also be formulated for two measurements performed on a single subsystem of a bipartite system described by a pure state, and consequently expressed as an uncertainty relation for the sum of conditional entropies.
Communicating likelihoods and probabilities in forecasts of volcanic eruptions
Doyle, Emma E. H.; McClure, John; Johnston, David M.; Paton, Douglas
2014-02-01
The issuing of forecasts and warnings of natural hazard events, such as volcanic eruptions, earthquake aftershock sequences and extreme weather often involves the use of probabilistic terms, particularly when communicated by scientific advisory groups to key decision-makers, who can differ greatly in relative expertise and function in the decision making process. Recipients may also differ in their perception of relative importance of political and economic influences on interpretation. Consequently, the interpretation of these probabilistic terms can vary greatly due to the framing of the statements, and whether verbal or numerical terms are used. We present a review from the psychology literature on how the framing of information influences communication of these probability terms. It is also unclear as to how people rate their perception of an event's likelihood throughout a time frame when a forecast time window is stated. Previous research has identified that, when presented with a 10-year time window forecast, participants viewed the likelihood of an event occurring ‘today’ as being of less than that in year 10. Here we show that this skew in perception also occurs for short-term time windows (under one week) that are of most relevance for emergency warnings. In addition, unlike the long-time window statements, the use of the phrasing “within the next…” instead of “in the next…” does not mitigate this skew, nor do we observe significant differences between the perceived likelihoods of scientists and non-scientists. This finding suggests that effects occurring due to the shorter time window may be ‘masking’ any differences in perception due to wording or career background observed for long-time window forecasts. These results have implications for scientific advice, warning forecasts, emergency management decision-making, and public information as any skew in perceived event likelihood towards the end of a forecast time window may result in
Comparisons of likelihood and machine learning methods of individual classification
Guinand, B.; Topchy, A.; Page, K.S.; Burnham-Curtis, M. K.; Punch, W.F.; Scribner, K.T.
2002-01-01
Classification methods used in machine learning (e.g., artificial neural networks, decision trees, and k-nearest neighbor clustering) are rarely used with population genetic data. We compare different nonparametric machine learning techniques with parametric likelihood estimations commonly employed in population genetics for purposes of assigning individuals to their population of origin (“assignment tests”). Classifier accuracy was compared across simulated data sets representing different levels of population differentiation (low and high FST), number of loci surveyed (5 and 10), and allelic diversity (average of three or eight alleles per locus). Empirical data for the lake trout (Salvelinus namaycush) exhibiting levels of population differentiation comparable to those used in simulations were examined to further evaluate and compare classification methods. Classification error rates associated with artificial neural networks and likelihood estimators were lower for simulated data sets compared to k-nearest neighbor and decision tree classifiers over the entire range of parameters considered. Artificial neural networks only marginally outperformed the likelihood method for simulated data (0–2.8% lower error rates). The relative performance of each machine learning classifier improved relative likelihood estimators for empirical data sets, suggesting an ability to “learn” and utilize properties of empirical genotypic arrays intrinsic to each population. Likelihood-based estimation methods provide a more accessible option for reliable assignment of individuals to the population of origin due to the intricacies in development and evaluation of artificial neural networks. In recent years, characterization of highly polymorphic molecular markers such as mini- and microsatellites and development of novel methods of analysis have enabled researchers to extend investigations of ecological and evolutionary processes below the population level to the level of
Ershadi, Ali
2013-05-01
The influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model. The Bayesian approach allows for an explicit quantification of the uncertainties in input variables: a source of error generally ignored in surface heat flux estimation. An application using field measurements from the Soil Moisture Experiment 2002 is presented. The spatial variability of selected input meteorological variables in a multitower site is used to formulate the prior estimates for the sampling uncertainties, and the likelihood function is formulated assuming Gaussian errors in the SEBS model. Land surface temperature, air temperature, and wind speed were estimated by sampling their posterior distribution using a Markov chain Monte Carlo algorithm. Results verify that Bayesian-inferred air temperature and wind speed were generally consistent with those observed at the towers, suggesting that local observations of these variables were spatially representative. Uncertainties in the land surface temperature appear to have the strongest effect on the estimated sensible heat flux, with Bayesian-inferred values differing by up to ±5°C from the observed data. These differences suggest that the footprint of the in situ measured land surface temperature is not representative of the larger-scale variability. As such, these measurements should be used with caution in the calculation of surface heat fluxes and highlight the importance of capturing the spatial variability in the land surface temperature: particularly, for remote sensing retrieval algorithms that use this variable for flux estimation.
Image restoration, uncertainty, and information.
Yu, F T
1969-01-01
Some of the physical interpretations about image restoration are discussed. From the theory of information the unrealizability of an inverse filter can be explained by degradation of information, which is due to distortion on the recorded image. The image restoration is a time and space problem, which can be recognized from the theory of relativity (the problem of image restoration is related to Heisenberg's uncertainty principle in quantum mechanics). A detailed discussion of the relationship between information and energy is given. Two general results may be stated: (1) the restoration of the image from the distorted signal is possible only if it satisfies the detectability condition. However, the restored image, at the best, can only approach to the maximum allowable time criterion. (2) The restoration of an image by superimposing the distorted signal (due to smearing) is a physically unrealizable method. However, this restoration procedure may be achieved by the expenditure of an infinite amount of energy.
Visualizing uncertainty : Towards a better understanding of weather forecasts
Toet, A.; Tak, S.; Erp, J.B.F. van
2016-01-01
Uncertainty visualizations are increasingly used in communications to the general public. A well-known example is the weather forecast. Rather than providing an exact temperature value, weather forecasts often show the range in which the temperature will lie. But uncertainty visualizations are also
Methodologies of Uncertainty Propagation Calculation
International Nuclear Information System (INIS)
Chojnacki, Eric
2002-01-01
After recalling the theoretical principle and the practical difficulties of the methodologies of uncertainty propagation calculation, the author discussed how to propagate input uncertainties. He said there were two kinds of input uncertainty: - variability: uncertainty due to heterogeneity, - lack of knowledge: uncertainty due to ignorance. It was therefore necessary to use two different propagation methods. He demonstrated this in a simple example which he generalised, treating the variability uncertainty by the probability theory and the lack of knowledge uncertainty by the fuzzy theory. He cautioned, however, against the systematic use of probability theory which may lead to unjustifiable and illegitimate precise answers. Mr Chojnacki's conclusions were that the importance of distinguishing variability and lack of knowledge increased as the problem was getting more and more complex in terms of number of parameters or time steps, and that it was necessary to develop uncertainty propagation methodologies combining probability theory and fuzzy theory
LOFT uncertainty-analysis methodology
International Nuclear Information System (INIS)
Lassahn, G.D.
1983-01-01
The methodology used for uncertainty analyses of measurements in the Loss-of-Fluid Test (LOFT) nuclear-reactor-safety research program is described and compared with other methodologies established for performing uncertainty analyses
LOFT uncertainty-analysis methodology
International Nuclear Information System (INIS)
Lassahn, G.D.
1983-01-01
The methodology used for uncertainty analyses of measurements in the Loss-of-Fluid Test (LOFT) nuclear reactor safety research program is described and compared with other methodologies established for performing uncertainty analyses
Quasi-likelihood generalized linear regression analysis of fatality risk data.
2009-01-01
Transportation-related fatality risks is a function of many interacting human, vehicle, and environmental factors. Statistically valid analysis of such data is challenged both by the complexity of plausible structural models relating fatality rates t...
Uncertainty analysis for Ulysses safety evaluation report
International Nuclear Information System (INIS)
Frank, M.V.
1991-01-01
As part of the effort to review the Ulysses Final Safety Analysis Report and to understand the risk of plutonium release from the Ulysses spacecraft General Purpose Heat Source---Radioisotope Thermal Generator (GPHS-RTG), the Interagency Nuclear Safety Review Panel (INSRP) and the author performed an integrated, quantitative analysis of the uncertainties of the calculated risk of plutonium release from Ulysses. Using state-of-art probabilistic risk assessment technology, the uncertainty analysis accounted for both variability and uncertainty of the key parameters of the risk analysis. The results show that INSRP had high confidence that risk of fatal cancers from potential plutonium release associated with calculated launch and deployment accident scenarios is low
Assignment of uncertainties to scientific data
International Nuclear Information System (INIS)
Froehner, F.H.
1994-01-01
Long-standing problems of uncertainty assignment to scientific data came into a sharp focus in recent years when uncertainty information ('covariance files') had to be added to application-oriented large libraries of evaluated nuclear data such as ENDF and JEF. Question arouse about the best way to express uncertainties, the meaning of statistical and systematic errors, the origin of correlation and construction of covariance matrices, the combination of uncertain data from different sources, the general usefulness of results that are strictly valid only for Gaussian or only for linear statistical models, etc. Conventional statistical theory is often unable to give unambiguous answers, and tends to fail when statistics is bad so that prior information becomes crucial. Modern probability theory, on the other hand, incorporating decision information becomes group-theoretic results, is shown to provide straight and unique answers to such questions, and to deal easily with prior information and small samples. (author). 10 refs