The confounding effect of population structure on bayesian skyline plot inferences of demographic history

DEFF Research Database (Denmark)

Heller, Rasmus; Chikhi, Lounes; Siegismund, Hans

2013-01-01

Many coalescent-based methods aiming to infer the demographic history of populations assume a single, isolated and panmictic population (i.e. a Wright-Fisher model). While this assumption may be reasonable under many conditions, several recent studies have shown that the results can be misleading...... when it is violated. Among the most widely applied demographic inference methods are Bayesian skyline plots (BSPs), which are used across a range of biological fields. Violations of the panmixia assumption are to be expected in many biological systems, but the consequences for skyline plot inferences...... the best scheme for inferring demographic change over a typical time scale. Analyses of data from a structured African buffalo population demonstrate how BSP results can be strengthened by simulations. We recommend that sample selection should be carefully considered in relation to population structure...

Probability biases as Bayesian inference

Directory of Open Access Journals (Sweden)

Andre; C. R. Martins

2006-11-01

Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.

Coalescence of GaAs on (001) Si nano-trenches based on three-stage epitaxial lateral overgrowth

Energy Technology Data Exchange (ETDEWEB)

He, Yunrui; Wang, Jun, E-mail: wangjun12@bupt.edu.cn; Hu, Haiyang; Wang, Qi; Huang, Yongqing; Ren, Xiaomin [State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876 (China)

2015-05-18

The coalescence of selective area grown GaAs regions has been performed on patterned 1.8 μm GaAs buffer layer on Si via metal-organic chemical vapor deposition. We propose a promising method of three-stage epitaxial lateral overgrowth (ELO) to achieve uniform coalescence and flat surface. Rough surface caused by the coalescence of different growth fronts is smoothened by this method. Low root-mean-square surface roughness of 6.29 nm has been obtained on a 410-nm-thick coalesced ELO GaAs layer. Cross-sectional transmission electron microscope study shows that the coalescence of different growth fronts will induce some new dislocations. However, the coalescence-induced dislocations tend to mutually annihilate and only a small part of them reach the GaAs surface. High optical quality of the ELO GaAs layer has been confirmed by low temperature (77 K) photoluminescence measurements. This research promises a very large scale integration platform for the monolithic integration of GaAs-based device on Si.

Bayesian computation with R

CERN Document Server

Albert, Jim

2009-01-01

There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The earl

Prediction of crack coalescence of steam generator tubes in nuclear power plants

International Nuclear Information System (INIS)

Abou-Hanna, Jeries; McGreevy, Timothy E.; Majumdar, Saurin

2004-01-01

Prediction of failure pressures of cracked steam generator tubes of nuclear power plants is an important ingredient in scheduling inspection and repair of tubes. Prediction is usually based on nondestructive evaluation (NDE) of cracks. NDE often reveals two neighboring cracks. If the cracks interact, the tube pressure under which the ligament between the two cracks fails could be much lower than the critical burst pressure of an individual equivalent crack. The ability to accurately predict the ligament failure pressure, called ''coalescence pressure,'' is important. The failure criterion was established by nonlinear finite element model (FEM) analyses of coalescence of two 100% through-wall collinear cracks. The ligament failure is precipitated by local instability of the ligament under plane strain conditions. As a result of this local instability, the ligament thickness in the radial direction decreases abruptly with pressure. Good correlation of FEM analysis results with experimental data obtained at Argonne National Laboratory's Energy Technology Division demonstrated that nonlinear FEM analyses are capable of predicting the coalescence pressure accurately for 100% through-wall cracks. This failure criterion and FEA work have been extended to axial cracks of varying ligament width, crack length, and cases where cracks are offset by axial or circumferential ligaments

Phylogenetic analysis at deep timescales: unreliable gene trees, bypassed hidden support, and the coalescence/concatalescence conundrum.

Science.gov (United States)

Gatesy, John; Springer, Mark S

2014-11-01

Large datasets are required to solve difficult phylogenetic problems that are deep in the Tree of Life. Currently, two divergent systematic methods are commonly applied to such datasets: the traditional supermatrix approach (= concatenation) and "shortcut" coalescence (= coalescence methods wherein gene trees and the species tree are not co-estimated). When applied to ancient clades, these contrasting frameworks often produce congruent results, but in recent phylogenetic analyses of Placentalia (placental mammals), this is not the case. A recent series of papers has alternatively disputed and defended the utility of shortcut coalescence methods at deep phylogenetic scales. Here, we examine this exchange in the context of published phylogenomic data from Mammalia; in particular we explore two critical issues - the delimitation of data partitions ("genes") in coalescence analysis and hidden support that emerges with the combination of such partitions in phylogenetic studies. Hidden support - increased support for a clade in combined analysis of all data partitions relative to the support evident in separate analyses of the various data partitions, is a hallmark of the supermatrix approach and a primary rationale for concatenating all characters into a single matrix. In the most extreme cases of hidden support, relationships that are contradicted by all gene trees are supported when all of the genes are analyzed together. A valid fear is that shortcut coalescence methods might bypass or distort character support that is hidden in individual loci because small gene fragments are analyzed in isolation. Given the extensive systematic database for Mammalia, the assumptions and applicability of shortcut coalescence methods can be assessed with rigor to complement a small but growing body of simulation work that has directly compared these methods to concatenation. We document several remarkable cases of hidden support in both supermatrix and coalescence paradigms and argue

Hydrodynamic effects on coalescence.

Energy Technology Data Exchange (ETDEWEB)

Dimiduk, Thomas G.; Bourdon, Christopher Jay; Grillet, Anne Mary; Baer, Thomas A.; de Boer, Maarten Pieter; Loewenberg, Michael (Yale University, New Haven, CT); Gorby, Allen D.; Brooks, Carlton, F.

2006-10-01

The goal of this project was to design, build and test novel diagnostics to probe the effect of hydrodynamic forces on coalescence dynamics. Our investigation focused on how a drop coalesces onto a flat surface which is analogous to two drops coalescing, but more amenable to precise experimental measurements. We designed and built a flow cell to create an axisymmetric compression flow which brings a drop onto a flat surface. A computer-controlled system manipulates the flow to steer the drop and maintain a symmetric flow. Particle image velocimetry was performed to confirm that the control system was delivering a well conditioned flow. To examine the dynamics of the coalescence, we implemented an interferometry capability to measure the drainage of the thin film between the drop and the surface during the coalescence process. A semi-automated analysis routine was developed which converts the dynamic interferogram series into drop shape evolution data.

Bayesian-based localization in inhomogeneous transmission media

DEFF Research Database (Denmark)

Nadimi, E. S.; Blanes-Vidal, V.; Johansen, P. M.

2013-01-01

In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network with heteroge......In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network...... with heterogeneous transmission medium as a function of the given RSS measurements and the characteristics of the heterogeneous medium. The results of this study show that the localization mean square error (MSE) of the Bayesian-based method outperformed all other existing localization approaches. © 2013 ACM....

Estimating the Effective Sample Size of Tree Topologies from Bayesian Phylogenetic Analyses

Science.gov (United States)

Lanfear, Robert; Hua, Xia; Warren, Dan L.

2016-01-01

Bayesian phylogenetic analyses estimate posterior distributions of phylogenetic tree topologies and other parameters using Markov chain Monte Carlo (MCMC) methods. Before making inferences from these distributions, it is important to assess their adequacy. To this end, the effective sample size (ESS) estimates how many truly independent samples of a given parameter the output of the MCMC represents. The ESS of a parameter is frequently much lower than the number of samples taken from the MCMC because sequential samples from the chain can be non-independent due to autocorrelation. Typically, phylogeneticists use a rule of thumb that the ESS of all parameters should be greater than 200. However, we have no method to calculate an ESS of tree topology samples, despite the fact that the tree topology is often the parameter of primary interest and is almost always central to the estimation of other parameters. That is, we lack a method to determine whether we have adequately sampled one of the most important parameters in our analyses. In this study, we address this problem by developing methods to estimate the ESS for tree topologies. We combine these methods with two new diagnostic plots for assessing posterior samples of tree topologies, and compare their performance on simulated and empirical data sets. Combined, the methods we present provide new ways to assess the mixing and convergence of phylogenetic tree topologies in Bayesian MCMC analyses. PMID:27435794

SODIUM ALUMINOSILICATE FOULING AND CLEANING OF DECONTAMINATED SALT SOLUTION COALESCERS

International Nuclear Information System (INIS)

Poirier, M.; Thomas Peters, T.; Fernando Fondeur, F.; Samuel Fink, S.

2008-01-01

During initial non-radioactive operations at the Modular Caustic Side Solvent Extraction Unit (MCU), the pressure drop across the decontaminated salt solution coalescer reached ∼10 psi while processing ∼1250 gallons of salt solution, indicating possible fouling or plugging of the coalescer. An analysis of the feed solution and the 'plugged coalescer' concluded that the plugging was due to sodium aluminosilicate solids. MCU personnel requested Savannah River National Laboratory (SRNL) to investigate the formation of the sodium aluminosilicate solids (NAS) and the impact of the solids on the decontaminated salt solution coalescer. Researchers performed developmental testing of the cleaning protocols with a bench-scale coalescer container 1-inch long segments of a new coalescer element fouled using simulant solution. In addition, the authors obtained a 'plugged' Decontaminated Salt Solution coalescer from non-radioactive testing in the MCU and cleaned it according to the proposed cleaning procedure. Conclusions from this testing include the following: (1) Testing with the bench-scale coalescer showed an increase in pressure drop from solid particles, but the increase was not as large as observed at MCU. (2) Cleaning the bench-scale coalescer with nitric acid reduced the pressure drop and removed a large amount of solid particles (11 g of bayerite if all aluminum is present in that form or 23 g of sodium aluminosilicate if all silicon is present in that form). (3) Based on analysis of the cleaning solutions from bench-scale test, the 'dirt capacity' of a 40 inch coalescer for the NAS solids tested is calculated as 450-950 grams. (4) Cleaning the full-scale coalescer with nitric acid reduced the pressure drop and removed a large amount of solid particles (60 g of aluminum and 5 g of silicon). (5) Piping holdup in the full-scale coalescer system caused the pH to differ from the target value. Comparable hold-up in the facility could lead to less effective cleaning and

Fast "coalescent" simulation

Directory of Open Access Journals (Sweden)

Wall Jeff D

2006-03-01

Full Text Available Abstract Background The amount of genome-wide molecular data is increasing rapidly, as is interest in developing methods appropriate for such data. There is a consequent increasing need for methods that are able to efficiently simulate such data. In this paper we implement the sequentially Markovian coalescent algorithm described by McVean and Cardin and present a further modification to that algorithm which slightly improves the closeness of the approximation to the full coalescent model. The algorithm ignores a class of recombination events known to affect the behavior of the genealogy of the sample, but which do not appear to affect the behavior of generated samples to any substantial degree. Results We show that our software is able to simulate large chromosomal regions, such as those appropriate in a consideration of genome-wide data, in a way that is several orders of magnitude faster than existing coalescent algorithms. Conclusion This algorithm provides a useful resource for those needing to simulate large quantities of data for chromosomal-length regions using an approach that is much more efficient than traditional coalescent models.

Bayesian natural language semantics and pragmatics

CERN Document Server

Zeevat, Henk

2015-01-01

The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice's contributions to pragmatics or in interpretation by abduction.

An Experimental Study of the Fracture Coalescence Behaviour of Brittle Sandstone Specimens Containing Three Fissures

Science.gov (United States)

Yang, S. Q.; Yang, D. S.; Jing, H. W.; Li, Y. H.; Wang, S. Y.

2012-07-01

To analyse the fracture coalescence behaviour of rock, rectangular prismatic sandstone specimens (80 × 160 × 30 mm in size) containing three fissures were tested under uniaxial compression. The strength and deformation behaviours of the specimens are first analysed by investigating the effects of the ligament angle β2 on the peak strength, peak strain and crack initiation stress of the specimens. To confirm the sequence of crack coalescence, a photographic monitoring technique is used throughout the entire period of deformation. Based on the results, the relationship between the real-time crack coalescence process and the axial stress-strain curve of brittle sandstone specimens is also developed, and this relationship can be used to evaluate the macroscopic deformation characteristics of pre-cracked rock. The equivalent strain evolution fields of the specimen, with α = β1 = 45° and β2 = 90°, are obtained using the digital image correlation technique and show good agreement with the experimental results of pre-cracked brittle sandstone. These experimental results are expected to improve the understanding of fracture mechanisms and be used in rock engineering with intermittent structures, such as deep underground excavated tunnels.

Delimiting Coalescence Genes (C-Genes) in Phylogenomic Data Sets.

Science.gov (United States)

Springer, Mark S; Gatesy, John

2018-02-26

coalescence methods have emerged as a popular alternative for inferring species trees with large genomic datasets, because these methods explicitly account for incomplete lineage sorting. However, statistical consistency of summary coalescence methods is not guaranteed unless several model assumptions are true, including the critical assumption that recombination occurs freely among but not within coalescence genes (c-genes), which are the fundamental units of analysis for these methods. Each c-gene has a single branching history, and large sets of these independent gene histories should be the input for genome-scale coalescence estimates of phylogeny. By contrast, numerous studies have reported the results of coalescence analyses in which complete protein-coding sequences are treated as c-genes even though exons for these loci can span more than a megabase of DNA. Empirical estimates of recombination breakpoints suggest that c-genes may be much shorter, especially when large clades with many species are the focus of analysis. Although this idea has been challenged recently in the literature, the inverse relationship between c-gene size and increased taxon sampling in a dataset-the 'recombination ratchet'-is a fundamental property of c-genes. For taxonomic groups characterized by genes with long intron sequences, complete protein-coding sequences are likely not valid c-genes and are inappropriate units of analysis for summary coalescence methods unless they occur in recombination deserts that are devoid of incomplete lineage sorting (ILS). Finally, it has been argued that coalescence methods are robust when the no-recombination within loci assumption is violated, but recombination must matter at some scale because ILS, a by-product of recombination, is the raison d'etre for coalescence methods. That is, extensive recombination is required to yield the large number of independently segregating c-genes used to infer a species tree. If coalescent methods are powerful

Direct numerical simulation of water droplet coalescence in the oil

International Nuclear Information System (INIS)

Mohammadi, Mehdi; Shahhosseini, Shahrokh; Bayat, Mahmoud

2012-01-01

Highlights: ► VOF computational technique has been used to simulate coalescence of two water droplets in oil. ► The model was validated with the experimental data for binary droplet coalescence. ► Based on the CFD simulation results a correlation has been proposed to predict the coalescence time. - Abstract: Coalescence of two water droplets in the oil was simulated using Computational Fluid Dynamics (CFD) techniques. The finite volume numerical method was applied to solve the Navier–Stokes equations in conjunction with the Volume of Fluid (VOF) approach for interface tracking. The effects of some parameters consisting of the collision velocity, off-center collision parameter, oil viscosity and water–oil interfacial tension on the coalescence time were investigated. The simulation results were validated against the experimental data available in the literature. The results revealed that quicker coalescence could be achieved if the head-on collisions occur or the droplets approach each other with a high velocity. In addition, low oil viscosities or large water–oil interfacial tensions cause less coalescence time. Moreover, a correlation was developed to predict coalescence efficiency as a function of the mentioned parameters.

Identifying the rooted species tree from the distribution of unrooted gene trees under the coalescent.

Science.gov (United States)

Allman, Elizabeth S; Degnan, James H; Rhodes, John A

2011-06-01

Gene trees are evolutionary trees representing the ancestry of genes sampled from multiple populations. Species trees represent populations of individuals-each with many genes-splitting into new populations or species. The coalescent process, which models ancestry of gene copies within populations, is often used to model the probability distribution of gene trees given a fixed species tree. This multispecies coalescent model provides a framework for phylogeneticists to infer species trees from gene trees using maximum likelihood or Bayesian approaches. Because the coalescent models a branching process over time, all trees are typically assumed to be rooted in this setting. Often, however, gene trees inferred by traditional phylogenetic methods are unrooted. We investigate probabilities of unrooted gene trees under the multispecies coalescent model. We show that when there are four species with one gene sampled per species, the distribution of unrooted gene tree topologies identifies the unrooted species tree topology and some, but not all, information in the species tree edges (branch lengths). The location of the root on the species tree is not identifiable in this situation. However, for 5 or more species with one gene sampled per species, we show that the distribution of unrooted gene tree topologies identifies the rooted species tree topology and all its internal branch lengths. The length of any pendant branch leading to a leaf of the species tree is also identifiable for any species from which more than one gene is sampled.

Phylogenetic systematics and biogeography of hummingbirds: Bayesian and maximum likelihood analyses of partitioned data and selection of an appropriate partitioning strategy.

Science.gov (United States)

McGuire, Jimmy A; Witt, Christopher C; Altshuler, Douglas L; Remsen, J V

2007-10-01

Hummingbirds are an important model system in avian biology, but to date the group has been the subject of remarkably few phylogenetic investigations. Here we present partitioned Bayesian and maximum likelihood phylogenetic analyses for 151 of approximately 330 species of hummingbirds and 12 outgroup taxa based on two protein-coding mitochondrial genes (ND2 and ND4), flanking tRNAs, and two nuclear introns (AK1 and BFib). We analyzed these data under several partitioning strategies ranging between unpartitioned and a maximum of nine partitions. In order to select a statistically justified partitioning strategy following partitioned Bayesian analysis, we considered four alternative criteria including Bayes factors, modified versions of the Akaike information criterion for small sample sizes (AIC(c)), Bayesian information criterion (BIC), and a decision-theoretic methodology (DT). Following partitioned maximum likelihood analyses, we selected a best-fitting strategy using hierarchical likelihood ratio tests (hLRTS), the conventional AICc, BIC, and DT, concluding that the most stringent criterion, the performance-based DT, was the most appropriate methodology for selecting amongst partitioning strategies. In the context of our well-resolved and well-supported phylogenetic estimate, we consider the historical biogeography of hummingbirds using ancestral state reconstructions of (1) primary geographic region of occurrence (i.e., South America, Central America, North America, Greater Antilles, Lesser Antilles), (2) Andean or non-Andean geographic distribution, and (3) minimum elevational occurrence. These analyses indicate that the basal hummingbird assemblages originated in the lowlands of South America, that most of the principle clades of hummingbirds (all but Mountain Gems and possibly Bees) originated on this continent, and that there have been many (at least 30) independent invasions of other primary landmasses, especially Central America.

Bayesian methods for hackers probabilistic programming and Bayesian inference

CERN Document Server

Davidson-Pilon, Cameron

2016-01-01

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...

Coalescent: an open-science framework for importance sampling in coalescent theory.

Science.gov (United States)

Tewari, Susanta; Spouge, John L

2015-01-01

Background. In coalescent theory, computer programs often use importance sampling to calculate likelihoods and other statistical quantities. An importance sampling scheme can exploit human intuition to improve statistical efficiency of computations, but unfortunately, in the absence of general computer frameworks on importance sampling, researchers often struggle to translate new sampling schemes computationally or benchmark against different schemes, in a manner that is reliable and maintainable. Moreover, most studies use computer programs lacking a convenient user interface or the flexibility to meet the current demands of open science. In particular, current computer frameworks can only evaluate the efficiency of a single importance sampling scheme or compare the efficiencies of different schemes in an ad hoc manner. Results. We have designed a general framework (http://coalescent.sourceforge.net; language: Java; License: GPLv3) for importance sampling that computes likelihoods under the standard neutral coalescent model of a single, well-mixed population of constant size over time following infinite sites model of mutation. The framework models the necessary core concepts, comes integrated with several data sets of varying size, implements the standard competing proposals, and integrates tightly with our previous framework for calculating exact probabilities. For a given dataset, it computes the likelihood and provides the maximum likelihood estimate of the mutation parameter. Well-known benchmarks in the coalescent literature validate the accuracy of the framework. The framework provides an intuitive user interface with minimal clutter. For performance, the framework switches automatically to modern multicore hardware, if available. It runs on three major platforms (Windows, Mac and Linux). Extensive tests and coverage make the framework reliable and maintainable. Conclusions. In coalescent theory, many studies of computational efficiency consider only

Coalescent: an open-science framework for importance sampling in coalescent theory

Directory of Open Access Journals (Sweden)

Susanta Tewari

2015-08-01

Full Text Available Background. In coalescent theory, computer programs often use importance sampling to calculate likelihoods and other statistical quantities. An importance sampling scheme can exploit human intuition to improve statistical efficiency of computations, but unfortunately, in the absence of general computer frameworks on importance sampling, researchers often struggle to translate new sampling schemes computationally or benchmark against different schemes, in a manner that is reliable and maintainable. Moreover, most studies use computer programs lacking a convenient user interface or the flexibility to meet the current demands of open science. In particular, current computer frameworks can only evaluate the efficiency of a single importance sampling scheme or compare the efficiencies of different schemes in an ad hoc manner.Results. We have designed a general framework (http://coalescent.sourceforge.net; language: Java; License: GPLv3 for importance sampling that computes likelihoods under the standard neutral coalescent model of a single, well-mixed population of constant size over time following infinite sites model of mutation. The framework models the necessary core concepts, comes integrated with several data sets of varying size, implements the standard competing proposals, and integrates tightly with our previous framework for calculating exact probabilities. For a given dataset, it computes the likelihood and provides the maximum likelihood estimate of the mutation parameter. Well-known benchmarks in the coalescent literature validate the accuracy of the framework. The framework provides an intuitive user interface with minimal clutter. For performance, the framework switches automatically to modern multicore hardware, if available. It runs on three major platforms (Windows, Mac and Linux. Extensive tests and coverage make the framework reliable and maintainable.Conclusions. In coalescent theory, many studies of computational efficiency

Evidence of a major gene from Bayesian segregation analyses of liability to osteochondral diseases in pigs

NARCIS (Netherlands)

Kadarmideen, H.N.; Janss, L.L.G.

2005-01-01

Bayesian segregation analyses were used to investigate the mode of inheritance of osteochondral lesions (osteochondrosis, OC) in pigs. Data consisted of 1163 animals with OC and their pedigrees included 2891 animals. Mixed-inheritance threshold models (MITM) and several variants of MITM, in

Le phénomène de coalescence. Etude bibliographique Coalescence Phenomena. A Review

Directory of Open Access Journals (Sweden)

Palermo T.

2006-11-01

Full Text Available Nous présentons une revue des différents travaux expérimentaux et théoriques effectués sur le phénomène de coalescence en limitant principalement notre discussion au cas de la coalescence d'une goutte isolée à une interface plane ou déformable. La coalescence se divise en deux phases distinctes : le drainage et la rupture du film interfacial. Ce film est constitué du liquide de la phase continue séparant la goutte de l'interface. La condition de rupture est principalement contrôlée par les forces électrostatiques dues à la double couche et par les forces de Van der Waals. Les résultats expérimentaux mettent en évidence un phénomène de coalescence partielle ainsi que l'existence d'une distribution statistique du temps de coalescence. Ils montrent également l'influence complexe de nombreux paramètres physiques et physico-chimiques sur le temps de coalescence. On rappelle les principaux modèles théoriques décrivant le drainage et la rupture des films liquides. Ces modèles permettent, entre autre, d'aboutir à des expressions mathématiques reliant le temps de coalescence à des paramètres tels que la tension interfaciale, les densités et les viscosités des fluides, la taille des gouttes. The problem linked to the stability of oil-in-water emulsions (e. g. deoiling of water and water-in-oil emulsions (e. g. dehydration of crudes is one of the major problems encountered in the petroleum industry. From the thermodynamic standpoint, an emulsion is always unstable (Fig. I. 1. The kinematic stability characterizing the separation rate of the dispersed phase from the continuous phase can nonetheless be controlled by the coalescence of droplets present in the emulsion (Fig. I. 2. This article reviews various experimental and theoretical works on the phenomenon of coalescence but the discussion is limited mainly to the coalescence of an single drop at a flat or deformable interface. The coalescence of a single drop is governed

Bayesian target tracking based on particle filter

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter (EKF) proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo (MCMC) method, the resampling step, etc novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.

Experimental visualization coalesced interaction of sliding bubble near wall in vertical narrow rectangular channel

International Nuclear Information System (INIS)

Xu Jianjun; Chen Bingde; Wang Xiaojun

2011-01-01

The characteristic of the coalesced sliding bubble was visually observed by wide side and narrow side of the narrow rectangular channel using high speed digital camera. The results show that the coalesced time among the sliding bubbles is quick, and the new formation of coalesced bubble is not lift-off, and it continues to slide along the heated surface in low heat flux for the isolated bubble region. The influence region is about 2 times projected area of the sliding bubble when the sliding bubbles begin to interact. The sliding bubble velocities increase duo to the interaction among the bubbles, which contributes to enhance heat transfer of this region. Finally, the effect of coalesced interaction of growing bubble in the nucleation sites on bubble lift-off was discussed and analysed. (authors)

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

Directory of Open Access Journals (Sweden)

Sho Fukuda

2014-12-01

Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

Crack Coalescence in Molded Gypsum and Carrara Marble

Science.gov (United States)

Wong, N.; Einstein, H. H.

2007-12-01

This research investigates the fracturing and coalescence behavior in prismatic laboratory-molded gypsum and Carrara marble specimens, which consist of either one or two pre-existing open flaws, under uniaxial compression. The tests are monitored by a high speed video system with a frame rate up to 24,000 frames/second. It allows one to precisely observe the cracking mechanisms, in particular if shear or tensile fracturing takes place. Seven crack types and nine crack coalescence categories are identified. The flaw inclination angle, the ligament length and the bridging angle between two flaws have different extents of influence on the coalescence patterns. For coplanar flaws, as the flaw inclination angle increases, there is a general trend of variation from shear coalescence to tensile coalescence. For stepped flaws, as the bridging angle changes from negative to small positive, and further up to large positive values, the coalescence generally progresses from categories of no coalescence, indirect coalescence to direct coalescence. For direct coalescence, it generally progresses from shear, mixed shear-tensile to tensile as the bridging angle increases. Some differences in fracturing and coalescence processes are observed in gypsum and marble, particularly the crack initiation in marble is preceded by the development of macroscopic white patches, but not in gypsum. Scanning Electron Microprobe (SEM) study reveals that the white patches consist of zones of microcracks (process zones).

The image recognition based on neural network and Bayesian decision

Science.gov (United States)

Wang, Chugege

2018-04-01

The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

Coala: an R framework for coalescent simulation.

Science.gov (United States)

Staab, Paul R; Metzler, Dirk

2016-06-15

Simulation programs based on the coalescent efficiently generate genetic data according to a given model of evolution. We present coala, an R package for calling coalescent simulators with a unified syntax. It can execute simulations with several programs, calculate additional summary statistics and combine multiple simulations to create biologically more realistic data. The package is publicly available on CRAN and on https://github.com/statgenlmu/coala under the conditions of the MIT license. metzler@bio.lmu.de. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Analysing and exemplifying forensic conclusion criteria in terms of Bayesian decision theory.

Science.gov (United States)

Biedermann, A; Bozza, S; Taroni, F

2018-03-01

There is ongoing discussion in forensic science and the law about the nature of the conclusions reached based on scientific evidence, and on how such conclusions - and conclusion criteria - may be justified by rational argument. Examples, among others, are encountered in fields such as fingermarks (e.g., 'this fingermark comes from Mr. A's left thumb'), handwriting examinations (e.g., 'the questioned signature is that of Mr. A'), kinship analyses (e.g., 'Mr. A is the father of child C') or anthropology (e.g., 'these are human remains'). Considerable developments using formal methods of reasoning based on, for example (Bayesian) decision theory, are available in literature, but currently such reference principles are not explicitly used in operational forensic reporting and ensuing decision-making. Moreover, applied examples, illustrating the principles, are scarce. A potential consequence of this in practical proceedings, and hence a cause of concern, is that underlying ingredients of decision criteria (such as losses quantifying the undesirability of adverse decision consequences), are not properly dealt with. There is merit, thus, in pursuing the study and discussion of practical examples, demonstrating that formal decision-theoretic principles are not merely conceptual considerations. Actually, these principles can be shown to underpin practical decision-making procedures and existing legal decision criteria, though often not explicitly apparent as such. In this paper, we will present such examples and discuss their properties from a Bayesian decision-theoretic perspective. We will argue that these are essential concepts for an informed discourse on decision-making across forensic disciplines and the development of a coherent view on this topic. We will also emphasize that these principles are of normative nature in the sense that they provide standards against which actual judgment and decision-making may be compared. Most importantly, these standards are

Bubble coalescence in breathing DNA

DEFF Research Database (Denmark)

Novotný, Tomas; Pedersen, Jonas Nyvold; Ambjörnsson, Tobias

2007-01-01

We investigate the coalescence of two DNA bubbles initially located at weak segments and separated by a more stable barrier region in a designed construct of double-stranded DNA. The characteristic time for bubble coalescence and the corresponding distribution are derived, as well as the distribu...... vicious walkers in opposite potentials....

Gene tree rooting methods give distributions that mimic the coalescent process.

Science.gov (United States)

Tian, Yuan; Kubatko, Laura S

2014-01-01

Multi-locus phylogenetic inference is commonly carried out via models that incorporate the coalescent process to model the possibility that incomplete lineage sorting leads to incongruence between gene trees and the species tree. An interesting question that arises in this context is whether data "fit" the coalescent model. Previous work (Rosenfeld et al., 2012) has suggested that rooting of gene trees may account for variation in empirical data that has been previously attributed to the coalescent process. We examine this possibility using simulated data. We show that, in the case of four taxa, the distribution of gene trees observed from rooting estimated gene trees with either the molecular clock or with outgroup rooting can be closely matched by the distribution predicted by the coalescent model with specific choices of species tree branch lengths. We apply commonly-used coalescent-based methods of species tree inference to assess their performance in these situations. Copyright © 2013 Elsevier Inc. All rights reserved.

Genetic Variability Under the Seedbank Coalescent.

Science.gov (United States)

Blath, Jochen; González Casanova, Adrián; Eldon, Bjarki; Kurt, Noemi; Wilke-Berenguer, Maite

2015-07-01

We analyze patterns of genetic variability of populations in the presence of a large seedbank with the help of a new coalescent structure called the seedbank coalescent. This ancestral process appears naturally as a scaling limit of the genealogy of large populations that sustain seedbanks, if the seedbank size and individual dormancy times are of the same order as those of the active population. Mutations appear as Poisson processes on the active lineages and potentially at reduced rate also on the dormant lineages. The presence of "dormant" lineages leads to qualitatively altered times to the most recent common ancestor and nonclassical patterns of genetic diversity. To illustrate this we provide a Wright-Fisher model with a seedbank component and mutation, motivated from recent models of microbial dormancy, whose genealogy can be described by the seedbank coalescent. Based on our coalescent model, we derive recursions for the expectation and variance of the time to most recent common ancestor, number of segregating sites, pairwise differences, and singletons. Estimates (obtained by simulations) of the distributions of commonly employed distance statistics, in the presence and absence of a seedbank, are compared. The effect of a seedbank on the expected site-frequency spectrum is also investigated using simulations. Our results indicate that the presence of a large seedbank considerably alters the distribution of some distance statistics, as well as the site-frequency spectrum. Thus, one should be able to detect from genetic data the presence of a large seedbank in natural populations. Copyright © 2015 by the Genetics Society of America.

Bubble coalescence in a Newtonian fluid

Science.gov (United States)

Garg, Vishrut; Basaran, Osman

2017-11-01

Bubble coalescence plays a central role in the hydrodynamics of gas-liquid systems such as bubble column reactors, spargers, and foams. Two bubbles approaching each other at velocity V coalesce when the thin film between them ruptures, which is often the rate-limiting step. Experimental studies of this system are difficult, and recent works provide conflicting results on the effect of V on coalescence times. We simulate the head-on approach of two bubbles of equal radii R in an incompressible Newtonian fluid (density ρ, viscosity μ, and surface tension σ) by solving numerically the free boundary problem comprised of the Navier Stokes and continuity equations. Simulations are made challenging by the existence of highly disparate lengthscales, i.e. film thickness and drop radii, which are resolved by using the method of elliptic mesh generation. For a given liquid, the bubbles are shown to coalesce for all velocities below a critical value. The effects of Ohnesorge number Oh = μ /√{ ρσR } on coalescence time and critical velocity are also investigated.

Study on shielded pump system failure analysis method based on Bayesian network

International Nuclear Information System (INIS)

Bao Yilan; Huang Gaofeng; Tong Lili; Cao Xuewu

2012-01-01

This paper applies Bayesian network to the system failure analysis, with an aim to improve knowledge representation of the uncertainty logic and multi-fault states in system failure analysis. A Bayesian network for shielded pump failure analysis is presented, conducting fault parameter learning, updating Bayesian network parameter based on new samples. Finally, through the Bayesian network inference, vulnerability in this system, the largest possible failure modes, and the fault probability are obtained. The powerful ability of Bayesian network to analyze system fault is illustrated by examples. (authors)

Bayesian outcome-based strategy classification.

Science.gov (United States)

Lee, Michael D

2016-03-01

Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the widely-used "weighted additive" (WADD) model, and show how this model, as well as other important models like "take-the-best" (TTB), can and should be expressed in terms of meaningful priors. Methodologically, they develop an inference approach based on the Minimum Description Length (MDL) principles that balances both the goodness-of-fit and complexity of the decision models they consider. This paper aims to preserve these useful contributions, but provide a complementary Bayesian approach with some theoretical and methodological advantages. We develop a simple graphical model, implemented in JAGS, that allows for fully Bayesian inferences about which models people use to make decisions. To demonstrate the Bayesian approach, we apply it to the models and data considered by Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014), showing how a prior predictive analysis of the models, and posterior inferences about which models people use and the parameter settings at which they use them, can contribute to our understanding of human decision making.

Comparison Between Bayesian and Maximum Entropy Analyses of Flow Networks†

Directory of Open Access Journals (Sweden)

Steven H. Waldrip

2017-02-01

Full Text Available We compare the application of Bayesian inference and the maximum entropy (MaxEnt method for the analysis of flow networks, such as water, electrical and transport networks. The two methods have the advantage of allowing a probabilistic prediction of flow rates and other variables, when there is insufficient information to obtain a deterministic solution, and also allow the effects of uncertainty to be included. Both methods of inference update a prior to a posterior probability density function (pdf by the inclusion of new information, in the form of data or constraints. The MaxEnt method maximises an entropy function subject to constraints, using the method of Lagrange multipliers,to give the posterior, while the Bayesian method finds its posterior by multiplying the prior with likelihood functions incorporating the measured data. In this study, we examine MaxEnt using soft constraints, either included in the prior or as probabilistic constraints, in addition to standard moment constraints. We show that when the prior is Gaussian,both Bayesian inference and the MaxEnt method with soft prior constraints give the same posterior means, but their covariances are different. In the Bayesian method, the interactions between variables are applied through the likelihood function, using second or higher-order cross-terms within the posterior pdf. In contrast, the MaxEnt method incorporates interactions between variables using Lagrange multipliers, avoiding second-order correlation terms in the posterior covariance. The MaxEnt method with soft prior constraints, therefore, has a numerical advantage over Bayesian inference, in that the covariance terms are avoided in its integrations. The second MaxEnt method with soft probabilistic constraints is shown to give posterior means of similar, but not identical, structure to the other two methods, due to its different formulation.

Motion Learning Based on Bayesian Program Learning

Directory of Open Access Journals (Sweden)

Cheng Meng-Zhen

2017-01-01

Full Text Available The concept of virtual human has been highly anticipated since the 1980s. By using computer technology, Human motion simulation could generate authentic visual effect, which could cheat human eyes visually. Bayesian Program Learning train one or few motion data, generate new motion data by decomposing and combining. And the generated motion will be more realistic and natural than the traditional one.In this paper, Motion learning based on Bayesian program learning allows us to quickly generate new motion data, reduce workload, improve work efficiency, reduce the cost of motion capture, and improve the reusability of data.

A default Bayesian hypothesis test for mediation.

Science.gov (United States)

Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan

2015-03-01

In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).

Mechanism and simulation of droplet coalescence in molten steel

Science.gov (United States)

Ni, Bing; Zhang, Tao; Ni, Hai-qi; Luo, Zhi-guo

2017-11-01

Droplet coalescence in liquid steel was carefully investigated through observations of the distribution pattern of inclusions in solidified steel samples. The process of droplet coalescence was slow, and the critical Weber number ( We) was used to evaluate the coalescence or separation of droplets. The relationship between the collision parameter and the critical We indicated whether slow coalescence or bouncing of droplets occurred. The critical We was 5.5, which means that the droplets gradually coalesce when We ≤ 5.5, whereas they bounce when We > 5.5. For the carbonate wire feeding into liquid steel, a mathematical model implementing a combined computational fluid dynamics (CFD)-discrete element method (DEM) approach was developed to simulate the movement and coalescence of variably sized droplets in a bottom-argon-blowing ladle. In the CFD model, the flow field was solved on the premise that the fluid was a continuous medium. Meanwhile, the droplets were dispersed in the DEM model, and the coalescence criterion of the particles was added to simulate the collision- coalescence process of the particles. The numerical simulation results and observations of inclusion coalescence in steel samples are consistent.

Bayesian and maximum likelihood estimation of genetic maps

DEFF Research Database (Denmark)

York, Thomas L.; Durrett, Richard T.; Tanksley, Steven

2005-01-01

There has recently been increased interest in the use of Markov Chain Monte Carlo (MCMC)-based Bayesian methods for estimating genetic maps. The advantage of these methods is that they can deal accurately with missing data and genotyping errors. Here we present an extension of the previous methods...... of genotyping errors. A similar advantage of the Bayesian method was not observed for missing data. We also re-analyse a recently published set of data from the eggplant and show that the use of the MCMC-based method leads to smaller estimates of genetic distances....

Risk Based Maintenance of Offshore Wind Turbines Using Bayesian Networks

DEFF Research Database (Denmark)

Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

2010-01-01

This paper presents how Bayesian networks can be used to make optimal decisions for repairs of offshore wind turbines. The Bayesian network is an efficient tool for updating a deterioration model whenever new information becomes available from inspections/monitoring. The optimal decision is found...... such that the preventive maintenance effort is balanced against the costs to corrective maintenance including indirect costs to reduced production. The basis for the optimization is the risk based Bayesian decision theory. The method is demonstrated through an application example....

Bayesian methods in reliability

Science.gov (United States)

Sander, P.; Badoux, R.

1991-11-01

The present proceedings from a course on Bayesian methods in reliability encompasses Bayesian statistical methods and their computational implementation, models for analyzing censored data from nonrepairable systems, the traits of repairable systems and growth models, the use of expert judgment, and a review of the problem of forecasting software reliability. Specific issues addressed include the use of Bayesian methods to estimate the leak rate of a gas pipeline, approximate analyses under great prior uncertainty, reliability estimation techniques, and a nonhomogeneous Poisson process. Also addressed are the calibration sets and seed variables of expert judgment systems for risk assessment, experimental illustrations of the use of expert judgment for reliability testing, and analyses of the predictive quality of software-reliability growth models such as the Weibull order statistics.

Coalescence preference in dense packing of bubbles

Science.gov (United States)

Kim, Yeseul; Gim, Bopil; Gim, Bopil; Weon, Byung Mook

2015-11-01

Coalescence preference is the tendency that a merged bubble from the contact of two original bubbles (parent) tends to be near to the bigger parent. Here, we show that the coalescence preference can be blocked by densely packing of neighbor bubbles. We use high-speed high-resolution X-ray microscopy to clearly visualize individual coalescence phenomenon which occurs in micro scale seconds and inside dense packing of microbubbles with a local packing fraction of ~40%. Previous theory and experimental evidence predict a power of -5 between the relative coalescence position and the parent size. However, our new observation for coalescence preference in densely packed microbubbles shows a different power of -2. We believe that this result may be important to understand coalescence dynamics in dense packing of soft matter. This work (NRF-2013R1A22A04008115) was supported by Mid-career Researcher Program through NRF grant funded by the MEST and also was supported by Ministry of Science, ICT and Future Planning (2009-0082580) and by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry and Education, Science and Technology (NRF-2012R1A6A3A04039257).

Analysis of Void Growth and Coalescence in Porous Polymer Materials. Coalescence in Polymer Materials

Directory of Open Access Journals (Sweden)

S. A. Reffas

2013-06-01

Full Text Available The use of polymeric materials in engineering applications is growing more and more all over the world. This issue requests new methodologies of analysis in order to assess the material’s capability to withstand complex loads. The use of polyacetal in engineering applications has increased rapidly in the last decade. In order to evaluate the behavior, the damage and coalescence of this type of polymer, a numerical method based on damage which occurs following several stages (nucleation of cavities, their growth and coalescence in more advanced stages of deformation is proposed in this work. A particular attention is given on the stress-strain and the volumetric strain evolution under different triaxiality and for three initial void shapes. Its application to polyacetal allows approving this approach for technical polymers. Finally, this method allow us to compare the obtained results of basic calculations at different triaxiality and to discuss their possible influence on the initial size and the geometrical shape of the porosity on the material failure.

ObStruct: a method to objectively analyse factors driving population structure using Bayesian ancestry profiles.

Directory of Open Access Journals (Sweden)

Velimir Gayevskiy

Full Text Available Bayesian inference methods are extensively used to detect the presence of population structure given genetic data. The primary output of software implementing these methods are ancestry profiles of sampled individuals. While these profiles robustly partition the data into subgroups, currently there is no objective method to determine whether the fixed factor of interest (e.g. geographic origin correlates with inferred subgroups or not, and if so, which populations are driving this correlation. We present ObStruct, a novel tool to objectively analyse the nature of structure revealed in Bayesian ancestry profiles using established statistical methods. ObStruct evaluates the extent of structural similarity between sampled and inferred populations, tests the significance of population differentiation, provides information on the contribution of sampled and inferred populations to the observed structure and crucially determines whether the predetermined factor of interest correlates with inferred population structure. Analyses of simulated and experimental data highlight ObStruct's ability to objectively assess the nature of structure in populations. We show the method is capable of capturing an increase in the level of structure with increasing time since divergence between simulated populations. Further, we applied the method to a highly structured dataset of 1,484 humans from seven continents and a less structured dataset of 179 Saccharomyces cerevisiae from three regions in New Zealand. Our results show that ObStruct provides an objective metric to classify the degree, drivers and significance of inferred structure, as well as providing novel insights into the relationships between sampled populations, and adds a final step to the pipeline for population structure analyses.

Satellite Formation during Coalescence of Unequal Size Drops

KAUST Repository

Zhang, F. H.

2009-03-12

The coalescence of a drop with a flat liquid surface pinches off a satellite from its top, in the well-known coalescence cascade, whereas the coalescence of two equally sized drops does not appear to leave such a satellite. Herein we perform experiments to identify the critical diameter ratio of two drops, above which a satellite is produced during their coalescence. We find that the critical parent ratio is as small as 1.55, but grows monotonically with the Ohnesorge number. The daughter size is typically about 50% of the mother drop. However, we have identified novel pinch-off dynamics close to the critical size ratio, where the satellite does not fully separate, but rather goes directly into a second stage of the coalescence cascade, thus generating a much smaller satellite droplet.

Satellite Formation during Coalescence of Unequal Size Drops

KAUST Repository

Zhang, F. H.; Li, E. Q.; Thoroddsen, Sigurdur T

2009-01-01

The coalescence of a drop with a flat liquid surface pinches off a satellite from its top, in the well-known coalescence cascade, whereas the coalescence of two equally sized drops does not appear to leave such a satellite. Herein we perform experiments to identify the critical diameter ratio of two drops, above which a satellite is produced during their coalescence. We find that the critical parent ratio is as small as 1.55, but grows monotonically with the Ohnesorge number. The daughter size is typically about 50% of the mother drop. However, we have identified novel pinch-off dynamics close to the critical size ratio, where the satellite does not fully separate, but rather goes directly into a second stage of the coalescence cascade, thus generating a much smaller satellite droplet.

GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence

OpenAIRE

Abbott, B. P.; Abbott, R.; Adhikari, R. X.; Anderson, S. B.; Arai, K.; Araya, M. C.; Barayoga, J. C.; Barish, B. C.; Berger, B. K.; Billingsley, G.; Blackburn, J. K.; Bork, R.; Brooks, A. F.; Brunett, S.; Cahillane, C.

2016-01-01

We report the observation of a gravitational-wave signal produced by the coalescence of two stellar-mass black holes. The signal, GW151226, was observed by the twin detectors of the Laser Interferometer Gravitational-Wave Observatory (LIGO) on December 26, 2015 at 03:38:53 UTC. The signal was initially identified within 70 s by an online matched-filter search targeting binary coalescences. Subsequent off-line analyses recovered GW151226 with a network signal-to-noise ratio of 13 and a signifi...

MapReduce Based Parallel Bayesian Network for Manufacturing Quality Control

Science.gov (United States)

Zheng, Mao-Kuan; Ming, Xin-Guo; Zhang, Xian-Yu; Li, Guo-Ming

2017-09-01

Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the circumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian network of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly proportionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The integration of bigdata analytics and BN method offers a whole new perspective in manufacturing quality control.

Para-allopatry in hybridizing fire-bellied toads (Bombina bombina and B. variegata): Inference from transcriptome-wide coalescence analyses

Czech Academy of Sciences Publication Activity Database

Nürnberger, Beate; Fijarczyk, A.; Lohse, K.; Szymura, J. M.; Blaxter, M. L.

2016-01-01

Roč. 70, č. 8 (2016), s. 1803-1818 ISSN 0014-3820 Institutional support: RVO:68081766 Keywords : ecological speciation * genome-wide coalescence * hybrid zone * introgression * RNA-seq. Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 4.201, year: 2016

Rational hypocrisy: a Bayesian analysis based on informal argumentation and slippery slopes.

Science.gov (United States)

Rai, Tage S; Holyoak, Keith J

2014-01-01

Moral hypocrisy is typically viewed as an ethical accusation: Someone is applying different moral standards to essentially identical cases, dishonestly claiming that one action is acceptable while otherwise equivalent actions are not. We suggest that in some instances the apparent logical inconsistency stems from different evaluations of a weak argument, rather than dishonesty per se. Extending Corner, Hahn, and Oaksford's (2006) analysis of slippery slope arguments, we develop a Bayesian framework in which accusations of hypocrisy depend on inferences of shared category membership between proposed actions and previous standards, based on prior probabilities that inform the strength of competing hypotheses. Across three experiments, we demonstrate that inferences of hypocrisy increase as perceptions of the likelihood of shared category membership between precedent cases and current cases increase, that these inferences follow established principles of category induction, and that the presence of self-serving motives increases inferences of hypocrisy independent of changes in the actions themselves. Taken together, these results demonstrate that Bayesian analyses of weak arguments may have implications for assessing moral reasoning. © 2014 Cognitive Science Society, Inc.

When mechanism matters: Bayesian forecasting using models of ecological diffusion

Science.gov (United States)

Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

2017-01-01

Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

GW151226: observation of gravitational waves from a 22-solar-mass binary black hole \\ud coalescence

OpenAIRE

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Aggarwal, N.

2016-01-01

We report the observation of a gravitational-wave signal produced by the coalescence of two stellar-mass black holes. The signal, GW151226, was observed by the twin detectors of the Laser Interferometer Gravitational-Wave Observatory (LIGO) on December 26, 2015 at 03:38:53 UTC. The signal was initially identified within 70 s by an online matched-filter search targeting binary coalescences. Subsequent off-line analyses recovered GW151226 with a network signal-to-noise ratio of 13 and a signifi...

Compact binary coalescences in the band of ground-based gravitational-wave detectors

International Nuclear Information System (INIS)

Mandel, Ilya; O'Shaughnessy, Richard

2010-01-01

As the ground-based gravitational-wave telescopes LIGO, Virgo and GEO 600 approach the era of first detections, we review the current knowledge of the coalescence rates and the mass and spin distributions of merging neutron-star and black-hole binaries. We emphasize the bi-directional connection between gravitational-wave astronomy and conventional astrophysics. Astrophysical input will make possible informed decisions about optimal detector configurations and search techniques. Meanwhile, rate upper limits, detected merger rates and the distribution of masses and spins measured by gravitational-wave searches will constrain astrophysical parameters through comparisons with astrophysical models. Future developments necessary to the success of gravitational-wave astronomy are discussed.

A fluid-mechanical model of elastocapillary coalescence

KAUST Repository

Singh, Kiran

2014-03-25

© 2014 Cambridge University Press. We present a fluid-mechanical model of the coalescence of a number of elastic objects due to surface tension. We consider an array of spring-block elements separated by thin liquid films, whose dynamics are modelled using lubrication theory. With this simplified model of elastocapillary coalescence, we present the results of numerical simulations for a large number of elements, N = O(10⁴). A linear stability analysis shows that pairwise coalescence is always the most unstable mode of deformation. However, the numerical simulations show that the cluster sizes actually produced by coalescence from a small white-noise perturbation have a distribution that depends on the relative strength of surface tension and elasticity, as measured by an elastocapillary number K. Both the maximum cluster size and the mean cluster size scale like K^-1/2 for small K. An analytical solution for the response of the system to a localized perturbation shows that such perturbations generate propagating disturbance fronts, which leave behind \\'frozen-in\\' clusters of a predictable size that also depends on K. A good quantitative comparison between the cluster-size statistics from noisy perturbations and this \\'frozen-in\\' cluster size suggests that propagating fronts may play a crucial role in the dynamics of coalescence.

Bayesian data analysis in population ecology: motivations, methods, and benefits

Science.gov (United States)

Dorazio, Robert

2016-01-01

During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

GeneRecon—A coalescent based tool for fine-scale association mapping

DEFF Research Database (Denmark)

Mailund, Thomas; Schierup, Mikkel Heide; Pedersen, Christian Nørgaard Storm

2006-01-01

GeneRecon is a tool for fine-scale association mapping using a coalescence model. GeneRecon takes as input case-control data from phased or unphased SNP and micro-satellite genotypes. The posterior distribution of disease locus position is obtained by Metropolis Hastings sampling in the state space...

Explosive coalescence of Magnetic Islands

International Nuclear Information System (INIS)

Tajima, T.; Sakai, J.I.

1985-04-01

An explosive reconnection process associated with nonlinear evolution of the coalescence instability is found through studies of particle and magnetohydrodynamic simulations. The explosive coalescence is a self-similar process of magnetic collapse, in which the magnetic and electrostatic energies and temperatures explode toward the explosion time t 0 as (t 0 -t)/sup 8/3/,(t 0 -t) -4 , and (t 0 -t)/sup -8/3/, respectively. Ensuing amplitude oscillations in these quantities are identified by deriving an equation of motion for the scale factor in the Sagdeev potential

An Analysis of Construction Accident Factors Based on Bayesian Network

OpenAIRE

Yunsheng Zhao; Jinyong Pei

2013-01-01

In this study, we have an analysis of construction accident factors based on bayesian network. Firstly, accidents cases are analyzed to build Fault Tree method, which is available to find all the factors causing the accidents, then qualitatively and quantitatively analyzes the factors with Bayesian network method, finally determines the safety management program to guide the safety operations. The results of this study show that bad condition of geological environment has the largest posterio...

Bunch coalescing in the Fermilab Main Ring

International Nuclear Information System (INIS)

Wildman, D.; Martin, P.; Meisner, K.; Miller, H.W.

1987-01-01

A new RF system has been installed in the Fermilab Main Ring to coalesce up to 13 individual bunches of protons or antiprotons into a single high-intensity bunch. The coalescing process consists of adiabatically reducing the h=1113 Main Ring RF voltage from 1 MV to less than 1 kV, capturing the debunched beam in a linearized h=53 and h=106 bucket, rotating for a quarter of a synchrotron oscillation period, and then recapturing the beam in a single h=1113 bucket. The new system is described and the results of recent coalescing experiments are compared with computer-generated particle tracking simulations

Surfactant effect on drop coalescence and film drainage hydrodynamics

Science.gov (United States)

Weheliye, Weheliye; Chinaud, Maxime; Voulgaropoulos, Victor; Angeli, Panagiota

2015-11-01

Coalescence of a drop on an aqueous-organic interface is studied in two test geometries A rectangular acrylic vessel and a Hele-Shaw cell (two parallel plates placed 2mm apart) are investigated for the experiments. Time resolved Particle Image Velocimetry (PIV) measurements provide information on the hydrodynamics during the bouncing stage of the droplet and on the vortices generated at the bulk fluid after the droplet has coalesced. The velocity field inside the droplet during its coalescence is presented. By localizing the rupture point of the coalescence in the quasi two dimensional cell, the film drainage dynamics are discussed by acquiring its flow velocity by PIV measurements with a straddling camera. The effect of surface tension forces in the coalescence of the droplet is investigated by introducing surface active agents at various concentrations extending on both sides of the critical micelle concentration.

Coalescence dynamics of mobile and immobile fluid interfaces

KAUST Repository

Vakarelski, Ivan Uriev; Manica, Rogerio; Li, Erqiang; Basheva, Elka S; Chan, Derek Y. C.; Thoroddsen, Sigurdur T.

2018-01-01

Coalescence dynamics between deformable bubbles and droplets can be dramatically affected by the mobility of the interfaces with fully tangentially mobile bubble-liquid or droplet-liquid interfaces expected to accelerate the coalescence by orders

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds.

Science.gov (United States)

Zhang, Yanmin; Jiao, Yu; Xiong, Xiao; Liu, Haichun; Ran, Ting; Xu, Jinxing; Lu, Shuai; Xu, Anyang; Pan, Jing; Qiao, Xin; Shi, Zhihao; Lu, Tao; Chen, Yadong

2015-11-01

The discovery of novel scaffolds against a specific target has long been one of the most significant but challengeable goals in discovering lead compounds. A scaffold that binds in important regions of the active pocket is more favorable as a starting point because scaffolds generally possess greater optimization possibilities. However, due to the lack of sufficient chemical space diversity of the databases and the ineffectiveness of the screening methods, it still remains a great challenge to discover novel active scaffolds. Since the strengths and weaknesses of both fragment-based drug design and traditional virtual screening (VS), we proposed a fragment VS concept based on Bayesian categorization for the discovery of novel scaffolds. This work investigated the proposal through an application on VEGFR-2 target. Firstly, scaffold and structural diversity of chemical space for 10 compound databases were explicitly evaluated. Simultaneously, a robust Bayesian classification model was constructed for screening not only compound databases but also their corresponding fragment databases. Although analysis of the scaffold diversity demonstrated a very unevenly distribution of scaffolds over molecules, results showed that our Bayesian model behaved better in screening fragments than molecules. Through a literature retrospective research, several generated fragments with relatively high Bayesian scores indeed exhibit VEGFR-2 biological activity, which strongly proved the effectiveness of fragment VS based on Bayesian categorization models. This investigation of Bayesian-based fragment VS can further emphasize the necessity for enrichment of compound databases employed in lead discovery by amplifying the diversity of databases with novel structures.

Bayesian Inference for Signal-Based Seismic Monitoring

Science.gov (United States)

Moore, D.

2015-12-01

Traditional seismic monitoring systems rely on discrete detections produced by station processing software, discarding significant information present in the original recorded signal. SIG-VISA (Signal-based Vertically Integrated Seismic Analysis) is a system for global seismic monitoring through Bayesian inference on seismic signals. By modeling signals directly, our forward model is able to incorporate a rich representation of the physics underlying the signal generation process, including source mechanisms, wave propagation, and station response. This allows inference in the model to recover the qualitative behavior of recent geophysical methods including waveform matching and double-differencing, all as part of a unified Bayesian monitoring system that simultaneously detects and locates events from a global network of stations. We demonstrate recent progress in scaling up SIG-VISA to efficiently process the data stream of global signals recorded by the International Monitoring System (IMS), including comparisons against existing processing methods that show increased sensitivity from our signal-based model and in particular the ability to locate events (including aftershock sequences that can tax analyst processing) precisely from waveform correlation effects. We also provide a Bayesian analysis of an alleged low-magnitude event near the DPRK test site in May 2010 [1] [2], investigating whether such an event could plausibly be detected through automated processing in a signal-based monitoring system. [1] Zhang, Miao and Wen, Lianxing. "Seismological Evidence for a Low-Yield Nuclear Test on 12 May 2010 in North Korea". Seismological Research Letters, January/February 2015. [2] Richards, Paul. "A Seismic Event in North Korea on 12 May 2010". CTBTO SnT 2015 oral presentation, video at https://video-archive.ctbto.org/index.php/kmc/preview/partner_id/103/uiconf_id/4421629/entry_id/0_ymmtpps0/delivery/http

On spatial coalescents with multiple mergers in two dimensions.

Science.gov (United States)

Heuer, Benjamin; Sturm, Anja

2013-08-01

We consider the genealogy of a sample of individuals taken from a spatially structured population when the variance of the offspring distribution is relatively large. The space is structured into discrete sites of a graph G. If the population size at each site is large, spatial coalescents with multiple mergers, so called spatial Λ-coalescents, for which ancestral lines migrate in space and coalesce according to some Λ-coalescent mechanism, are shown to be appropriate approximations to the genealogy of a sample of individuals. We then consider as the graph G the two dimensional torus with side length 2L+1 and show that as L tends to infinity, and time is rescaled appropriately, the partition structure of spatial Λ-coalescents of individuals sampled far enough apart converges to the partition structure of a non-spatial Kingman coalescent. From a biological point of view this means that in certain circumstances both the spatial structure as well as larger variances of the underlying offspring distribution are harder to detect from the sample. However, supplemental simulations show that for moderately large L the different structure is still evident. Copyright © 2012 Elsevier Inc. All rights reserved.

Coalescence measurements for evolving foams monitored by real-time projection imaging

International Nuclear Information System (INIS)

Myagotin, A; Helfen, L; Baumbach, T

2009-01-01

Real-time radiographic projection imaging together with novel spatio-temporal image analysis is presented to be a powerful technique for the quantitative analysis of coalescence processes accompanying the generation and temporal evolution of foams and emulsions. Coalescence events can be identified as discontinuities in a spatio-temporal image representing a sequence of projection images. Detection, identification of intensity and localization of the discontinuities exploit a violation criterion of the Fourier shift theorem and are based on recursive spatio-temporal image partitioning. The proposed method is suited for automated measurements of discontinuity rates (i.e., discontinuity intensity per unit time), so that large series of radiographs can be analyzed without user intervention. The application potential is demonstrated by the quantification of coalescence during the formation and decay of metal foams monitored by real-time x-ray radiography

Reliability estimation of safety-critical software-based systems using Bayesian networks

International Nuclear Information System (INIS)

Helminen, A.

2001-06-01

Due to the nature of software faults and the way they cause system failures new methods are needed for the safety and reliability evaluation of software-based safety-critical automation systems in nuclear power plants. In the research project 'Programmable automation system safety integrity assessment (PASSI)', belonging to the Finnish Nuclear Safety Research Programme (FINNUS, 1999-2002), various safety assessment methods and tools for software based systems are developed and evaluated. The project is financed together by the Radiation and Nuclear Safety Authority (STUK), the Ministry of Trade and Industry (KTM) and the Technical Research Centre of Finland (VTT). In this report the applicability of Bayesian networks to the reliability estimation of software-based systems is studied. The applicability is evaluated by building Bayesian network models for the systems of interest and performing simulations for these models. In the simulations hypothetical evidence is used for defining the parameter relations and for determining the ability to compensate disparate evidence in the models. Based on the experiences from modelling and simulations we are able to conclude that Bayesian networks provide a good method for the reliability estimation of software-based systems. (orig.)

Coalescence of magnetic islands

International Nuclear Information System (INIS)

Pellat, R.

1982-01-01

The paper gives the analytical theory of the coalescence instability and of a new, one island, instability. These instabilities are expected to be relevant for the disruptions observed in Tokamak experiments and astrophysical plasmas

Bayesian inference for psychology. Part II: Example applications with JASP.

Science.gov (United States)

Wagenmakers, Eric-Jan; Love, Jonathon; Marsman, Maarten; Jamil, Tahira; Ly, Alexander; Verhagen, Josine; Selker, Ravi; Gronau, Quentin F; Dropmann, Damian; Boutin, Bruno; Meerhoff, Frans; Knight, Patrick; Raj, Akash; van Kesteren, Erik-Jan; van Doorn, Johnny; Šmíra, Martin; Epskamp, Sacha; Etz, Alexander; Matzke, Dora; de Jong, Tim; van den Bergh, Don; Sarafoglou, Alexandra; Steingroever, Helen; Derks, Koen; Rouder, Jeffrey N; Morey, Richard D

2018-02-01

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

Explosive coalescence of magnetic islands and explosive particle acceleration

International Nuclear Information System (INIS)

Tajima, T.; Sakai, J.I.

1985-07-01

An explosive reconnection process associated with the nonlinear evolution of the coalescence instability is found through studies of the electromagnetic particle simulation and the magnetohydrodynamic particle simulation. The explosive coalescence is a process of magnetic collapse, in which we find the magnetic and electrostatic field energies and temperatures (ion temperature in the coalescing direction, in particular) explode toward the explosion time t 0 as (t 0 - t)/sup -8/3/, (t 0 - t) -4 , and (t 0 - t)/sup -8/3/, respectively for a canonical case. Single-peak, double-peak, and triple-peak structures of magnetic energy, temperature, and electrostatic energy, respectively, are observed on the simulation as overshoot amplitude oscillations and are theoretically explained. The heuristic model of Brunel and Tajima is extended to this explosive coalescence in order to extract the basic process. Since the explosive coalescence exhibits self-similarity, a temporal universality, we theoretically search for a self-similar solution to the two-fluid plasma equations

Modelling binary black-hole coalescence

International Nuclear Information System (INIS)

Baker, John

2003-01-01

The final burst of radiation from the coalescence of two supermassive black holes produces extraordinary gravitational wave luminosity making these events visible to LISA even out to large redshift. Interpreting such observations will require detailed theoretical models, based on general relativity. The effort to construct these models is just beginning to produce results. I describe the Lazarus approach to modelling these radiation bursts, and present recent results which indicate that the system loses, in the last few wave cycles, about 3% of its mass-energy as strongly polarized gravitational radiation

Personalized Multi-Student Improvement Based on Bayesian Cybernetics

Science.gov (United States)

Kaburlasos, Vassilis G.; Marinagi, Catherine C.; Tsoukalas, Vassilis Th.

2008-01-01

This work presents innovative cybernetics (feedback) techniques based on Bayesian statistics for drawing questions from an Item Bank towards personalized multi-student improvement. A novel software tool, namely "Module for Adaptive Assessment of Students" (or, "MAAS" for short), implements the proposed (feedback) techniques. In conclusion, a pilot…

Testing general relativity using Bayesian model selection: Applications to observations of gravitational waves from compact binary systems

International Nuclear Information System (INIS)

Del Pozzo, Walter; Veitch, John; Vecchio, Alberto

2011-01-01

Second-generation interferometric gravitational-wave detectors, such as Advanced LIGO and Advanced Virgo, are expected to begin operation by 2015. Such instruments plan to reach sensitivities that will offer the unique possibility to test general relativity in the dynamical, strong-field regime and investigate departures from its predictions, in particular, using the signal from coalescing binary systems. We introduce a statistical framework based on Bayesian model selection in which the Bayes factor between two competing hypotheses measures which theory is favored by the data. Probability density functions of the model parameters are then used to quantify the inference on individual parameters. We also develop a method to combine the information coming from multiple independent observations of gravitational waves, and show how much stronger inference could be. As an introduction and illustration of this framework-and a practical numerical implementation through the Monte Carlo integration technique of nested sampling-we apply it to gravitational waves from the inspiral phase of coalescing binary systems as predicted by general relativity and a very simple alternative theory in which the graviton has a nonzero mass. This method can (and should) be extended to more realistic and physically motivated theories.

msBayes: Pipeline for testing comparative phylogeographic histories using hierarchical approximate Bayesian computation

Directory of Open Access Journals (Sweden)

Takebayashi Naoki

2007-07-01

Full Text Available Abstract Background Although testing for simultaneous divergence (vicariance across different population-pairs that span the same barrier to gene flow is of central importance to evolutionary biology, researchers often equate the gene tree and population/species tree thereby ignoring stochastic coalescent variance in their conclusions of temporal incongruence. In contrast to other available phylogeographic software packages, msBayes is the only one that analyses data from multiple species/population pairs under a hierarchical model. Results msBayes employs approximate Bayesian computation (ABC under a hierarchical coalescent model to test for simultaneous divergence (TSD in multiple co-distributed population-pairs. Simultaneous isolation is tested by estimating three hyper-parameters that characterize the degree of variability in divergence times across co-distributed population pairs while allowing for variation in various within population-pair demographic parameters (sub-parameters that can affect the coalescent. msBayes is a software package consisting of several C and R programs that are run with a Perl "front-end". Conclusion The method reasonably distinguishes simultaneous isolation from temporal incongruence in the divergence of co-distributed population pairs, even with sparse sampling of individuals. Because the estimate step is decoupled from the simulation step, one can rapidly evaluate different ABC acceptance/rejection conditions and the choice of summary statistics. Given the complex and idiosyncratic nature of testing multi-species biogeographic hypotheses, we envision msBayes as a powerful and flexible tool for tackling a wide array of difficult research questions that use population genetic data from multiple co-distributed species. The msBayes pipeline is available for download at http://msbayes.sourceforge.net/ under an open source license (GNU Public License. The msBayes pipeline is comprised of several C and R programs that

Development of uncertainty-based work injury model using Bayesian structural equation modelling.

Science.gov (United States)

Chatterjee, Snehamoy

2014-01-01

This paper proposed a Bayesian method-based structural equation model (SEM) of miners' work injury for an underground coal mine in India. The environmental and behavioural variables for work injury were identified and causal relationships were developed. For Bayesian modelling, prior distributions of SEM parameters are necessary to develop the model. In this paper, two approaches were adopted to obtain prior distribution for factor loading parameters and structural parameters of SEM. In the first approach, the prior distributions were considered as a fixed distribution function with specific parameter values, whereas, in the second approach, prior distributions of the parameters were generated from experts' opinions. The posterior distributions of these parameters were obtained by applying Bayesian rule. The Markov Chain Monte Carlo sampling in the form Gibbs sampling was applied for sampling from the posterior distribution. The results revealed that all coefficients of structural and measurement model parameters are statistically significant in experts' opinion-based priors, whereas, two coefficients are not statistically significant when fixed prior-based distributions are applied. The error statistics reveals that Bayesian structural model provides reasonably good fit of work injury with high coefficient of determination (0.91) and less mean squared error as compared to traditional SEM.

Innovative Bayesian and Parsimony Phylogeny of Dung Beetles (Coleoptera, Scarabaeidae, Scarabaeinae) Enhanced by Ontology-Based Partitioning of Morphological Characters

Science.gov (United States)

Tarasov, Sergei; Génier, François

2015-01-01

Scarabaeine dung beetles are the dominant dung feeding group of insects and are widely used as model organisms in conservation, ecology and developmental biology. Due to the conflicts among 13 recently published phylogenies dealing with the higher-level relationships of dung beetles, the phylogeny of this lineage remains largely unresolved. In this study, we conduct rigorous phylogenetic analyses of dung beetles, based on an unprecedented taxon sample (110 taxa) and detailed investigation of morphology (205 characters). We provide the description of morphology and thoroughly illustrate the used characters. Along with parsimony, traditionally used in the analysis of morphological data, we also apply the Bayesian method with a novel approach that uses anatomy ontology for matrix partitioning. This approach allows for heterogeneity in evolutionary rates among characters from different anatomical regions. Anatomy ontology generates a number of parameter-partition schemes which we compare using Bayes factor. We also test the effect of inclusion of autapomorphies in the morphological analysis, which hitherto has not been examined. Generally, schemes with more parameters were favored in the Bayesian comparison suggesting that characters located on different body regions evolve at different rates and that partitioning of the data matrix using anatomy ontology is reasonable; however, trees from the parsimony and all the Bayesian analyses were quite consistent. The hypothesized phylogeny reveals many novel clades and provides additional support for some clades recovered in previous analyses. Our results provide a solid basis for a new classification of dung beetles, in which the taxonomic limits of the tribes Dichotomiini, Deltochilini and Coprini are restricted and many new tribes must be described. Based on the consistency of the phylogeny with biogeography, we speculate that dung beetles may have originated in the Mesozoic contrary to the traditional view pointing to a

Coalescence preference and droplet size inequality during fluid phase segregation

Science.gov (United States)

Roy, Sutapa

2018-02-01

Using molecular dynamics simulations and scaling arguments, we investigate the coalescence preference dynamics of liquid droplets in a phase-segregating off-critical, single-component fluid. It is observed that the preferential distance of the product drop from its larger parent, during a coalescence event, gets smaller for large parent size inequality. The relative coalescence position exhibits a power-law dependence on the parent size ratio with an exponent q ≃ 3.1 . This value of q is in strong contrast with earlier reports 2.1 and 5.1 in the literature. The dissimilarity is explained by considering the underlying coalescence mechanisms.

Partial coalescence from bubbles to drops

KAUST Repository

Zhang, F. H.

2015-10-07

The coalescence of drops is a fundamental process in the coarsening of emulsions. However, counter-intuitively, this coalescence process can produce a satellite, approximately half the size of the original drop, which is detrimental to the overall coarsening. This also occurs during the coalescence of bubbles, while the resulting satellite is much smaller, approximately 10 %. To understand this difference, we have conducted a set of coalescence experiments using xenon bubbles inside a pressure chamber, where we can continuously raise the pressure from 1 up to 85 atm and thereby vary the density ratio between the inner and outer fluid, from 0.005 up to unity. Using high-speed video imaging, we observe a continuous increase in satellite size as the inner density is varied from the bubble to emulsion-droplet conditions, with the most rapid changes occurring as the bubble density grows up to 15 % of that of the surrounding liquid. We propose a model that successfully relates the satellite size to the capillary wave mode responsible for its pinch-off and the overall deformations from the drainage. The wavelength of the primary wave changes during its travel to the apex, with the instantaneous speed adjusting to the local wavelength. By estimating the travel time of this wave mode on the bubble surface, we also show that the model is consistent with the experiments. This wavenumber is determined by both the global drainage as well as the interface shapes during the rapid coalescence in the neck connecting the two drops or bubbles. The rate of drainage is shown to scale with the density of the inner fluid. Empirically, we find that the pinch-off occurs when 60 % of the bubble fluid has drained from it. Numerical simulations using the volume-of-fluid method with dynamic adaptive grid refinement can reproduce these dynamics, as well as show the associated vortical structure and stirring of the coalescing fluid masses. Enhanced stirring is observed for cases with second

E-commerce System Security Assessment based on Bayesian Network Algorithm Research

OpenAIRE

Ting Li; Xin Li

2013-01-01

Evaluation of e-commerce network security is based on assessment method Bayesian networks, and it first defines the vulnerability status of e-commerce system evaluation index and the vulnerability of the state model of e-commerce systems, and after the principle of the Bayesian network reliability of e-commerce system and the criticality of the vulnerabilities were analyzed, experiments show that the change method is a good evaluation of the security of e-commerce systems.

Bayesian approach and application to operation safety

International Nuclear Information System (INIS)

Procaccia, H.; Suhner, M.Ch.

2003-01-01

The management of industrial risks requires the development of statistical and probabilistic analyses which use all the available convenient information in order to compensate the insufficient experience feedback in a domain where accidents and incidents remain too scarce to perform a classical statistical frequency analysis. The Bayesian decision approach is well adapted to this problem because it integrates both the expertise and the experience feedback. The domain of knowledge is widen, the forecasting study becomes possible and the decisions-remedial actions are strengthen thanks to risk-cost-benefit optimization analyzes. This book presents the bases of the Bayesian approach and its concrete applications in various industrial domains. After a mathematical presentation of the industrial operation safety concepts and of the Bayesian approach principles, this book treats of some of the problems that can be solved thanks to this approach: softwares reliability, controls linked with the equipments warranty, dynamical updating of databases, expertise modeling and weighting, Bayesian optimization in the domains of maintenance, quality control, tests and design of new equipments. A synthesis of the mathematical formulae used in this approach is given in conclusion. (J.S.)

Impact of censoring on learning Bayesian networks in survival modelling.

Science.gov (United States)

Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

2009-11-01

Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from

A Bayesian approach to assess data from radionuclide activity analyses in environmental samples

International Nuclear Information System (INIS)

Barrera, Manuel; Lourdes Romero, M.; Nunez-Lagos, Rafael; Bernardo, Jose M.

2007-01-01

A Bayesian statistical approach is introduced to assess experimental data from the analyses of radionuclide activity concentration in environmental samples (low activities). A theoretical model has been developed that allows the use of known prior information about the value of the measurand (activity), together with the experimental value determined through the measurement. The model has been applied to data of the Inter-laboratory Proficiency Test organised periodically among Spanish environmental radioactivity laboratories that are producing the radiochemical results for the Spanish radioactive monitoring network. A global improvement of laboratories performance is produced when this prior information is taken into account. The prior information used in this methodology is an interval within which the activity is known to be contained, but it could be extended to any other experimental quantity with a different type of prior information available

BEAST: Bayesian evolutionary analysis by sampling trees

Directory of Open Access Journals (Sweden)

Drummond Alexei J

2007-11-01

Full Text Available Abstract Background The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. Results BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/ under the GNU LGPL license. Conclusion BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.

ULTRAMASSIVE BLACK HOLE COALESCENCE

International Nuclear Information System (INIS)

Khan, Fazeel Mahmood; Holley-Bockelmann, Kelly; Berczik, Peter

2015-01-01

Although supermassive black holes (SMBHs) correlate well with their host galaxies, there is an emerging view that outliers exist. Henize 2-10, NGC 4889, and NGC 1277 are examples of SMBHs at least an order of magnitude more massive than their host galaxy suggests. The dynamical effects of such ultramassive central black holes is unclear. Here, we perform direct N-body simulations of mergers of galactic nuclei where one black hole is ultramassive to study the evolution of the remnant and the black hole dynamics in this extreme regime. We find that the merger remnant is axisymmetric near the center, while near the large SMBH influence radius, the galaxy is triaxial. The SMBH separation shrinks rapidly due to dynamical friction, and quickly forms a binary black hole; if we scale our model to the most massive estimate for the NGC 1277 black hole, for example, the timescale for the SMBH separation to shrink from nearly a kiloparsec to less than a parsec is roughly 10 Myr. By the time the SMBHs form a hard binary, gravitational wave emission dominates, and the black holes coalesce in a mere few Myr. Curiously, these extremely massive binaries appear to nearly bypass the three-body scattering evolutionary phase. Our study suggests that in this extreme case, SMBH coalescence is governed by dynamical friction followed nearly directly by gravitational wave emission, resulting in a rapid and efficient SMBH coalescence timescale. We discuss the implications for gravitational wave event rates and hypervelocity star production

Accurate phenotyping: Reconciling approaches through Bayesian model averaging.

Directory of Open Access Journals (Sweden)

Carla Chia-Ming Chen

Full Text Available Genetic research into complex diseases is frequently hindered by a lack of clear biomarkers for phenotype ascertainment. Phenotypes for such diseases are often identified on the basis of clinically defined criteria; however such criteria may not be suitable for understanding the genetic composition of the diseases. Various statistical approaches have been proposed for phenotype definition; however our previous studies have shown that differences in phenotypes estimated using different approaches have substantial impact on subsequent analyses. Instead of obtaining results based upon a single model, we propose a new method, using Bayesian model averaging to overcome problems associated with phenotype definition. Although Bayesian model averaging has been used in other fields of research, this is the first study that uses Bayesian model averaging to reconcile phenotypes obtained using multiple models. We illustrate the new method by applying it to simulated genetic and phenotypic data for Kofendred personality disorder-an imaginary disease with several sub-types. Two separate statistical methods were used to identify clusters of individuals with distinct phenotypes: latent class analysis and grade of membership. Bayesian model averaging was then used to combine the two clusterings for the purpose of subsequent linkage analyses. We found that causative genetic loci for the disease produced higher LOD scores using model averaging than under either individual model separately. We attribute this improvement to consolidation of the cores of phenotype clusters identified using each individual method.

On the use of higher order wave forms in the search for gravitational waves emitted by compact binary coalescences

Science.gov (United States)

McKechan, David J. A.

2010-11-01

This thesis concerns the use, in gravitational wave data analysis, of higher order wave form models of the gravitational radiation emitted by compact binary coalescences. We begin with an introductory chapter that includes an overview of the theory of general relativity, gravitational radiation and ground-based interferometric gravitational wave detectors. We then discuss, in Chapter 2, the gravitational waves emitted by compact binary coalescences, with an explanation of higher order waveforms and how they differ from leading order waveforms we also introduce the post-Newtonian formalism. In Chapter 3 the method and results of a gravitational wave search for low mass compact binary coalescences using a subset of LIGO's 5th science run data are presented and in the subsequent chapter we examine how one could use higher order waveforms in such analyses. We follow the development of a new search algorithm that incorporates higher order waveforms with promising results for detection efficiency and parameter estimation. In Chapter 5, a new method of windowing time-domain waveforms that offers benefit to gravitational wave searches is presented. The final chapter covers the development of a game designed as an outreach project to raise public awareness and understanding of the search for gravitational waves.

Imputation of missing genotypes within LD-blocks relying on the basic coalescent and beyond: consideration of population growth and structure.

Science.gov (United States)

Kabisch, Maria; Hamann, Ute; Lorenzo Bermejo, Justo

2017-10-17

Genotypes not directly measured in genetic studies are often imputed to improve statistical power and to increase mapping resolution. The accuracy of standard imputation techniques strongly depends on the similarity of linkage disequilibrium (LD) patterns in the study and reference populations. Here we develop a novel approach for genotype imputation in low-recombination regions that relies on the coalescent and permits to explicitly account for population demographic factors. To test the new method, study and reference haplotypes were simulated and gene trees were inferred under the basic coalescent and also considering population growth and structure. The reference haplotypes that first coalesced with study haplotypes were used as templates for genotype imputation. Computer simulations were complemented with the analysis of real data. Genotype concordance rates were used to compare the accuracies of coalescent-based and standard (IMPUTE2) imputation. Simulations revealed that, in LD-blocks, imputation accuracy relying on the basic coalescent was higher and less variable than with IMPUTE2. Explicit consideration of population growth and structure, even if present, did not practically improve accuracy. The advantage of coalescent-based over standard imputation increased with the minor allele frequency and it decreased with population stratification. Results based on real data indicated that, even in low-recombination regions, further research is needed to incorporate recombination in coalescence inference, in particular for studies with genetically diverse and admixed individuals. To exploit the full potential of coalescent-based methods for the imputation of missing genotypes in genetic studies, further methodological research is needed to reduce computer time, to take into account recombination, and to implement these methods in user-friendly computer programs. Here we provide reproducible code which takes advantage of publicly available software to facilitate

A Bayesian approach to meta-analysis of plant pathology studies.

Science.gov (United States)

Mila, A L; Ngugi, H K

2011-01-01

Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework

Coalescence dynamics of mobile and immobile fluid interfaces

KAUST Repository

Vakarelski, Ivan Uriev

2018-01-12

Coalescence dynamics between deformable bubbles and droplets can be dramatically affected by the mobility of the interfaces with fully tangentially mobile bubble-liquid or droplet-liquid interfaces expected to accelerate the coalescence by orders of magnitudes. However, there is a lack of systematic experimental investigations that quantify this effect. By using high speed camera imaging we examine the free rise and coalescence of small air-bubbles (100 to 1300 μm in diameter) with a liquid interface. A perfluorocarbon liquid, PP11 is used as a model liquid to investigate coalescence dynamics between fully-mobile and immobile deformable interfaces. The mobility of the bubble surface was determined by measuring the terminal rise velocity of small bubbles rising at Reynolds numbers, Re less than 0.1 and the mobility of free PP11 surface by measuring the deceleration kinetics of the small bubble toward the interface. Induction or film drainage times of a bubble at the mobile PP11-air surface were found to be more than two orders of magnitude shorter compared to the case of bubble and an immobile PP11-water interface. A theoretical model is used to illustrate the effect of hydrodynamics and interfacial mobility on the induction time or film drainage time. The results of this study are expected to stimulate the development of a comprehensive theoretical model for coalescence dynamics between two fully or partially mobile fluid interfaces.

Coalescence of liquid drops: Different models versus experiment

KAUST Repository

Sprittles, J. E.

2012-01-01

The process of coalescence of two identical liquid drops is simulated numerically in the framework of two essentially different mathematical models, and the results are compared with experimental data on the very early stages of the coalescence process reported recently. The first model tested is the "conventional" one, where it is assumed that coalescence as the formation of a single body of fluid occurs by an instant appearance of a liquid bridge smoothly connecting the two drops, and the subsequent process is the evolution of this single body of fluid driven by capillary forces. The second model under investigation considers coalescence as a process where a section of the free surface becomes trapped between the bulk phases as the drops are pressed against each other, and it is the gradual disappearance of this "internal interface" that leads to the formation of a single body of fluid and the conventional model taking over. Using the full numerical solution of the problem in the framework of each of the two models, we show that the recently reported electrical measurements probing the very early stages of the process are better described by the interface formation/disappearance model. New theory-guided experiments are suggested that would help to further elucidate the details of the coalescence phenomenon. As a by-product of our research, the range of validity of different "scaling laws" advanced as approximate solutions to the problem formulated using the conventional model is established. © 2012 American Institute of Physics.

Node insertion in Coalescence Fractal Interpolation Function

International Nuclear Information System (INIS)

Prasad, Srijanani Anurag

2013-01-01

The Iterated Function System (IFS) used in the construction of Coalescence Hidden-variable Fractal Interpolation Function (CHFIF) depends on the interpolation data. The insertion of a new point in a given set of interpolation data is called the problem of node insertion. In this paper, the effect of insertion of new point on the related IFS and the Coalescence Fractal Interpolation Function is studied. Smoothness and Fractal Dimension of a CHFIF obtained with a node are also discussed

Competing risk models in reliability systems, a Weibull distribution model with Bayesian analysis approach

International Nuclear Information System (INIS)

Iskandar, Ismed; Gondokaryono, Yudi Satria

2016-01-01

In reliability theory, the most important problem is to determine the reliability of a complex system from the reliability of its components. The weakness of most reliability theories is that the systems are described and explained as simply functioning or failed. In many real situations, the failures may be from many causes depending upon the age and the environment of the system and its components. Another problem in reliability theory is one of estimating the parameters of the assumed failure models. The estimation may be based on data collected over censored or uncensored life tests. In many reliability problems, the failure data are simply quantitatively inadequate, especially in engineering design and maintenance system. The Bayesian analyses are more beneficial than the classical one in such cases. The Bayesian estimation analyses allow us to combine past knowledge or experience in the form of an apriori distribution with life test data to make inferences of the parameter of interest. In this paper, we have investigated the application of the Bayesian estimation analyses to competing risk systems. The cases are limited to the models with independent causes of failure by using the Weibull distribution as our model. A simulation is conducted for this distribution with the objectives of verifying the models and the estimators and investigating the performance of the estimators for varying sample size. The simulation data are analyzed by using Bayesian and the maximum likelihood analyses. The simulation results show that the change of the true of parameter relatively to another will change the value of standard deviation in an opposite direction. For a perfect information on the prior distribution, the estimation methods of the Bayesian analyses are better than those of the maximum likelihood. The sensitivity analyses show some amount of sensitivity over the shifts of the prior locations. They also show the robustness of the Bayesian analysis within the range

Coalescent Processes with Skewed Offspring Distributions and Nonequilibrium Demography.

Science.gov (United States)

Matuszewski, Sebastian; Hildebrandt, Marcel E; Achaz, Guillaume; Jensen, Jeffrey D

2018-01-01

Nonequilibrium demography impacts coalescent genealogies leaving detectable, well-studied signatures of variation. However, similar genomic footprints are also expected under models of large reproductive skew, posing a serious problem when trying to make inference. Furthermore, current approaches consider only one of the two processes at a time, neglecting any genomic signal that could arise from their simultaneous effects, preventing the possibility of jointly inferring parameters relating to both offspring distribution and population history. Here, we develop an extended Moran model with exponential population growth, and demonstrate that the underlying ancestral process converges to a time-inhomogeneous psi-coalescent. However, by applying a nonlinear change of time scale-analogous to the Kingman coalescent-we find that the ancestral process can be rescaled to its time-homogeneous analog, allowing the process to be simulated quickly and efficiently. Furthermore, we derive analytical expressions for the expected site-frequency spectrum under the time-inhomogeneous psi-coalescent, and develop an approximate-likelihood framework for the joint estimation of the coalescent and growth parameters. By means of extensive simulation, we demonstrate that both can be estimated accurately from whole-genome data. In addition, not accounting for demography can lead to serious biases in the inferred coalescent model, with broad implications for genomic studies ranging from ecology to conservation biology. Finally, we use our method to analyze sequence data from Japanese sardine populations, and find evidence of high variation in individual reproductive success, but few signs of a recent demographic expansion. Copyright © 2018 by the Genetics Society of America.

A Bayesian approach to multi-messenger astronomy: identification of gravitational-wave host galaxies

International Nuclear Information System (INIS)

Fan, XiLong; Messenger, Christopher; Heng, Ik Siong

2014-01-01

We present a general framework for incorporating astrophysical information into Bayesian parameter estimation techniques used by gravitational wave data analysis to facilitate multi-messenger astronomy. Since the progenitors of transient gravitational wave events, such as compact binary coalescences, are likely to be associated with a host galaxy, improvements to the source sky location estimates through the use of host galaxy information are explored. To demonstrate how host galaxy properties can be included, we simulate a population of compact binary coalescences and show that for ∼8.5% of simulations within 200 Mpc, the top 10 most likely galaxies account for a ∼50% of the total probability of hosting a gravitational wave source. The true gravitational wave source host galaxy is in the top 10 galaxy candidates ∼10% of the time. Furthermore, we show that by including host galaxy information, a better estimate of the inclination angle of a compact binary gravitational wave source can be obtained. We also demonstrate the flexibility of our method by incorporating the use of either the B or K band into our analysis.

An application of the 'Bayesian cohort model' to nuclear power plant cost analyses

International Nuclear Information System (INIS)

Ono, Kenji; Nakamura, Takashi

2002-01-01

We have developed a new method for identifying the effects of calendar year, plant age and commercial operation starting year on the costs and performances of nuclear power plants and also developed an analysis system running on personal computers. The method extends the Bayesian cohort model for time series social survey data proposed by one of the authors. The proposed method was shown to be able to separate the above three effects more properly than traditional methods such as taking simple means by time domain. The analyses of US nuclear plant cost and performance data by using the proposed method suggest that many of the US plants spent relatively long time and much capital cost for modification at their age of about 10 to 20 years, but that, after those ages, they performed fairly well with lower and stabilized O and M and additional capital costs. (author)

From qualitative reasoning models to Bayesian-based learner modeling

NARCIS (Netherlands)

Milošević, U.; Bredeweg, B.; de Kleer, J.; Forbus, K.D.

2010-01-01

Assessing the knowledge of a student is a fundamental part of intelligent learning environments. We present a Bayesian network based approach to dealing with uncertainty when estimating a learner’s state of knowledge in the context of Qualitative Reasoning (QR). A proposal for a global architecture

Simulations of plasma heating caused by the coalescence of multiple current loops in a proton-boron fusion plasma

International Nuclear Information System (INIS)

Haruki, T.; Yousefi, H. R.; Sakai, J.-I.

2010-01-01

Two dimensional particle-in-cell simulations of a dense plasma focus were performed to investigate a plasma heating process caused by the coalescence of multiple current loops in a proton-boron-electron plasma. Recently, it was reported that the electric field produced during the coalescence of two current loops in a proton-boron-electron plasma heats up all plasma species; proton-boron nuclear fusion may therefore be achievable using a dense plasma focus device. Based on this work, the coalescence process for four and eight current loops was investigated. It was found that the return current plays an important role in both the current pinch and the plasma heating. The coalescence of four current loops led to the breakup of the return current from the pinched plasma, resulting in plasma heating. For the coalescence of eight current loops, the plasma was confined by the pinch but the plasma heating was smaller than the two and four loop cases. Therefore the heating associated with current loop coalescence depends on the number of initial current loops. These results are useful for understanding the coalescence of multiple current loops in a proton-boron-electron plasma.

Finding the best resolution for the Kingman-Tajima coalescent: theory and applications.

Science.gov (United States)

Sainudiin, Raazesh; Stadler, Tanja; Véber, Amandine

2015-05-01

Many summary statistics currently used in population genetics and in phylogenetics depend only on a rather coarse resolution of the underlying tree (the number of extant lineages, for example). Hence, for computational purposes, working directly on these resolutions appears to be much more efficient. However, this approach seems to have been overlooked in the past. In this paper, we describe six different resolutions of the Kingman-Tajima coalescent together with the corresponding Markov chains, which are essential for inference methods. Two of the resolutions are the well-known n-coalescent and the lineage death process due to Kingman. Two other resolutions were mentioned by Kingman and Tajima, but never explicitly formalized. Another two resolutions are novel, and complete the picture of a multi-resolution coalescent. For all of them, we provide the forward and backward transition probabilities, the probability of visiting a given state as well as the probability of a given realization of the full Markov chain. We also provide a description of the state-space that highlights the computational gain obtained by working with lower-resolution objects. Finally, we give several examples of summary statistics that depend on a coarser resolution of Kingman's coalescent, on which simulations are usually based.

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

Science.gov (United States)

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

Understanding Computational Bayesian Statistics

CERN Document Server

Bolstad, William M

2011-01-01

A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic

Bayesian statistics an introduction

CERN Document Server

Lee, Peter M

2012-01-01

Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel

Online variational Bayesian filtering-based mobile target tracking in wireless sensor networks.

Science.gov (United States)

Zhou, Bingpeng; Chen, Qingchun; Li, Tiffany Jing; Xiao, Pei

2014-11-11

The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision's randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer-Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying.

Liquid Marble Coalescence and Triggered Microreaction Driven by Acoustic Levitation.

Science.gov (United States)

Chen, Zhen; Zang, Duyang; Zhao, Liang; Qu, Mengfei; Li, Xu; Li, Xiaoguang; Li, Lixin; Geng, Xingguo

2017-06-27

Liquid marbles show promising potential for application in the microreactor field. Control of the coalescence between two or among multiple liquid marbles is critical; however, the successful merging of two isolated marbles is difficult because of their mechanically robust particle shells. In this work, the coalescence of multiple liquid marbles was achieved via acoustic levitation. The dynamic behaviors of the liquid marbles were monitored by a high-speed camera. Driven by the sound field, the liquid marbles moved toward each other, collided, and eventually coalesced into a larger single marble. The underlying mechanisms of this process were probed via sound field simulation and acoustic radiation pressure calculation. The results indicated that the pressure gradient on the liquid marble surface favors the formation of a liquid bridge between the liquid marbles, resulting in their coalescence. A preliminary indicator reaction was induced by the coalescence of dual liquid marbles, which suggests that expected chemical reactions can be successfully triggered with multiple reagents contained in isolated liquid marbles via acoustic levitation.

Cosmology with coalescing massive black holes

International Nuclear Information System (INIS)

Hughes, Scott A; Holz, Daniel E

2003-01-01

The gravitational waves generated in the coalescence of massive binary black holes will be measurable by LISA to enormous distances. Redshifts z ∼ 10 or larger (depending somewhat on the mass of the binary) can potentially be probed by such measurements, suggesting that binary coalescences can be made into cosmological tools. We discuss two particularly interesting types of probe. First, by combining gravitational-wave measurements with information about the cosmography of the universe, we can study the evolution of black-hole masses and merger rates as a function of redshift, providing information about the growth of structures at high redshift and possibly constraining hierarchical merger scenarios. Second, if it is possible to associate an 'electromagnetic' counterpart with a coalescence, it may be possible to measure both redshift and luminosity distance to an event with less than ∼1% error. Such a measurement would constitute an amazingly precise cosmological standard candle. Unfortunately, gravitational lensing uncertainties will reduce the quality of this candle significantly. Though not as amazing as might have been hoped, such a candle would nonetheless very usefully complement other distance-redshift probes, in particular providing a valuable check on systematic effects in such measurements

Bubble coalescence dynamics and supersaturation in electrolytic gas evolution

Energy Technology Data Exchange (ETDEWEB)

Stover, R.L. [Univ. of California, Berkeley, CA (United States). Dept. of Chemical Engineering]|[Lawrence Berkeley National Lab., CA (United States). Energy and Environment Div.

1996-08-01

The apparatus and procedures developed in this research permit the observation of electrolytic bubble coalescence, which heretofore has not been possible. The influence of bubble size, electrolyte viscosity, surface tension, gas type, and pH on bubble coalescence was examined. The Navier-Stokes equations with free surface boundary conditions were solved numerically for the full range of experimental variables that were examined. Based on this study, the following mechanism for bubble coalescence emerges: when two gas bubbles coalesce, the surface energy decreases as the curvature and surface area of the resultant bubble decrease, and the energy is imparted into the surrounding liquid. The initial motion is driven by the surface tension and slowed by the inertia and viscosity of the surrounding fluid. The initial velocity of the interface is approximately proportional to the square root of the surface tension and inversely proportional to the square root of the bubble radius. Fluid inertia sustains the oblate/prolate oscillations of the resultant bubble. The period of the oscillations varies with the bubble radius raised to the 3/2 power and inversely with the square root of the surface tension. Viscous resistance dampens the oscillations at a rate proportional to the viscosity and inversely proportional to the square of the bubble radius. The numerical simulations were consistent with most of the experimental results. The differences between the computed and measured saddle point decelerations and periods suggest that the surface tension in the experiments may have changed during each run. By adjusting the surface tension in the simulation, a good fit was obtained for the 150-{micro}m diameter bubbles. The simulations fit the experiments on larger bubbles with very little adjustment of surface tension. A more focused analysis should be done to elucidate the phenomena that occur in the receding liquid film immediately following rupture.

Coalescent Modelling Suggests Recent Secondary-Contact of Cryptic Penguin Species.

Science.gov (United States)

Grosser, Stefanie; Burridge, Christopher P; Peucker, Amanda J; Waters, Jonathan M

2015-01-01

Molecular genetic analyses present powerful tools for elucidating demographic and biogeographic histories of taxa. Here we present genetic evidence showing a dynamic history for two cryptic lineages within Eudyptula, the world's smallest penguin. Specifically, we use a suite of genetic markers to reveal that two congeneric taxa ('Australia' and 'New Zealand') co-occur in southern New Zealand, with only low levels of hybridization. Coalescent modelling suggests that the Australian little penguin only recently expanded into southern New Zealand. Analyses conducted under time-dependent molecular evolutionary rates lend support to the hypothesis of recent anthropogenic turnover, consistent with shifts detected in several other New Zealand coastal vertebrate taxa. This apparent turnover event highlights the dynamic nature of the region's coastal ecosystem.

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

Science.gov (United States)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

Science.gov (United States)

Herskovits, Edward H.; Gerring, Joan P.

2003-01-01

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

The coalescence of heterogeneous liquid metal on nano substrate

Science.gov (United States)

Wang, Long; Li, Yifan; Zhou, Xuyan; Li, Tao; Li, Hui

2017-06-01

Molecular dynamics simulation has been performed to study the asymmetric coalescence of heterogeneous liquid metal on graphene. Simulation results show that the anomalies in the drop coalescence is mainly caused by the wettability of heterogeneous liquid metal. The silver atoms incline to distribute on the outer layer of the gold and copper droplets, revealing that the structure is determined by the interaction between different metal atoms. The coalescence and fusion of heterogeneous liquid metal drop can be predicted by comparing the wettability and the atomic mass of metallic liquid drops, which has important implications in the industrial application such as ink-jet printing and metallurgy.

ENERGY AWARE NETWORK: BAYESIAN BELIEF NETWORKS BASED DECISION MANAGEMENT SYSTEM

Directory of Open Access Journals (Sweden)

Santosh Kumar Chaudhari

2011-06-01

Full Text Available A Network Management System (NMS plays a very important role in managing an ever-evolving telecommunication network. Generally an NMS monitors & maintains the health of network elements. The growing size of the network warrants extra functionalities from the NMS. An NMS provides all kinds of information about networks which can be used for other purposes apart from monitoring & maintaining networks like improving QoS & saving energy in the network. In this paper, we add another dimension to NMS services, namely, making an NMS energy aware. We propose a Decision Management System (DMS framework which uses a machine learning technique called Bayesian Belief Networks (BBN, to make the NMS energy aware. The DMS is capable of analysing and making control decisions based on network traffic. We factor in the cost of rerouting and power saving per port. Simulations are performed on standard network topologies, namely, ARPANet and IndiaNet. It is found that ~2.5-6.5% power can be saved.

Wavelet-Based Bayesian Methods for Image Analysis and Automatic Target Recognition

National Research Council Canada - National Science Library

Nowak, Robert

2001-01-01

.... We have developed two new techniques. First, we have develop a wavelet-based approach to image restoration and deconvolution problems using Bayesian image models and an alternating-maximation method...

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.

2012-01-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical

Coalescence model of two collinear cracks existing in steam generator tubes

International Nuclear Information System (INIS)

Moon, S.-I.; Chang, Y.-S.; Kim, Y.-J.; Park, Y.-W.; Song, M.-H.; Choi, Y.-H.; Lee, J.-H.

2005-01-01

The 40% of wall thickness criterion has been used as a plugging rule of steam generator tubes but it can be applicable just to a single-cracked tubes. In the previous studies preformed by the authors, a total of 10 local failure prediction models were introduced to estimate the coalescence load of two adjacent collinear through-wall cracks existing in thin plates, and the reaction force model and plastic zone contact model were selected as optimum models among them. The objective of this study is to verify the applicability of the proposed optimum local failure prediction models to the tubes with two collinear through-wall cracks. For this, a series of plastic collapse tests and finite element analyses were carried out using the tubes containing two collinear through-wall cracks. It has been shown that the proposed optimum failure models can predict the local failure behavior of two collinear through-wall cracks existing in tubes well. And a coalescence evaluation diagram was developed which can be used to determine whether the adjacent cracks detected by NED coalsece or not. (authors)

Coalescence in dense water/oil dispersions

Energy Technology Data Exchange (ETDEWEB)

Thunem, H

1993-06-01

This project has been divided into three parts. The first part has been to review a large amount of literature to obtain models describing separate stages of the coalescence of a single drop at an interface. These stages include the drop deformation, the film thinning, the critical film thickness, and the drop breakup. The second part has been to evaluate the different models and select which to use in the development of the OneDrop program. The models describing drop deformation and film thinning were supplied by Charles and Mason, however the film thinning model has been slightly enhanced in this project. The models and the enhancements made have been compared to experimental data from the literature and from work done by undergraduate students at our department. The third part of the project has been to implement the models to drop-drop coalescence, and to write the SIM program to simulate the coalescence in a system of many drops. We use the same equations as in the OneDrop case, except for the film thinning. But by using a similar derivation as for the OneDrop case, an equation for the SIM case has been developed. We have made the assumption that the physical phenomena regarding drop deformation, film thinning and critical film thickness are the same in the OneDrop and SIM cases, so the experimental validation of OneDrop also apply to SIM. By using the results from the SIM program, we can obtain some information about how different physical parameters will affect the collision frequency and collision efficiency. We may use this information to derive empirical equations describing these parameters effect on the coalescence probability in a dispersion. 207 refs., 83 figs., 21 tabs.

A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution

Directory of Open Access Journals (Sweden)

Shaobo Li

2018-03-01

Full Text Available Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts’ knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural or functional features along with customer satisfaction, manufacturing cost, sale price, market sales, and indirect factors are modeled as random variables denoted as nodes in the Bayesian networks. The structure of the Bayesian model is then determined based on the historical data, which captures the dynamic sophisticated relationship of customer demands of a product, structural design, and market performance. Application of our approach to an electric toothbrush product family evolution design problem shows that our model allows for designers to interrogate with the model and obtain theoretical and decision support for dynamic product feature design process.

Development and validation of models for bubble coalescence and breakup

Energy Technology Data Exchange (ETDEWEB)

Liao, Yiaxiang

2013-10-08

A generalized model for bubble coalescence and breakup has been developed, which is based on a comprehensive survey of existing theories and models. One important feature of the model is that all important mechanisms leading to bubble coalescence and breakup in a turbulent gas-liquid flow are considered. The new model is tested extensively in a 1D Test Solver and a 3D CFD code ANSYS CFX for the case of vertical gas-liquid pipe flow under adiabatic conditions, respectively. Two kinds of extensions of the standard multi-fluid model, i.e. the discrete population model and the inhomogeneous MUSIG (multiple-size group) model, are available in the two solvers, respectively. These extensions with suitable closure models such as those for coalescence and breakup are able to predict the evolution of bubble size distribution in dispersed flows and to overcome the mono-dispersed flow limitation of the standard multi-fluid model. For the validation of the model the high quality database of the TOPFLOW L12 experiments for air-water flow in a vertical pipe was employed. A wide range of test points, which cover the bubbly flow, turbulent-churn flow as well as the transition regime, is involved in the simulations. The comparison between the simulated results such as bubble size distribution, gas velocity and volume fraction and the measured ones indicates a generally good agreement for all selected test points. As the superficial gas velocity increases, bubble size distribution evolves via coalescence dominant regimes first, then breakup-dominant regimes and finally turns into a bimodal distribution. The tendency of the evolution is well reproduced by the model. However, the tendency is almost always overestimated, i.e. too much coalescence in the coalescence dominant case while too much breakup in breakup dominant ones. The reason of this problem is discussed by studying the contribution of each coalescence and breakup mechanism at different test points. The redistribution of the

Development and validation of models for bubble coalescence and breakup

International Nuclear Information System (INIS)

Liao, Yiaxiang

2013-01-01

A generalized model for bubble coalescence and breakup has been developed, which is based on a comprehensive survey of existing theories and models. One important feature of the model is that all important mechanisms leading to bubble coalescence and breakup in a turbulent gas-liquid flow are considered. The new model is tested extensively in a 1D Test Solver and a 3D CFD code ANSYS CFX for the case of vertical gas-liquid pipe flow under adiabatic conditions, respectively. Two kinds of extensions of the standard multi-fluid model, i.e. the discrete population model and the inhomogeneous MUSIG (multiple-size group) model, are available in the two solvers, respectively. These extensions with suitable closure models such as those for coalescence and breakup are able to predict the evolution of bubble size distribution in dispersed flows and to overcome the mono-dispersed flow limitation of the standard multi-fluid model. For the validation of the model the high quality database of the TOPFLOW L12 experiments for air-water flow in a vertical pipe was employed. A wide range of test points, which cover the bubbly flow, turbulent-churn flow as well as the transition regime, is involved in the simulations. The comparison between the simulated results such as bubble size distribution, gas velocity and volume fraction and the measured ones indicates a generally good agreement for all selected test points. As the superficial gas velocity increases, bubble size distribution evolves via coalescence dominant regimes first, then breakup-dominant regimes and finally turns into a bimodal distribution. The tendency of the evolution is well reproduced by the model. However, the tendency is almost always overestimated, i.e. too much coalescence in the coalescence dominant case while too much breakup in breakup dominant ones. The reason of this problem is discussed by studying the contribution of each coalescence and breakup mechanism at different test points. The redistribution of the

Bayesian Spatial NBDA for Diffusion Data with Home-Base Coordinates.

Directory of Open Access Journals (Sweden)

Glenna F Nightingale

Full Text Available Network-based diffusion analysis (NBDA is a statistical method that allows the researcher to identify and quantify a social influence on the spread of behaviour through a population. Hitherto, NBDA analyses have not directly modelled spatial population structure. Here we present a spatial extension of NBDA, applicable to diffusion data where the spatial locations of individuals in the population, or of their home bases or nest sites, are available. The method is based on the estimation of inter-individual associations (for association matrix construction from the mean inter-point distances as represented on a spatial point pattern of individuals, nests or home bases. We illustrate the method using a simulated dataset, and show how environmental covariates (such as that obtained from a satellite image, or from direct observations in the study area can also be included in the analysis. The analysis is conducted in a Bayesian framework, which has the advantage that prior knowledge of the rate at which the individuals acquire a given task can be incorporated into the analysis. This method is especially valuable for studies for which detailed spatially structured data, but no other association data, is available. Technological advances are making the collection of such data in the wild more feasible: for example, bio-logging facilitates the collection of a wide range of variables from animal populations in the wild. We provide an R package, spatialnbda, which is hosted on the Comprehensive R Archive Network (CRAN. This package facilitates the construction of association matrices with the spatial x and y coordinates as the input arguments, and spatial NBDA analyses.

Using acoustics to study and stimulate the coalescence of oil drops surrounded by water

Energy Technology Data Exchange (ETDEWEB)

Gardner, E.A.; Apfel, R.E. (Yale Univ., New Haven, CT (United States). Dept. of Mechanical Engineering)

1993-08-01

The coalescence of oil drops in water is studied using acoustic levitation and stimulated with acoustic cavitation. Unlike most earlier studies, which investigate the coalescence of a single drop with an initially planar interface, the use of acoustic radiation forces allows two drops to be brought into contact and allowed to coalesce. The acoustic technique has the advantage over other drop-drop coalescence systems in that the drops remain in contact until they coalesce without the use of solid supports to control them. Additionally, acoustic cavitation is observed to deposit sufficient energy in the oil-water interface to trigger the coalescence of a pair of 2-mm-diameter drops. This stimulation mechanism could have application to emulsion breaking. Some of the factors that affect spontaneous and stimulated coalescence are investigated.

Predicting Catastrophic Phase Inversion on the Basis of Droplet Coalescence Kinetics

NARCIS (Netherlands)

Vaessen, G.E.J.; Visschers, M.; Stein, H.N.

1996-01-01

A predictive model for catastrophic phase inversion, based on the kinetics of droplet breakup and coalescence, is presented here. Two inversion mechanisms can be distinguished, depending on the direction of the phase inversion process. With the surfactant predominantly present in the dispersed

Rapid molecular evolution of human bocavirus revealed by Bayesian coalescent inference.

Science.gov (United States)

Zehender, Gianguglielmo; De Maddalena, Chiara; Canuti, Marta; Zappa, Alessandra; Amendola, Antonella; Lai, Alessia; Galli, Massimo; Tanzi, Elisabetta

2010-03-01

Human bocavirus (HBoV) is a linear single-stranded DNA virus belonging to the Parvoviridae family that has recently been isolated from the upper respiratory tract of children with acute respiratory infection. All of the strains observed so far segregate into two genotypes (1 and 2) with a low level of polymorphism. Given the recent description of the infection and the lack of epidemiological and molecular data, we estimated the virus's rates of molecular evolution and population dynamics. A dataset of forty-nine dated VP2 sequences, including also eight new isolates obtained from pharyngeal swabs of Italian patients with acute respiratory tract infections, was submitted to phylogenetic analysis. The model parameters, evolutionary rates and population dynamics were co-estimated using a Bayesian Markov Chain Monte Carlo approach, and site-specific positive and negative selection was also investigated. Recombination was investigated by seven different methods and one suspected recombinant strain was excluded from further analysis. The estimated mean evolutionary rate of HBoV was 8.6x10(-4)subs/site/year, and that of the 1st+2nd codon positions was more than 15 times less than that of the 3rd codon position. Viral population dynamics analysis revealed that the two known genotypes diverged recently (mean tMRCA: 24 years), and that the epidemic due to HBoV genotype 2 grew exponentially at a rate of 1.01year(-1). Selection analysis of the partial VP2 showed that 8.5% of sites were under significant negative pressure and the absence of positive selection. Our results show that, like other parvoviruses, HBoV is characterised by a rapid evolution. The low level of polymorphism is probably due to a relatively recent divergence between the circulating genotypes and strong purifying selection acting on viral antigens.

Coalescence kinetics of oil-in-water emulsions studied with microfluidics

NARCIS (Netherlands)

Krebs, T.; Schroen, C.G.P.H.; Boom, R.M.

2013-01-01

We report the results of experiments on the coalescence dynamics in flowing oil-in-water emulsions using an integrated microfluidic device. The microfluidic circuit permits direct observation of shear-induced collisions and coalescence events between emulsion droplets. Three mineral oils with a

Learning to Detect Traffic Incidents from Data Based on Tree Augmented Naive Bayesian Classifiers

Directory of Open Access Journals (Sweden)

Dawei Li

2017-01-01

Full Text Available This study develops a tree augmented naive Bayesian (TAN classifier based incident detection algorithm. Compared with the Bayesian networks based detection algorithms developed in the previous studies, this algorithm has less dependency on experts’ knowledge. The structure of TAN classifier for incident detection is learned from data. The discretization of continuous attributes is processed using an entropy-based method automatically. A simulation dataset on the section of the Ayer Rajah Expressway (AYE in Singapore is used to demonstrate the development of proposed algorithm, including wavelet denoising, normalization, entropy-based discretization, and structure learning. The performance of TAN based algorithm is evaluated compared with the previous developed Bayesian network (BN based and multilayer feed forward (MLF neural networks based algorithms with the same AYE data. The experiment results show that the TAN based algorithms perform better than the BN classifiers and have a similar performance to the MLF based algorithm. However, TAN based algorithm would have wider vista of applications because the theory of TAN classifiers is much less complicated than MLF. It should be found from the experiment that the TAN classifier based algorithm has a significant superiority over the speed of model training and calibration compared with MLF.

Bayesian analyses of Yemeni mitochondrial genomes suggest multiple migration events with Africa and Western Eurasia.

Science.gov (United States)

Vyas, Deven N; Kitchen, Andrew; Miró-Herrans, Aida T; Pearson, Laurel N; Al-Meeri, Ali; Mulligan, Connie J

2016-03-01

Anatomically, modern humans are thought to have migrated out of Africa ∼60,000 years ago in the first successful global dispersal. This initial migration may have passed through Yemen, a region that has experienced multiple migrations events with Africa and Eurasia throughout human history. We use Bayesian phylogenetics to determine how ancient and recent migrations have shaped Yemeni mitogenomic variation. We sequenced 113 mitogenomes from multiple Yemeni regions with a focus on haplogroups M, N, and L3(xM,N) as these groups have the oldest evolutionary history outside of Africa. We performed Bayesian evolutionary analyses to generate time-measured phylogenies calibrated by Neanderthal and Denisovan mitogenomes in order to determine the age of Yemeni-specific clades. As defined by Yemeni monophyly, Yemeni in situ evolution is limited to the Holocene or latest Pleistocene (ages of clades in subhaplogroups L3b1a1a, L3h2, L3x1, M1a1f, M1a5, N1a1a3, and N1a3 range from 2 to 14 kya) and is often situated within broader Horn of Africa/southern Arabia in situ evolution (L3h2, L3x1, M1a1f, M1a5, and N1a1a3 ages range from 7 to 29 kya). Five subhaplogroups show no monophyly and are candidates for Holocene migration into Yemen (L0a2a2a, L3d1a1a, L3i2, M1a1b, and N1b1a). Yemeni mitogenomes are largely the product of Holocene migration, and subsequent in situ evolution, from Africa and western Eurasia. However, we hypothesize that recent population movements may obscure the genetic signature of more ancient migrations. Additional research, e.g., analyses of Yemeni nuclear genetic data, is needed to better reconstruct the complex population and migration histories associated with Out of Africa. © 2015 Wiley Periodicals, Inc.

Electrohydrodynamic coalescence of droplets using an embedded potential flow model

Science.gov (United States)

Garzon, M.; Gray, L. J.; Sethian, J. A.

2018-03-01

The coalescence, and subsequent satellite formation, of two inviscid droplets is studied numerically. The initial drops are taken to be of equal and different sizes, and simulations have been carried out with and without the presence of an electrical field. The main computational challenge is the tracking of a free surface that changes topology. Coupling level set and boundary integral methods with an embedded potential flow model, we seamlessly compute through these singular events. As a consequence, the various coalescence modes that appear depending upon the relative ratio of the parent droplets can be studied. Computations of first stage pinch-off, second stage pinch-off, and complete engulfment are analyzed and compared to recent numerical studies and laboratory experiments. Specifically, we study the evolution of bridge radii and the related scaling laws, the minimum drop radii evolution from coalescence to satellite pinch-off, satellite sizes, and the upward stretching of the near cylindrical protrusion at the droplet top. Clear evidence of partial coalescence self-similarity is presented for parent droplet ratios between 1.66 and 4. This has been possible due to the fact that computational initial conditions only depend upon the mother droplet size, in contrast with laboratory experiments where the difficulty in establishing the same initial physical configuration is well known. The presence of electric forces changes the coalescence patterns, and it is possible to control the satellite droplet size by tuning the electrical field intensity. All of the numerical results are in very good agreement with recent laboratory experiments for water droplet coalescence.

Population dynamics of American horseshoe crabs—historic climatic events and recent anthropogenic pressures

DEFF Research Database (Denmark)

Faurby, Søren; King, Tim L.; Obst, Matthias

2010-01-01

with Bayesian coalescent-based methods. The analysis showed strong declines in population sizes throughout the species’ distribution except in the geographically isolated southern-most population in Mexico, where a strong increase in population size was inferred. Analyses suggested that demographic changes....... This study highlights the importance of considering both climatic changes and anthropogenic effects in efforts to understand population dynamics—a topic which is highly relevant in the ongoing assessments of the effects of climate change and overharvest....

Cryptic genetic diversity is paramount in small-bodied amphibians of the genus Euparkerella (Anura: Craugastoridae endemic to the Brazilian Atlantic forest.

Directory of Open Access Journals (Sweden)

Luciana A Fusinatto

Full Text Available Morphological similarity associated to restricted distributions and low dispersal abilities make the direct developing "Terrarana" frogs of the genus Euparkerella a good model for examining diversification processes. We here infer phylogenetic relationships within the genus Euparkerella, using DNA sequence data from one mitochondrial and four nuclear genes coupled with traditional Bayesian phylogenetic reconstruction approaches and more recent coalescent methods of species tree inference. We also used Bayesian clustering analysis and a recent Bayesian coalescent-based approach specifically to infer species delimitation. The analysis of 39 individuals from the four known Euparkerella species uncovered high levels of genetic diversity, especially within the two previously morphologically-defined E. cochranae and E. brasiliensis. Within these species, the gene trees at five independent loci and trees from combined data (concatenated dataset and the species tree uncovered six deeply diverged and geographically coherent evolutionary units, which may have diverged between the Miocene and the Pleistocene. These six units were also uncovered in the Bayesian clustering analysis, and supported by the Bayesian coalescent-based species delimitation (BPP, and Genealogical Sorting Index (GSI, providing thus strong evidence for underestimation of the current levels of diversity within Euparkerella. The cryptic diversity now uncovered opens new opportunities to examine the origins and maintenance of microendemism in the context of spatial heterogeneity and/or human induced fragmentation of the highly threatened Brazilian Atlantic forest hotspot.

Combining Volcano Monitoring Timeseries Analyses with Bayesian Belief Networks to Update Hazard Forecast Estimates

Science.gov (United States)

Odbert, Henry; Hincks, Thea; Aspinall, Willy

2015-04-01

Volcanic hazard assessments must combine information about the physical processes of hazardous phenomena with observations that indicate the current state of a volcano. Incorporating both these lines of evidence can inform our belief about the likelihood (probability) and consequences (impact) of possible hazardous scenarios, forming a basis for formal quantitative hazard assessment. However, such evidence is often uncertain, indirect or incomplete. Approaches to volcano monitoring have advanced substantially in recent decades, increasing the variety and resolution of multi-parameter timeseries data recorded at volcanoes. Interpreting these multiple strands of parallel, partial evidence thus becomes increasingly complex. In practice, interpreting many timeseries requires an individual to be familiar with the idiosyncrasies of the volcano, monitoring techniques, configuration of recording instruments, observations from other datasets, and so on. In making such interpretations, an individual must consider how different volcanic processes may manifest as measureable observations, and then infer from the available data what can or cannot be deduced about those processes. We examine how parts of this process may be synthesised algorithmically using Bayesian inference. Bayesian Belief Networks (BBNs) use probability theory to treat and evaluate uncertainties in a rational and auditable scientific manner, but only to the extent warranted by the strength of the available evidence. The concept is a suitable framework for marshalling multiple strands of evidence (e.g. observations, model results and interpretations) and their associated uncertainties in a methodical manner. BBNs are usually implemented in graphical form and could be developed as a tool for near real-time, ongoing use in a volcano observatory, for example. We explore the application of BBNs in analysing volcanic data from the long-lived eruption at Soufriere Hills Volcano, Montserrat. We show how our method

The coalescence instability in solar flares

Science.gov (United States)

Tajima, T.; Brunel, F.; Sakai, J.-I.; Vlahos, L.; Kundu, M. R.

1985-01-01

The nonlinear coalescence instability of current carrying solar loops can explain many of the characteristics of the solar flares such as their impulsive nature, heating and high energy particle acceleration, amplitude oscillations of electromagnetic and emission as well as the characteristics of two-dimensional microwave images obtained during a flare. The plasma compressibility leads to the explosive phase of loop coalescence and its overshoot results in amplitude oscillations in temperatures by adiabatic compression and decompression. It is noted that the presence of strong electric fields and super-Alfvenic flows during the course of the instability play an important role in the production of nonthermal particles. A qualitative explanation on the physical processes taking place during the nonlinear stages of the instability is given.

The coalescence instability in solar flares

International Nuclear Information System (INIS)

Tajima, T.; Brunel, F.; Sakai, J.I.; Vlahos, L.; Kundu, M.R.

1984-01-01

The non-linear coalescence instability of current carrying solar loops can explain many of the characteristics of the solar flares such as their impulsive nature, heating and high energy particle acceleration, amplitude oscillations of electromagnetic emission as well as the characteristics of 2-D microwave images obtained during a flare. The plasma compressibility leads to the explosive phase of loop coalescence and its overshoot results in amplitude oscillations in temperatures by adiabatic compression and decompression. We note that the presence of strong electric fields and super-Alfvenic flows during the course of the instabilty paly an important role in the production of non-thermal particles. A qualitative explanation on the physical processes taking place during the non-linear stages of the instability is given. (author)

Predicting Mycobacterium tuberculosis Complex Clades Using Knowledge-Based Bayesian Networks

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Minoo Aminian

2014-01-01

Full Text Available We develop a novel approach for incorporating expert rules into Bayesian networks for classification of Mycobacterium tuberculosis complex (MTBC clades. The proposed knowledge-based Bayesian network (KBBN treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes, since these are routinely gathered from MTBC isolates of tuberculosis (TB patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web.

Bayesian risk-based decision method for model validation under uncertainty

International Nuclear Information System (INIS)

Jiang Xiaomo; Mahadevan, Sankaran

2007-01-01

This paper develops a decision-making methodology for computational model validation, considering the risk of using the current model, data support for the current model, and cost of acquiring new information to improve the model. A Bayesian decision theory-based method is developed for this purpose, using a likelihood ratio as the validation metric for model assessment. An expected risk or cost function is defined as a function of the decision costs, and the likelihood and prior of each hypothesis. The risk is minimized through correctly assigning experimental data to two decision regions based on the comparison of the likelihood ratio with a decision threshold. A Bayesian validation metric is derived based on the risk minimization criterion. Two types of validation tests are considered: pass/fail tests and system response value measurement tests. The methodology is illustrated for the validation of reliability prediction models in a tension bar and an engine blade subjected to high cycle fatigue. The proposed method can effectively integrate optimal experimental design into model validation to simultaneously reduce the cost and improve the accuracy of reliability model assessment

Bayesian molecular dating: opening up the black box.

Science.gov (United States)

Bromham, Lindell; Duchêne, Sebastián; Hua, Xia; Ritchie, Andrew M; Duchêne, David A; Ho, Simon Y W

2018-05-01

Molecular dating analyses allow evolutionary timescales to be estimated from genetic data, offering an unprecedented capacity for investigating the evolutionary past of all species. These methods require us to make assumptions about the relationship between genetic change and evolutionary time, often referred to as a 'molecular clock'. Although initially regarded with scepticism, molecular dating has now been adopted in many areas of biology. This broad uptake has been due partly to the development of Bayesian methods that allow complex aspects of molecular evolution, such as variation in rates of change across lineages, to be taken into account. But in order to do this, Bayesian dating methods rely on a range of assumptions about the evolutionary process, which vary in their degree of biological realism and empirical support. These assumptions can have substantial impacts on the estimates produced by molecular dating analyses. The aim of this review is to open the 'black box' of Bayesian molecular dating and have a look at the machinery inside. We explain the components of these dating methods, the important decisions that researchers must make in their analyses, and the factors that need to be considered when interpreting results. We illustrate the effects that the choices of different models and priors can have on the outcome of the analysis, and suggest ways to explore these impacts. We describe some major research directions that may improve the reliability of Bayesian dating. The goal of our review is to help researchers to make informed choices when using Bayesian phylogenetic methods to estimate evolutionary rates and timescales. © 2017 Cambridge Philosophical Society.

Assessment of partial coalescence in whippable oil-in-water food emulsions.

Science.gov (United States)

Petrut, Raul Flaviu; Danthine, Sabine; Blecker, Christophe

2016-03-01

Partial coalescence influences to a great extent the properties of final food products such as ice cream and whipped toppings. In return, the partial coalescence occurrence and development are conditioned, in such systems, by the emulsion's intrinsic properties (e.g. solid fat content, fat crystal shape and size), formulation (e.g. protein content, surfactants presence) and extrinsic factors (e.g. cooling rate, shearing). A set of methods is available for partial coalescence investigation and quantification. These methods are critically reviewed in this paper, balancing the weaknesses of the methods in terms of structure alteration (for turbidity, dye dilution, etc.) and assumptions made for mathematical models (for particle size determination) with their advantages (good repeatability, high sensitivity, etc.). With the methods proposed in literature, the partial coalescence investigations can be conducted quantitatively and/or qualitatively. Good correlation were observed between some of the quantitative methods such as dye dilution, calorimetry, fat particle size; while a poor correlation was found in the case of solvent extraction method with other quantitative methods. The most suitable way for partial coalescence quantification was implied to be the fat particle size method, which would give results with a high degree of confidence if used in combination with a microscopic technique for the confirmation of partial coalescence as the main destabilization mechanism. Copyright © 2015 Elsevier B.V. All rights reserved.

Review of the Dynamics of Coalescence and Demulsification by High-Voltage Pulsed Electric Fields

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Ye Peng

2016-01-01

Full Text Available The coalescence of droplets in oil can be implemented rapidly by high-voltage pulse electric field, which is an effective demulsification dehydration technological method. At present, it is widely believed that the main reason of pulse electric field promoting droplets coalescence is the dipole coalescence and oscillation coalescence in pulse electric field, and the optimal coalescence pulse electric field parameters exist. Around the above content, the dynamics of high-voltage pulse electric field promoting the coalescence of emulsified droplets is studied by researchers domestically and abroad. By review, the progress of high-voltage pulse electric field demulsification technology can get a better understanding, which has an effect of throwing a sprat to catch a whale on promoting the industrial application.

Shear-induced Bubble Coalescence in Rhyolitic Melts with Low Vesicularity

Science.gov (United States)

Okumura, S.; Nakamura, M.; Tsuchiyama, A.

2006-12-01

Development of bubble structure during magma ascent controls the dynamics of volcanic eruption, because the bubble structure influences the magma rheology and permeability, and hence magma degassing. In the flowing magmas, the bubble structure is expected to be changed by shear, as pointed out by some previous studies based on geological observations. However, the development of bubble structure has been experimentally studied only in the isostatic magmas. We have experimentally demonstrated for the first time, the shear-induced development of number density, size and shape of bubbles in a rhyolitic melt. The deformation experiments were performed by using an externally heated, piston-cylinder type apparatus with a rotational piston. At 975°C, natural obsidian (initial water content of 0.5 wt%) having cylindrical shape (ca. 4.7 mm in diameter and 5 mm in length) was vesiculated in the graphite container (ca. 5 and 9 mm in the inner and the outer diameters, respectively, and 5 mm in length), and the vesiculated samples were twisted at various rotational speeds up to 1 rpm. The number density, size and shape of bubbles in the quenched samples were then measured by using the X-ray computed tomography. The size distribution of bubbles shows that the number of larger bubbles increases with the rotational speed and at the outer zone of the samples at which the shear rate is high. In the high shear rate zone, the magnitude of bubble deformation is large. The 3D images of large bubbles clearly indicate that they were formed by coalescence. These results indicate that the degree of bubble coalescence is enhanced with the shear rate. The experimental results also demonstrated that the coalescence of bubbles occur even at low vesicularity (ca. 20 vol.%). Because the shear rate induced in this study (in the order of 0.01 1/s) seems to be produced for magmas ascending in a volcanic conduit, we propose the possibility that the vesiculated magmas undergo bubble coalescence at a

Computational approach for a pair of bubble coalescence process

International Nuclear Information System (INIS)

Nurul Hasan; Zalinawati binti Zakaria

2011-01-01

The coalescence of bubbles has great value in mineral recovery and oil industry. In this paper, two co-axial bubbles rising in a cylinder is modelled to study the coalescence of bubbles for four computational experimental test cases. The Reynolds' (Re) number is chosen in between 8.50 and 10, Bond number, Bo ∼4.25-50, Morton number, M 0.0125-14.7. The viscosity ratio (μ r ) and density ratio (ρ r ) of liquid to bubble are kept constant (100 and 850 respectively). It was found that the Bo number has significant effect on the coalescence process for constant Re, μ r and ρ r . The bubble-bubble distance over time was validated against published experimental data. The results show that VOF approach can be used to model these phenomena accurately. The surface tension was changed to alter the Bo and density of the fluids to alter the Re and M, keeping the μ r and ρ r the same. It was found that for lower Bo, the bubble coalesce is slower and the pocket at the lower part of the leading bubble is less concave (towards downward) which is supported by the experimental data.

Coalescent Simulation and Paleodistribution Modeling for Tabebuia rosealba Do Not Support South American Dry Forest Refugia Hypothesis.

Directory of Open Access Journals (Sweden)

Warita Alves de Melo

Full Text Available Studies based on contemporary plant occurrences and pollen fossil records have proposed that the current disjunct distribution of seasonally dry tropical forests (SDTFs across South America is the result of fragmentation of a formerly widespread and continuously distributed dry forest during the arid climatic conditions associated with the Last Glacial Maximum (LGM, which is known as the modern-day dry forest refugia hypothesis. We studied the demographic history of Tabebuia rosealba (Bignoniaceae to understand the disjunct geographic distribution of South American SDTFs based on statistical phylogeography and ecological niche modeling (ENM. We specifically tested the dry forest refugia hypothesis; i.e., if the multiple and isolated patches of SDTFs are current climatic relicts of a widespread and continuously distributed dry forest during the LGM. We sampled 235 individuals across 18 populations in Central Brazil and analyzed the polymorphisms at chloroplast (trnS-trnG, psbA-trnH and ycf6-trnC intergenic spacers and nuclear (ITS nrDNA genomes. We performed coalescence simulations of alternative hypotheses under demographic expectations from two a priori biogeographic hypotheses (1. the Pleistocene Arc hypothesis and, 2. a range shift to Amazon Basin and other two demographic expectances predicted by ENMs (3. expansion throughout the Neotropical South America, including Amazon Basin, and 4. retraction during the LGM. Phylogenetic analyses based on median-joining network showed haplotype sharing among populations with evidence of incomplete lineage sorting. Coalescent analyses showed smaller effective population sizes for T. roseoalba during the LGM compared to the present-day. Simulations and ENM also showed that its current spatial pattern of genetic diversity is most likely due to a scenario of range retraction during the LGM instead of the fragmentation from a once extensive and largely contiguous SDTF across South America, not supporting the

Coalescent Simulation and Paleodistribution Modeling for Tabebuia rosealba Do Not Support South American Dry Forest Refugia Hypothesis.

Science.gov (United States)

de Melo, Warita Alves; Lima-Ribeiro, Matheus S; Terribile, Levi Carina; Collevatti, Rosane G

2016-01-01

Studies based on contemporary plant occurrences and pollen fossil records have proposed that the current disjunct distribution of seasonally dry tropical forests (SDTFs) across South America is the result of fragmentation of a formerly widespread and continuously distributed dry forest during the arid climatic conditions associated with the Last Glacial Maximum (LGM), which is known as the modern-day dry forest refugia hypothesis. We studied the demographic history of Tabebuia rosealba (Bignoniaceae) to understand the disjunct geographic distribution of South American SDTFs based on statistical phylogeography and ecological niche modeling (ENM). We specifically tested the dry forest refugia hypothesis; i.e., if the multiple and isolated patches of SDTFs are current climatic relicts of a widespread and continuously distributed dry forest during the LGM. We sampled 235 individuals across 18 populations in Central Brazil and analyzed the polymorphisms at chloroplast (trnS-trnG, psbA-trnH and ycf6-trnC intergenic spacers) and nuclear (ITS nrDNA) genomes. We performed coalescence simulations of alternative hypotheses under demographic expectations from two a priori biogeographic hypotheses (1. the Pleistocene Arc hypothesis and, 2. a range shift to Amazon Basin) and other two demographic expectances predicted by ENMs (3. expansion throughout the Neotropical South America, including Amazon Basin, and 4. retraction during the LGM). Phylogenetic analyses based on median-joining network showed haplotype sharing among populations with evidence of incomplete lineage sorting. Coalescent analyses showed smaller effective population sizes for T. roseoalba during the LGM compared to the present-day. Simulations and ENM also showed that its current spatial pattern of genetic diversity is most likely due to a scenario of range retraction during the LGM instead of the fragmentation from a once extensive and largely contiguous SDTF across South America, not supporting the South

Observation of Coalescence Process of Silver Nanospheres During Shape Transformation to Nanoprisms

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Yu Pyng

2011-01-01

Full Text Available Abstract In this report, we observed the growth mechanism and the shape transformation from spherical nanoparticles (diameter ~6 nm to triangular nanoprisms (bisector length ~100 nm. We used a simple direct chemical reduction method and provided evidences for the growth of silver nanoprisms via a coalescence process. Unlike previous reports, our method does not rely upon light, heat, or strong oxidant for the shape transformation. This transformation could be launched by fine-tuning the pH value of the silver colloidal solution. Based on our extensive examination using transmission electron microscopy, we propose a non-point initiated growth mechanism, which is a combination of coalescence and dissolution–recrystallization process during the growth of silver nanoprisms.

A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model

Science.gov (United States)

Chen, Mingjie; Izady, Azizallah; Abdalla, Osman A.; Amerjeed, Mansoor

2018-02-01

Bayesian inference using Markov Chain Monte Carlo (MCMC) provides an explicit framework for stochastic calibration of hydrogeologic models accounting for uncertainties; however, the MCMC sampling entails a large number of model calls, and could easily become computationally unwieldy if the high-fidelity hydrogeologic model simulation is time consuming. This study proposes a surrogate-based Bayesian framework to address this notorious issue, and illustrates the methodology by inverse modeling a regional MODFLOW model. The high-fidelity groundwater model is approximated by a fast statistical model using Bagging Multivariate Adaptive Regression Spline (BMARS) algorithm, and hence the MCMC sampling can be efficiently performed. In this study, the MODFLOW model is developed to simulate the groundwater flow in an arid region of Oman consisting of mountain-coast aquifers, and used to run representative simulations to generate training dataset for BMARS model construction. A BMARS-based Sobol' method is also employed to efficiently calculate input parameter sensitivities, which are used to evaluate and rank their importance for the groundwater flow model system. According to sensitivity analysis, insensitive parameters are screened out of Bayesian inversion of the MODFLOW model, further saving computing efforts. The posterior probability distribution of input parameters is efficiently inferred from the prescribed prior distribution using observed head data, demonstrating that the presented BMARS-based Bayesian framework is an efficient tool to reduce parameter uncertainties of a groundwater system.

Dynamics of gas cell coalescence during baking expansion of leavened dough.

Science.gov (United States)

Miś, Antoni; Nawrocka, Agnieszka; Lamorski, Krzysztof; Dziki, Dariusz

2018-01-01

The investigation of the dynamics of gas cell coalescence, i.e. a phenomenon that deteriorates the homogeneity of the cellular structure of bread crumb, was carried out performing simultaneously measurements of the dough volume, pressure, and viscosity. It was demonstrated that, during the baking expansion of chemically leavened wheat flour dough, the maximum growth rate of the gas cell radius determined from the ratio of pressure exerted by the expanded dough to its viscosity was on average four-fold lower than that calculated from volume changes in the gas phase of the dough. Such a high discrepancy was interpreted as a result of the course of coalescence, and a formula for determination of its rate was developed. The coalescence rate in the initial baking expansion phase had negative values, indicating nucleation of newly formed gas cells, which increased the number of gas cells even by 8%. In the next baking expansion phase, the coalescence rate started to exhibit positive values, reflecting dominance of the coalescence phenomenon over nucleation. The maximum coalescence rates indicate that, during the period of the most intensive dough expansion, the number of gas cells decreased by 2-3% within one second. At the end of the formation of bread crumb, the number of the gas cells declined by 55-67% in comparison with the initial value. The correctness of the results was positively verified using X-ray micro-computed tomography. The developed method can be a useful tool for more profound exploration of the coalescence phenomenon at various stages of evolution of the cellular structure and its determinants, which may contribute to future development of more effective methods for improving the texture and sensory quality of bread crumb. Copyright © 2017 Elsevier Ltd. All rights reserved.

FUZZY CLUSTERING BASED BAYESIAN FRAMEWORK TO PREDICT MENTAL HEALTH PROBLEMS AMONG CHILDREN

Directory of Open Access Journals (Sweden)

M R Sumathi

2017-04-01

Full Text Available According to World Health Organization, 10-20% of children and adolescents all over the world are experiencing mental disorders. Correct diagnosis of mental disorders at an early stage improves the quality of life of children and avoids complicated problems. Various expert systems using artificial intelligence techniques have been developed for diagnosing mental disorders like Schizophrenia, Depression, Dementia, etc. This study focuses on predicting basic mental health problems of children, like Attention problem, Anxiety problem, Developmental delay, Attention Deficit Hyperactivity Disorder (ADHD, Pervasive Developmental Disorder(PDD, etc. using the machine learning techniques, Bayesian Networks and Fuzzy clustering. The focus of the article is on learning the Bayesian network structure using a novel Fuzzy Clustering Based Bayesian network structure learning framework. The performance of the proposed framework was compared with the other existing algorithms and the experimental results have shown that the proposed framework performs better than the earlier algorithms.

Basics of Bayesian methods.

Science.gov (United States)

Ghosh, Sujit K

2010-01-01

Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.

A Bayesian decision approach to rainfall thresholds based flood warning

Directory of Open Access Journals (Sweden)

M. L. V. Martina

2006-01-01

Full Text Available Operational real time flood forecasting systems generally require a hydrological model to run in real time as well as a series of hydro-informatics tools to transform the flood forecast into relatively simple and clear messages to the decision makers involved in flood defense. The scope of this paper is to set forth the possibility of providing flood warnings at given river sections based on the direct comparison of the quantitative precipitation forecast with critical rainfall threshold values, without the need of an on-line real time forecasting system. This approach leads to an extremely simplified alert system to be used by non technical stakeholders and could also be used to supplement the traditional flood forecasting systems in case of system failures. The critical rainfall threshold values, incorporating the soil moisture initial conditions, result from statistical analyses using long hydrological time series combined with a Bayesian utility function minimization. In the paper, results of an application of the proposed methodology to the Sieve river, a tributary of the Arno river in Italy, are given to exemplify its practical applicability.

Review of Reliability-Based Design Optimization Approach and Its Integration with Bayesian Method

Science.gov (United States)

Zhang, Xiangnan

2018-03-01

A lot of uncertain factors lie in practical engineering, such as external load environment, material property, geometrical shape, initial condition, boundary condition, etc. Reliability method measures the structural safety condition and determine the optimal design parameter combination based on the probabilistic theory. Reliability-based design optimization (RBDO) is the most commonly used approach to minimize the structural cost or other performance under uncertainty variables which combines the reliability theory and optimization. However, it cannot handle the various incomplete information. The Bayesian approach is utilized to incorporate this kind of incomplete information in its uncertainty quantification. In this paper, the RBDO approach and its integration with Bayesian method are introduced.

Experimental investigation of void coalescence in a dual phase steel using X-ray tomography

International Nuclear Information System (INIS)

Landron, C.; Bouaziz, O.; Maire, E.; Adrien, J.

2013-01-01

In situ tensile tests were carried out during X-ray microtomography imaging of a smooth and a notched specimen of dual phase steel. The void coalescence was first qualitatively observed and quantitative data concerning this damage step was then acquired. The void coalescence criteria of Brown and Embury and of Thomason were then tested against the experimental data at both the macroscopic and local levels. Although macroscopic implementation of the criteria gave acceptable results, the local approach was probably closest to the real nature of void coalescence, because it takes into account local coalescence events observed experimentally before final fracture. The correlation between actual coalescing couples of cavities and local implementation of the two criteria showed that the Thomason criterion is probably the best adapted to predict the local coalescence events in the case of the material studied

Toward a DNA taxonomy of Alpine Rhithrogena (Ephemeroptera: Heptageniidae using a mixed Yule-coalescent analysis of mitochondrial and nuclear DNA.

Directory of Open Access Journals (Sweden)

Laurent Vuataz

Full Text Available Aquatic larvae of many Rhithrogena mayflies (Ephemeroptera inhabit sensitive Alpine environments. A number of species are on the IUCN Red List and many recognized species have restricted distributions and are of conservation interest. Despite their ecological and conservation importance, ambiguous morphological differences among closely related species suggest that the current taxonomy may not accurately reflect the evolutionary diversity of the group. Here we examined the species status of nearly 50% of European Rhithrogena diversity using a widespread sampling scheme of Alpine species that included 22 type localities, general mixed Yule-coalescent (GMYC model analysis of one standard mtDNA marker and one newly developed nDNA marker, and morphological identification where possible. Using sequences from 533 individuals from 144 sampling localities, we observed significant clustering of the mitochondrial (cox1 marker into 31 GMYC species. Twenty-one of these could be identified based on the presence of topotypes (expertly identified specimens from the species' type locality or unambiguous morphology. These results strongly suggest the presence of both cryptic diversity and taxonomic oversplitting in Rhithrogena. Significant clustering was not detected with protein-coding nuclear PEPCK, although nine GMYC species were congruent with well supported terminal clusters of nDNA. Lack of greater congruence in the two data sets may be the result of incomplete sorting of ancestral polymorphism. Bayesian phylogenetic analyses of both gene regions recovered four of the six recognized Rhithrogena species groups in our samples as monophyletic. Future development of more nuclear markers would facilitate multi-locus analysis of unresolved, closely related species pairs. The DNA taxonomy developed here lays the groundwork for a future revision of the important but cryptic Rhithrogena genus in Europe.

Bayesian Recovery of Clipped OFDM Signals: A Receiver-based Approach

KAUST Repository

Al-Rabah, Abdullatif R.

2013-05-01

Recently, orthogonal frequency-division multiplexing (OFDM) has been adopted for high-speed wireless communications due to its robustness against multipath fading. However, one of the main fundamental drawbacks of OFDM systems is the high peak-to-average-power ratio (PAPR). Several techniques have been proposed for PAPR reduction. Most of these techniques require transmitter-based (pre-compensated) processing. On the other hand, receiver-based alternatives would save the power and reduce the transmitter complexity. By keeping this in mind, a possible approach is to limit the amplitude of the OFDM signal to a predetermined threshold and equivalently a sparse clipping signal is added. Then, estimating this clipping signal at the receiver to recover the original signal. In this work, we propose a Bayesian receiver-based low-complexity clipping signal recovery method for PAPR reduction. The method is able to i) effectively reduce the PAPR via simple clipping scheme at the transmitter side, ii) use Bayesian recovery algorithm to reconstruct the clipping signal at the receiver side by measuring part of subcarriers, iii) perform well in the absence of statistical information about the signal (e.g. clipping level) and the noise (e.g. noise variance), and at the same time iv is energy efficient due to its low complexity. Specifically, the proposed recovery technique is implemented in data-aided based. The data-aided method collects clipping information by measuring reliable 
data subcarriers, thus makes full use of spectrum for data transmission without the need for tone reservation. The study is extended further to discuss how to improve the recovery of the clipping signal utilizing some features of practical OFDM systems i.e., the oversampling and the presence of multiple receivers. Simulation results demonstrate the superiority of the proposed technique over other recovery algorithms. The overall objective is to show that the receiver-based Bayesian technique is highly

Micromechanics of transformation-induced plasticity and variant coalescence

International Nuclear Information System (INIS)

Marketz, F.; Fischer, F.D.; University for Mining and Metallurgy, Leoben; Tanaka, K.

1996-01-01

Quantitative micromechanics descriptions of both transformation-induced plasticity (TRIP) associated with the martensitic transformation in an Fe-Ni alloy and of variant coalescence in a Cu-Al-Ni shape memory alloy are presented. The macroscopic deformation behavior of a polycrystalline aggregate as a result of the rearrangements within the crystallites is modelled with the help of a finite element based periodic microfield approach. In the case of TRIP the parent→martensite transformation is described by microscale thermodynamic and kinetic equations taking into account internal stress states. The simulation of a classical experiment on TRIP allows to quantify the Magee-effect and the Greenwood-Johnson effect. Furthermore, the development of the martensitic microstructure is studied with respect to the stress-assisted transformation of preferred variants. In the case of variant coalescence the strain energy due to internal stress states has an important influence on the mechanical behavior. Formulating the reorientation process on the size scale of self-accommodating plate groups in terms of the mobility of the boundaries between martensitic variants the macroscopic behavior in uniaxial tension is predicted by an incremental modelling procedure. Furthermore, influence of energy dissipation on the overall behavior is quantified. (orig.)

Multispecies coalescent analysis of the early diversification of neotropical primates: phylogenetic inference under strong gene trees/species tree conflict.

Science.gov (United States)

Schrago, Carlos G; Menezes, Albert N; Furtado, Carolina; Bonvicino, Cibele R; Seuanez, Hector N

2014-11-05

Neotropical primates (NP) are presently distributed in the New World from Mexico to northern Argentina, comprising three large families, Cebidae, Atelidae, and Pitheciidae, consequently to their diversification following their separation from Old World anthropoids near the Eocene/Oligocene boundary, some 40 Ma. The evolution of NP has been intensively investigated in the last decade by studies focusing on their phylogeny and timescale. However, despite major efforts, the phylogenetic relationship between these three major clades and the age of their last common ancestor are still controversial because these inferences were based on limited numbers of loci and dating analyses that did not consider the evolutionary variation associated with the distribution of gene trees within the proposed phylogenies. We show, by multispecies coalescent analyses of selected genome segments, spanning along 92,496,904 bp that the early diversification of extant NP was marked by a 2-fold increase of their effective population size and that Atelids and Cebids are more closely related respective to Pitheciids. The molecular phylogeny of NP has been difficult to solve because of population-level phenomena at the early evolution of the lineage. The association of evolutionary variation with the distribution of gene trees within proposed phylogenies is crucial for distinguishing the mean genetic divergence between species (the mean coalescent time between loci) from speciation time. This approach, based on extensive genomic data provided by new generation DNA sequencing, provides more accurate reconstructions of phylogenies and timescales for all organisms. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Bayesian-network-based safety risk analysis in construction projects

International Nuclear Information System (INIS)

Zhang, Limao; Wu, Xianguo; Skibniewski, Miroslaw J.; Zhong, Jingbing; Lu, Yujie

2014-01-01

This paper presents a systemic decision support approach for safety risk analysis under uncertainty in tunnel construction. Fuzzy Bayesian Networks (FBN) is used to investigate causal relationships between tunnel-induced damage and its influential variables based upon the risk/hazard mechanism analysis. Aiming to overcome limitations on the current probability estimation, an expert confidence indicator is proposed to ensure the reliability of the surveyed data for fuzzy probability assessment of basic risk factors. A detailed fuzzy-based inference procedure is developed, which has a capacity of implementing deductive reasoning, sensitivity analysis and abductive reasoning. The “3σ criterion” is adopted to calculate the characteristic values of a triangular fuzzy number in the probability fuzzification process, and the α-weighted valuation method is adopted for defuzzification. The construction safety analysis progress is extended to the entire life cycle of risk-prone events, including the pre-accident, during-construction continuous and post-accident control. A typical hazard concerning the tunnel leakage in the construction of Wuhan Yangtze Metro Tunnel in China is presented as a case study, in order to verify the applicability of the proposed approach. The results demonstrate the feasibility of the proposed approach and its application potential. A comparison of advantages and disadvantages between FBN and fuzzy fault tree analysis (FFTA) as risk analysis tools is also conducted. The proposed approach can be used to provide guidelines for safety analysis and management in construction projects, and thus increase the likelihood of a successful project in a complex environment. - Highlights: • A systemic Bayesian network based approach for safety risk analysis is developed. • An expert confidence indicator for probability fuzzification is proposed. • Safety risk analysis progress is extended to entire life cycle of risk-prone events. • A typical

Kernel Bayesian ART and ARTMAP.

Science.gov (United States)

Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan

2018-02-01

Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Coalescence aspects of cobalt nanoparticles during in situ high-temperature annealing

NARCIS (Netherlands)

Palasantzas, G; Vystavel, T; Koch, SA; De Hosson, JTM

2006-01-01

In this work we investigate the coalescence aspects of Co nanoparticles. It was observed that nanoparticles in contact with the substrate are relatively immobile, whereas those on top of other Co particles can rearrange themselves during high-temperature annealing and further coalesce. Indeed,

Etude expérimentale des phénomènes de coalescence dans les systèmes bulles-gouttes Experimental Study of Coalescence Phenomena in Bubble-Drop Systems

Directory of Open Access Journals (Sweden)

Roques H.

2006-11-01

Full Text Available A l'occasion d'une étude sur les séparations eau-hydrocarbures par flottation, nous avons été amenés à étudier expérimentalement les coalescences bulle-goutte, goutte-goutte et bulle-bulle dans le système triphasique eau-air-kérosène. Les 4 montages expérimentaux décrits nous ont permis d'étudier les aspects statiques (structure du complexe bulle-goutte qui se forme et dynamiques (temps de coalescence moyens bulle-goutte, goutte-goutte et bulle-bulle et d'étudier l'influence de composés transférables d'une phase à l'autre sur ces temps de coalescence moyens. Du point de vue statique, la configuration stable du complexe bulle d'air-goutte de kérosène correspond à la formation à l'interface eau-air d'un film d'hydrocarbure qui entoure la bulle d'air, Par contre, la fixation d'une bulle d'air à la périphérie d'une goutte de kérosène, selon la disposition classique dans la flottation des solides, ne s'observe ici que rarement et toujours de façon transitoire. D'un point de vue cinétique on observe que : - les coalescences bulle-bulle ou goutte-goutte sont toujours favorisées (les temps de coalescence moyens diminuent lorsqu'on introduit dans la phase gaz ou dans l'une des phases liquides un composé transférable dans l'autre phase liquide; - les coalescences bulle-goutte sont favorisées par la présence dans la phase gaz d'un composé transférable dans la phase continue aqueuse ou par la présence dans la phase dispersée liquide d'un composé transférable dans la phase continue aqueuse; - les coalescences bulle-goutte sont défavorisées par la présence dans la phase continue aqueuse d'un composé transférable sur les gouttes constituant la phase dispersée liquide. During a study of water-hydrocarbon separations by flotation, we were led to make an experimental examination of bubble-drop, drop-drop and bubble-bubble coalescences in a three-phase water-air-kerosene system. The four experimental arrangements

Learning Bayesian networks for discrete data

KAUST Repository

Liang, Faming

2009-02-01

Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

Multimode multidrop serial coalescence effects during condensation on hierarchical superhydrophobic surfaces.

Science.gov (United States)

Rykaczewski, Konrad; Paxson, Adam T; Anand, Sushant; Chen, Xuemei; Wang, Zuankai; Varanasi, Kripa K

2013-01-22

The prospect of enhancing the condensation rate by decreasing the maximum drop departure diameter significantly below the capillary length through spontaneous drop motion has generated significant interest in condensation on superhydrophobic surfaces (SHS). The mobile coalescence leading to spontaneous drop motion was initially reported to occur only on hierarchical SHS, consisting of both nanoscale and microscale topological features. However, subsequent studies have shown that mobile coalescence also occurs on solely nanostructured SHS. Thus, recent focus has been on understanding the condensation process on nanostructured surfaces rather than on hierarchical SHS. In this work, we investigate the impact of microscale topography of hierarchical SHS on the droplet coalescence dynamics and wetting states during the condensation process. We show that isolated mobile and immobile coalescence between two drops, almost exclusively focused on in previous studies, are rare. We identify several new droplet shedding modes, which are aided by tangential propulsion of mobile drops. These droplet shedding modes comprise of multiple droplets merging during serial coalescence events, which culminate in formation of a drop that either departs or remains anchored to the surface. We directly relate postmerging drop adhesion to formation of drops in nanoscale as well as microscale Wenzel and Cassie-Baxter wetting states. We identify the optimal microscale feature spacing of the hierarchical SHS, which promotes departure of the highest number of microdroplets. This optimal surface architecture consists of microscale features spaced close enough to enable transition of larger droplets into micro-Cassie state yet, at the same time, provides sufficient spacing in-between the features for occurrence of mobile coalescence.

Partial coalescence as a tool to control sensory perception of emulsions

NARCIS (Netherlands)

Benjamins, J.; Vingerhoeds, M.H.; Zoet, F.D.; Hoog, de E.H.A.; Aken, van G.A.

2009-01-01

This study evaluates the role of partial coalescence of whey protein-stabilized emulsions on sensory perception. The selection of fats was restricted to vegetable fats that are essentially melted at oral temperatures. The sensitivity to partial coalescence was controlled by a variation in the fat

Nonlinear and non-Gaussian Bayesian based handwriting beautification

Science.gov (United States)

Shi, Cao; Xiao, Jianguo; Xu, Canhui; Jia, Wenhua

2013-03-01

A framework is proposed in this paper to effectively and efficiently beautify handwriting by means of a novel nonlinear and non-Gaussian Bayesian algorithm. In the proposed framework, format and size of handwriting image are firstly normalized, and then typeface in computer system is applied to optimize vision effect of handwriting. The Bayesian statistics is exploited to characterize the handwriting beautification process as a Bayesian dynamic model. The model parameters to translate, rotate and scale typeface in computer system are controlled by state equation, and the matching optimization between handwriting and transformed typeface is employed by measurement equation. Finally, the new typeface, which is transformed from the original one and gains the best nonlinear and non-Gaussian optimization, is the beautification result of handwriting. Experimental results demonstrate the proposed framework provides a creative handwriting beautification methodology to improve visual acceptance.

Conjunction analysis and propositional logic in fMRI data analysis using Bayesian statistics.

Science.gov (United States)

Rudert, Thomas; Lohmann, Gabriele

2008-12-01

To evaluate logical expressions over different effects in data analyses using the general linear model (GLM) and to evaluate logical expressions over different posterior probability maps (PPMs). In functional magnetic resonance imaging (fMRI) data analysis, the GLM was applied to estimate unknown regression parameters. Based on the GLM, Bayesian statistics can be used to determine the probability of conjunction, disjunction, implication, or any other arbitrary logical expression over different effects or contrast. For second-level inferences, PPMs from individual sessions or subjects are utilized. These PPMs can be combined to a logical expression and its probability can be computed. The methods proposed in this article are applied to data from a STROOP experiment and the methods are compared to conjunction analysis approaches for test-statistics. The combination of Bayesian statistics with propositional logic provides a new approach for data analyses in fMRI. Two different methods are introduced for propositional logic: the first for analyses using the GLM and the second for common inferences about different probability maps. The methods introduced extend the idea of conjunction analysis to a full propositional logic and adapt it from test-statistics to Bayesian statistics. The new approaches allow inferences that are not possible with known standard methods in fMRI. (c) 2008 Wiley-Liss, Inc.

A Bayesian network model for predicting type 2 diabetes risk based on electronic health records

Science.gov (United States)

Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen

2017-07-01

An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.

Copula Based Factorization in Bayesian Multivariate Infinite Mixture Models

OpenAIRE

Martin Burda; Artem Prokhorov

2012-01-01

Bayesian nonparametric models based on infinite mixtures of density kernels have been recently gaining in popularity due to their flexibility and feasibility of implementation even in complicated modeling scenarios. In economics, they have been particularly useful in estimating nonparametric distributions of latent variables. However, these models have been rarely applied in more than one dimension. Indeed, the multivariate case suffers from the curse of dimensionality, with a rapidly increas...

On hydrogen-induced plastic flow localization during void growth and coalescence

Energy Technology Data Exchange (ETDEWEB)

Ahn, D.C.; Sofronis, P. [Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, 1206 West Green Street, Urbana, IL 61801 (United States); Dodds, R.H. Jr. [Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, 205 North Mathews Avenue, Urbana, IL 61801 (United States)

2007-11-15

Hydrogen-enhanced localized plasticity (HELP) is recognized as a viable mechanism of hydrogen embrittlement. A possible way by which the HELP mechanism can bring about macroscopic material failure is through hydrogen-induced accelerated void growth and coalescence. Assuming a periodic array of spherical voids loaded axisymmetrically, we investigate the hydrogen effect on the occurrence of plastic flow localization upon void growth and its dependence on macroscopic stress triaxiality. Under a macroscopic stress triaxiality equal to 1 and prior to void coalescence, the finite element calculation results obtained with material data relevant to A533B steel indicate that a hydrogen-induced localized shear band forms at an angle of about 45 {sup circle} from the axis of symmetry. At triaxiality equal to 3, void coalescence takes place by accelerated hydrogen-induced localization of plasticity mainly in the ligament between the voids. Lastly, we discuss the numerical results within the context of experimental observations on void growth and coalescence in the presence of hydrogen. (author)

Application of parametric equations of motion to study the resonance coalescence in H2(+).

Science.gov (United States)

Kalita, Dhruba J; Gupta, Ashish K

2012-12-07

Recently, occurrence of coalescence point was reported in H(2)(+) undergoing multiphoton dissociation in strong laser field. We have applied parametric equations of motion and smooth exterior scaling method to study the coalescence phenomenon of H(2)(+). The advantage of this method is that one can easily trace the different states that are changing as the field parameters change. It was reported earlier that in the parameter space, only two bound states coalesce [R. Lefebvre, O. Atabek, M. Sindelka, and N. Moiseyev, Phys. Rev. Lett. 103, 123003 (2009)]. However, it is found that increasing the accuracy of the calculation leads to the coalescence between resonance states originating from the bound and the continuum states. We have also reported many other coalescence points.

Bayesian analyses of genetic parameters for growth traits in Nellore cattle raised on pasture.

Science.gov (United States)

Lopes, F B; Ferreira, J L; Lobo, R B; Rosa, G J M

2017-07-06

This study was carried out to investigate (co)variance components and genetic parameters for growth traits in beef cattle using a multi-trait model by Bayesian methods. Genetic and residual (co)variances and parameters were estimated for weights at standard ages of 120 (W120), 210 (W210), 365 (W365), and 450 days (W450), and for pre- and post-weaning daily weight gain (preWWG and postWWG) in Nellore cattle. Data were collected over 16 years (1993-2009), and all animals were raised on pasture in eight farms in the North of Brazil that participate in the National Association of Breeders and Researchers. Analyses were run by the Bayesian approach using Gibbs sampler. Additive direct heritabilities for W120, W210, W365, and W450 and for preWWG and postWWG were 0.28 ± 0.013, 0.32 ± 0.002, 0.31 ± 0.002, 0.50 ± 0.026, 0.61 ± 0.047, and 0.79 ± 0.055, respectively. The estimates of maternal heritability were 0.32 ± 0.012, 0.29 ± 0.004, 0.30 ± 0.005, 0.25 ± 0.015, 0.23 ± 0.017, and 0.22 ± 0.016, respectively, for W120, W210, W365, and W450 and for preWWG and postWWG. The estimates of genetic direct additive correlation among all traits were positive and ranged from 0.25 ± 0.03 (preWWG and postWWG) to 0.99 ± 0.00 (W210 and preWWG). The moderate to high estimates of heritability and genetic correlation for weights and daily weight gains at different ages is suggestive of genetic improvement in these traits by selection at an appropriate age. Maternal genetic effects seemed to be significant across the traits. When the focus is on direct and maternal effects, W210 seems to be a good criterium for the selection of Nellore cattle considering the importance of this breed as a major breed of beef cattle not only in Northern Brazil but all regions covered by tropical pastures. As in this study the genetic correlations among all traits were high, the selection based on weaning weight might be a good choice because at this age there are two important effects (maternal

Flow-induced coalescence in polydisperse systems

Czech Academy of Sciences Publication Activity Database

Fortelný, Ivan; Jůza, Josef

2014-01-01

Roč. 299, č. 10 (2014), s. 1213-1219 ISSN 1438-7492 R&D Projects: GA ČR GAP106/11/1069 Institutional support: RVO:61389013 Keywords : coalescence * polymer blends * polydisperse droplets Subject RIV: BK - Fluid Dynamics Impact factor: 2.661, year: 2014

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

DEFF Research Database (Denmark)

Tully, Philip J; Lindén, Henrik; Hennig, Matthias H

2016-01-01

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed...... in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods...

Formation of hollow nanoshells in solution-based reactions via collision coalescence of nanobubble-particle systems

Science.gov (United States)

Vongehr, Sascha; Tang, Shaochun

2016-06-01

Research on hollow nanoshells has, for years, claimed to involve free, pre-existing nanobubbles as soft templates. It is a challenge to demonstrate this due to the difficulty of in situ observation during solution-based reactions. We show that no available free-bubble theory can describe the mysterious behavior of the bubble number density n. A new mechanism of collision coalescence of bubble-particle systems is suggested to form hollow nanoshells. By approximating relative velocity as ˜R -z (R is bubble radius), numerical simulations can reproduce the counterintuitive observations in the regime 1 synthesis of grain-monolayer thin, fractal-like incomplete, multi-metallic nanoshells with superior catalytic activity. The behaviors of n, R, and shell thickness h are closely reproduced by z = 1.6.

Is the extremely rare Iberian endemic plant species Castrilanthemum debeauxii (Compositae, Anthemideae) a 'living fossil'? Evidence from a multi-locus species tree reconstruction.

Science.gov (United States)

Tomasello, Salvatore; Álvarez, Inés; Vargas, Pablo; Oberprieler, Christoph

2015-01-01

The present study provides results of multi-species coalescent species tree analyses of DNA sequences sampled from multiple nuclear and plastid regions to infer the phylogenetic relationships among the members of the subtribe Leucanthemopsidinae (Compositae, Anthemideae), to which besides the annual Castrilanthemum debeauxii (Degen, Hervier & É.Rev.) Vogt & Oberp., one of the rarest flowering plant species of the Iberian Peninsula, two other unispecific genera (Hymenostemma, Prolongoa), and the polyploidy complex of the genus Leucanthemopsis belong. Based on sequence information from two single- to low-copy nuclear regions (C16, D35, characterised by Chapman et al. (2007)), the multi-copy region of the nrDNA internal transcribed spacer regions ITS1 and ITS2, and two intergenic spacer regions of the cpDNA gene trees were reconstructed using Bayesian inference methods. For the reconstruction of a multi-locus species tree we applied three different methods: (a) analysis of concatenated sequences using Bayesian inference (MrBayes), (b) a tree reconciliation approach by minimizing the number of deep coalescences (PhyloNet), and (c) a coalescent-based species-tree method in a Bayesian framework ((∗)BEAST). All three species tree reconstruction methods unequivocally support the close relationship of the subtribe with the hitherto unclassified genus Phalacrocarpum, the sister-group relationship of Castrilanthemum with the three remaining genera of the subtribe, and the further sister-group relationship of the clade of Hymenostemma+Prolongoa with a monophyletic genus Leucanthemopsis. Dating of the (∗)BEAST phylogeny supports the long-lasting (Early Miocene, 15-22Ma) taxonomical independence and the switch from the plesiomorphic perennial to the apomorphic annual life-form assumed for the Castrilanthemum lineage that may have occurred not earlier than in the Pliocene (3Ma) when the establishment of a Mediterranean climate with summer droughts triggered evolution towards

Bayesian models for comparative analysis integrating phylogenetic uncertainty

Directory of Open Access Journals (Sweden)

Villemereuil Pierre de

2012-06-01

Full Text Available Abstract Background Uncertainty in comparative analyses can come from at least two sources: a phylogenetic uncertainty in the tree topology or branch lengths, and b uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow and inflated significance in hypothesis testing (e.g. p-values will be too small. Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. Methods We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. Results We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Conclusions Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible

Bayesian models for comparative analysis integrating phylogenetic uncertainty

Science.gov (United States)

2012-01-01

Background Uncertainty in comparative analyses can come from at least two sources: a) phylogenetic uncertainty in the tree topology or branch lengths, and b) uncertainty due to intraspecific variation in trait values, either due to measurement error or natural individual variation. Most phylogenetic comparative methods do not account for such uncertainties. Not accounting for these sources of uncertainty leads to false perceptions of precision (confidence intervals will be too narrow) and inflated significance in hypothesis testing (e.g. p-values will be too small). Although there is some application-specific software for fitting Bayesian models accounting for phylogenetic error, more general and flexible software is desirable. Methods We developed models to directly incorporate phylogenetic uncertainty into a range of analyses that biologists commonly perform, using a Bayesian framework and Markov Chain Monte Carlo analyses. Results We demonstrate applications in linear regression, quantification of phylogenetic signal, and measurement error models. Phylogenetic uncertainty was incorporated by applying a prior distribution for the phylogeny, where this distribution consisted of the posterior tree sets from Bayesian phylogenetic tree estimation programs. The models were analysed using simulated data sets, and applied to a real data set on plant traits, from rainforest plant species in Northern Australia. Analyses were performed using the free and open source software OpenBUGS and JAGS. Conclusions Incorporating phylogenetic uncertainty through an empirical prior distribution of trees leads to more precise estimation of regression model parameters than using a single consensus tree and enables a more realistic estimation of confidence intervals. In addition, models incorporating measurement errors and/or individual variation, in one or both variables, are easily formulated in the Bayesian framework. We show that BUGS is a useful, flexible general purpose tool for

Congruence between distribution modelling and phylogeographical analyses reveals Quaternary survival of a toadflax species (Linaria elegans) in oceanic climate areas of a mountain ring range.

Science.gov (United States)

Fernández-Mazuecos, Mario; Vargas, Pablo

2013-06-01

· The role of Quaternary climatic shifts in shaping the distribution of Linaria elegans, an Iberian annual plant, was investigated using species distribution modelling and molecular phylogeographical analyses. Three hypotheses are proposed to explain the Quaternary history of its mountain ring range. · The distribution of L. elegans was modelled using the maximum entropy method and projected to the last interglacial and to the last glacial maximum (LGM) using two different paleoclimatic models: the Community Climate System Model (CCSM) and the Model for Interdisciplinary Research on Climate (MIROC). Two nuclear and three plastid DNA regions were sequenced for 24 populations (119 individuals sampled). Bayesian phylogenetic, phylogeographical, dating and coalescent-based population genetic analyses were conducted. · Molecular analyses indicated the existence of northern and southern glacial refugia and supported two routes of post-glacial recolonization. These results were consistent with the LGM distribution as inferred under the CCSM paleoclimatic model (but not under the MIROC model). Isolation between two major refugia was dated back to the Riss or Mindel glaciations, > 100 kyr before present (bp). · The Atlantic distribution of inferred refugia suggests that the oceanic (buffered)-continental (harsh) gradient may have played a key and previously unrecognized role in determining Quaternary distribution shifts of Mediterranean plants. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

Bayesian Estimation of Small Effects in Exercise and Sports Science.

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Kerrie L Mengersen

Full Text Available The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL, and intermittent hypoxic exposure (IHE on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a 'magnitude-based inference' approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

Bayesian Estimation of Small Effects in Exercise and Sports Science.

Science.gov (United States)

Mengersen, Kerrie L; Drovandi, Christopher C; Robert, Christian P; Pyne, David B; Gore, Christopher J

2016-01-01

The aim of this paper is to provide a Bayesian formulation of the so-called magnitude-based inference approach to quantifying and interpreting effects, and in a case study example provide accurate probabilistic statements that correspond to the intended magnitude-based inferences. The model is described in the context of a published small-scale athlete study which employed a magnitude-based inference approach to compare the effect of two altitude training regimens (live high-train low (LHTL), and intermittent hypoxic exposure (IHE)) on running performance and blood measurements of elite triathletes. The posterior distributions, and corresponding point and interval estimates, for the parameters and associated effects and comparisons of interest, were estimated using Markov chain Monte Carlo simulations. The Bayesian analysis was shown to provide more direct probabilistic comparisons of treatments and able to identify small effects of interest. The approach avoided asymptotic assumptions and overcame issues such as multiple testing. Bayesian analysis of unscaled effects showed a probability of 0.96 that LHTL yields a substantially greater increase in hemoglobin mass than IHE, a 0.93 probability of a substantially greater improvement in running economy and a greater than 0.96 probability that both IHE and LHTL yield a substantially greater improvement in maximum blood lactate concentration compared to a Placebo. The conclusions are consistent with those obtained using a 'magnitude-based inference' approach that has been promoted in the field. The paper demonstrates that a fully Bayesian analysis is a simple and effective way of analysing small effects, providing a rich set of results that are straightforward to interpret in terms of probabilistic statements.

Bayesian Analysis of Individual Level Personality Dynamics

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Edward Cripps

2016-07-01

Full Text Available A Bayesian technique with analyses of within-person processes at the level of the individual is presented. The approach is used to examine if the patterns of within-person responses on a 12 trial simulation task are consistent with the predictions of ITA theory (Dweck, 1999. ITA theory states that the performance of an individual with an entity theory of ability is more likely to spiral down following a failure experience than the performance of an individual with an incremental theory of ability. This is because entity theorists interpret failure experiences as evidence of a lack of ability, which they believe is largely innate and therefore relatively fixed; whilst incremental theorists believe in the malleability of abilities and interpret failure experiences as evidence of more controllable factors such as poor strategy or lack of effort. The results of our analyses support ITA theory at both the within- and between-person levels of analyses and demonstrate the benefits of Bayesian techniques for the analysis of within-person processes. These include more formal specification of the theory and the ability to draw inferences about each individual, which allows for more nuanced interpretations of individuals within a personality category, such as differences in the individual probabilities of spiralling. While Bayesian techniques have many potential advantages for the analyses of within-person processes at the individual level, ease of use is not one of them for psychologists trained in traditional frequentist statistical techniques.

Partial coalescence from bubbles to drops

KAUST Repository

Zhang, F. H.; Thoraval, Marie-Jean; Thoroddsen, Sigurdur T; Taborek, P.

2015-01-01

the travel time of this wave mode on the bubble surface, we also show that the model is consistent with the experiments. This wavenumber is determined by both the global drainage as well as the interface shapes during the rapid coalescence in the neck

Probabilistic safety assessment model in consideration of human factors based on object-oriented bayesian networks

International Nuclear Information System (INIS)

Zhou Zhongbao; Zhou Jinglun; Sun Quan

2007-01-01

Effect of Human factors on system safety is increasingly serious, which is often ignored in traditional probabilistic safety assessment methods however. A new probabilistic safety assessment model based on object-oriented Bayesian networks is proposed in this paper. Human factors are integrated into the existed event sequence diagrams. Then the classes of the object-oriented Bayesian networks are constructed which are converted to latent Bayesian networks for inference. Finally, the inference results are integrated into event sequence diagrams for probabilistic safety assessment. The new method is applied to the accident of loss of coolant in a nuclear power plant. the results show that the model is not only applicable to real-time situation assessment, but also applicable to situation assessment based certain amount of information. The modeling complexity is kept down and the new method is appropriate to large complex systems due to the thoughts of object-oriented. (authors)

A parallel algorithm for filtering gravitational waves from coalescing binaries

International Nuclear Information System (INIS)

Sathyaprakash, B.S.; Dhurandhar, S.V.

1992-10-01

Coalescing binary stars are perhaps the most promising sources for the observation of gravitational waves with laser interferometric gravity wave detectors. The waveform from these sources can be predicted with sufficient accuracy for matched filtering techniques to be applied. In this paper we present a parallel algorithm for detecting signals from coalescing compact binaries by the method of matched filtering. We also report the details of its implementation on a 256-node connection machine consisting of a network of transputers. The results of our analysis indicate that parallel processing is a promising approach to on-line analysis of data from gravitational wave detectors to filter out coalescing binary signals. The algorithm described is quite general in that the kernel of the algorithm is applicable to any set of matched filters. (author). 15 refs, 4 figs

BAYESIAN FORECASTS COMBINATION TO IMPROVE THE ROMANIAN INFLATION PREDICTIONS BASED ON ECONOMETRIC MODELS

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Mihaela Simionescu

2014-12-01

Full Text Available There are many types of econometric models used in predicting the inflation rate, but in this study we used a Bayesian shrinkage combination approach. This methodology is used in order to improve the predictions accuracy by including information that is not captured by the econometric models. Therefore, experts’ forecasts are utilized as prior information, for Romania these predictions being provided by Institute for Economic Forecasting (Dobrescu macromodel, National Commission for Prognosis and European Commission. The empirical results for Romanian inflation show the superiority of a fixed effects model compared to other types of econometric models like VAR, Bayesian VAR, simultaneous equations model, dynamic model, log-linear model. The Bayesian combinations that used experts’ predictions as priors, when the shrinkage parameter tends to infinite, improved the accuracy of all forecasts based on individual models, outperforming also zero and equal weights predictions and naïve forecasts.

Technical Note: Approximate Bayesian parameterization of a process-based tropical forest model

Science.gov (United States)

Hartig, F.; Dislich, C.; Wiegand, T.; Huth, A.

2014-02-01

Inverse parameter estimation of process-based models is a long-standing problem in many scientific disciplines. A key question for inverse parameter estimation is how to define the metric that quantifies how well model predictions fit to the data. This metric can be expressed by general cost or objective functions, but statistical inversion methods require a particular metric, the probability of observing the data given the model parameters, known as the likelihood. For technical and computational reasons, likelihoods for process-based stochastic models are usually based on general assumptions about variability in the observed data, and not on the stochasticity generated by the model. Only in recent years have new methods become available that allow the generation of likelihoods directly from stochastic simulations. Previous applications of these approximate Bayesian methods have concentrated on relatively simple models. Here, we report on the application of a simulation-based likelihood approximation for FORMIND, a parameter-rich individual-based model of tropical forest dynamics. We show that approximate Bayesian inference, based on a parametric likelihood approximation placed in a conventional Markov chain Monte Carlo (MCMC) sampler, performs well in retrieving known parameter values from virtual inventory data generated by the forest model. We analyze the results of the parameter estimation, examine its sensitivity to the choice and aggregation of model outputs and observed data (summary statistics), and demonstrate the application of this method by fitting the FORMIND model to field data from an Ecuadorian tropical forest. Finally, we discuss how this approach differs from approximate Bayesian computation (ABC), another method commonly used to generate simulation-based likelihood approximations. Our results demonstrate that simulation-based inference, which offers considerable conceptual advantages over more traditional methods for inverse parameter estimation

CoaSim: A Flexible Environment for Simulating Genetic Data under Coalescent Models

DEFF Research Database (Denmark)

Mailund; Schierup, Mikkel Heide; Pedersen, Christian Nørgaard Storm

2005-01-01

get insight into these. Results We have created the CoaSim application as a flexible environment for Monte various types of genetic data under equilibrium and non-equilibrium coalescent variety of applications. Interaction with the tool is through the Guile version scripting language. Scheme scripts......Background Coalescent simulations are playing a large role in interpreting large scale intra- polymorphism surveys and for planning and evaluating association studies. Coalescent of data sets under different models can be compared to the actual data to test different evolutionary factors and thus...

Asymptotic distributions of coalescence times and ancestral lineage numbers for populations with temporally varying size.

Science.gov (United States)

Chen, Hua; Chen, Kun

2013-07-01

The distributions of coalescence times and ancestral lineage numbers play an essential role in coalescent modeling and ancestral inference. Both exact distributions of coalescence times and ancestral lineage numbers are expressed as the sum of alternating series, and the terms in the series become numerically intractable for large samples. More computationally attractive are their asymptotic distributions, which were derived in Griffiths (1984) for populations with constant size. In this article, we derive the asymptotic distributions of coalescence times and ancestral lineage numbers for populations with temporally varying size. For a sample of size n, denote by Tm the mth coalescent time, when m + 1 lineages coalesce into m lineages, and An(t) the number of ancestral lineages at time t back from the current generation. Similar to the results in Griffiths (1984), the number of ancestral lineages, An(t), and the coalescence times, Tm, are asymptotically normal, with the mean and variance of these distributions depending on the population size function, N(t). At the very early stage of the coalescent, when t → 0, the number of coalesced lineages n - An(t) follows a Poisson distribution, and as m → n, $$n\\left(n-1\\right){T}_{m}/2N\\left(0\\right)$$ follows a gamma distribution. We demonstrate the accuracy of the asymptotic approximations by comparing to both exact distributions and coalescent simulations. Several applications of the theoretical results are also shown: deriving statistics related to the properties of gene genealogies, such as the time to the most recent common ancestor (TMRCA) and the total branch length (TBL) of the genealogy, and deriving the allele frequency spectrum for large genealogies. With the advent of genomic-level sequencing data for large samples, the asymptotic distributions are expected to have wide applications in theoretical and methodological development for population genetic inference.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

DEFF Research Database (Denmark)

Sousa, Tiago; Pinto, Tiago; Praca, Isabel

2014-01-01

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi...

Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth.

Science.gov (United States)

Baker, Robert L; Leong, Wen Fung; An, Nan; Brock, Marcus T; Rubin, Matthew J; Welch, Stephen; Weinig, Cynthia

2018-02-01

We develop Bayesian function-valued trait models that mathematically isolate genetic mechanisms underlying leaf growth trajectories by factoring out genotype-specific differences in photosynthesis. Remote sensing data can be used instead of leaf-level physiological measurements. Characterizing the genetic basis of traits that vary during ontogeny and affect plant performance is a major goal in evolutionary biology and agronomy. Describing genetic programs that specifically regulate morphological traits can be complicated by genotypic differences in physiological traits. We describe the growth trajectories of leaves using novel Bayesian function-valued trait (FVT) modeling approaches in Brassica rapa recombinant inbred lines raised in heterogeneous field settings. While frequentist approaches estimate parameter values by treating each experimental replicate discretely, Bayesian models can utilize information in the global dataset, potentially leading to more robust trait estimation. We illustrate this principle by estimating growth asymptotes in the face of missing data and comparing heritabilities of growth trajectory parameters estimated by Bayesian and frequentist approaches. Using pseudo-Bayes factors, we compare the performance of an initial Bayesian logistic growth model and a model that incorporates carbon assimilation (A max ) as a cofactor, thus statistically accounting for genotypic differences in carbon resources. We further evaluate two remotely sensed spectroradiometric indices, photochemical reflectance (pri2) and MERIS Terrestrial Chlorophyll Index (mtci) as covariates in lieu of A max , because these two indices were genetically correlated with A max across years and treatments yet allow much higher throughput compared to direct leaf-level gas-exchange measurements. For leaf lengths in uncrowded settings, including A max improves model fit over the initial model. The mtci and pri2 indices also outperform direct A max measurements. Of particular

Modeling visual search using three-parameter probability functions in a hierarchical Bayesian framework.

Science.gov (United States)

Lin, Yi-Shin; Heinke, Dietmar; Humphreys, Glyn W

2015-04-01

In this study, we applied Bayesian-based distributional analyses to examine the shapes of response time (RT) distributions in three visual search paradigms, which varied in task difficulty. In further analyses we investigated two common observations in visual search-the effects of display size and of variations in search efficiency across different task conditions-following a design that had been used in previous studies (Palmer, Horowitz, Torralba, & Wolfe, Journal of Experimental Psychology: Human Perception and Performance, 37, 58-71, 2011; Wolfe, Palmer, & Horowitz, Vision Research, 50, 1304-1311, 2010) in which parameters of the response distributions were measured. Our study showed that the distributional parameters in an experimental condition can be reliably estimated by moderate sample sizes when Monte Carlo simulation techniques are applied. More importantly, by analyzing trial RTs, we were able to extract paradigm-dependent shape changes in the RT distributions that could be accounted for by using the EZ2 diffusion model. The study showed that Bayesian-based RT distribution analyses can provide an important means to investigate the underlying cognitive processes in search, including stimulus grouping and the bottom-up guidance of attention.

Influence of Energy and Temperature in Cluster Coalescence Induced by Deposition

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J. C. Jiménez-Sáez

2012-01-01

Full Text Available Coalescence induced by deposition of different Cu clusters on an epitaxial Co cluster supported on a Cu(001 substrate is studied by constant-temperature molecular dynamics simulations. The degree of epitaxy of the final system increases with increasing separation between the centres of mass of the projectile and target clusters during the collision. Structure, roughness, and epitaxial order of the supported cluster also influence the degree of epitaxy. The effect of energy and temperature is determinant on the epitaxial condition of the coalesced cluster, especially both factors modify the generation, growth and interaction among grains. A higher temperature favours the epitaxial growth for low impact parameters. A higher energy contributes to the epitaxial coalescence for any initial separation between the projectile and target clusters. The influence of projectile energy is notably greater than the influence of temperature since higher energies allow greater and instantaneous atomic reorganizations, so that the number of arisen grains just after the collision becomes smaller. The appearance of grain boundary dislocations is, therefore, a decisive factor in the epitaxial growth of the coalesced cluster.

Visualization by X-ray tomography of void growth and coalescence leading to fracture in model materials

International Nuclear Information System (INIS)

Weck, A.; Wilkinson, D.S.; Maire, E.; Toda, H.

2008-01-01

The literature contains many models for the process of void nucleation, growth and coalescence leading to ductile fracture. However, these models lack in-depth experimental validation, in part because void coalescence is difficult to capture experimentally. In this paper, an embedded array of holes is obtained by diffusion bonding a sheet filled with laser-drilled holes between two intact sheets. The experiments have been performed with both pure copper and Glidcop. Using X-ray computed tomography, we show that void growth and coalescence (or linkage) are well captured in both materials. The Brown and Embury model for void coalescence underestimates coalescence strains due to constraining effects. However, both the Rice and Tracey model for void growth and the Thomason model for void coalescence give good predictions for copper samples when stress triaxiality is considered. The Thomason model, however, fails to predict coalescence for the Glidcop samples; this is primarily due to secondary void nucleation

An introduction to using Bayesian linear regression with clinical data.

Science.gov (United States)

Baldwin, Scott A; Larson, Michael J

2017-11-01

Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bayesian networks improve causal environmental ...

Science.gov (United States)

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

Eyewitness identification: Bayesian information gain, base-rate effect equivalency curves, and reasonable suspicion.

Science.gov (United States)

Wells, Gary L; Yang, Yueran; Smalarz, Laura

2015-04-01

We provide a novel Bayesian treatment of the eyewitness identification problem as it relates to various system variables, such as instruction effects, lineup presentation format, lineup-filler similarity, lineup administrator influence, and show-ups versus lineups. We describe why eyewitness identification is a natural Bayesian problem and how numerous important observations require careful consideration of base rates. Moreover, we argue that the base rate in eyewitness identification should be construed as a system variable (under the control of the justice system). We then use prior-by-posterior curves and information-gain curves to examine data obtained from a large number of published experiments. Next, we show how information-gain curves are moderated by system variables and by witness confidence and we note how information-gain curves reveal that lineups are consistently more proficient at incriminating the guilty than they are at exonerating the innocent. We then introduce a new type of analysis that we developed called base rate effect-equivalency (BREE) curves. BREE curves display how much change in the base rate is required to match the impact of any given system variable. The results indicate that even relatively modest changes to the base rate can have more impact on the reliability of eyewitness identification evidence than do the traditional system variables that have received so much attention in the literature. We note how this Bayesian analysis of eyewitness identification has implications for the question of whether there ought to be a reasonable-suspicion criterion for placing a person into the jeopardy of an identification procedure. (c) 2015 APA, all rights reserved).

Application of Bayesian Decision Theory Based on Prior Information in the Multi-Objective Optimization Problem

Directory of Open Access Journals (Sweden)

Xia Lei

2010-12-01

Full Text Available General multi-objective optimization methods are hard to obtain prior information, how to utilize prior information has been a challenge. This paper analyzes the characteristics of Bayesian decision-making based on maximum entropy principle and prior information, especially in case that how to effectively improve decision-making reliability in deficiency of reference samples. The paper exhibits effectiveness of the proposed method using the real application of multi-frequency offset estimation in distributed multiple-input multiple-output system. The simulation results demonstrate Bayesian decision-making based on prior information has better global searching capability when sampling data is deficient.

Multi-scale simulation of droplet-droplet interactions and coalescence

CSIR Research Space (South Africa)

Musehane, Ndivhuwo M

2016-10-01

Full Text Available Conference on Computational and Applied Mechanics Potchefstroom 3–5 October 2016 Multi-scale simulation of droplet-droplet interactions and coalescence 1,2Ndivhuwo M. Musehane?, 1Oliver F. Oxtoby and 2Daya B. Reddy 1. Aeronautic Systems, Council... topology changes that result when droplets interact. This work endeavours to eliminate the need to use empirical correlations based on phenomenological models by developing a multi-scale model that predicts the outcome of a collision between droplets from...

Applying species-tree analyses to deep phylogenetic histories: challenges and potential suggested from a survey of empirical phylogenetic studies.

Science.gov (United States)

Lanier, Hayley C; Knowles, L Lacey

2015-02-01

difficulties with MCMC convergence and estimation of requisite population genetic parameters for coalescent-based approaches. Despite these difficulties, the findings generally support the utility of species-tree analyses for the estimation of species relationships throughout the ToL. We discuss strategies for successful application of species-tree approaches to deep phylogenies. Copyright © 2014 Elsevier Inc. All rights reserved.

Editorial: Bayesian benefits for child psychology and psychiatry researchers.

Science.gov (United States)

Oldehinkel, Albertine J

2016-09-01

For many scientists, performing statistical tests has become an almost automated routine. However, p-values are frequently used and interpreted incorrectly; and even when used appropriately, p-values tend to provide answers that do not match researchers' questions and hypotheses well. Bayesian statistics present an elegant and often more suitable alternative. The Bayesian approach has rarely been applied in child psychology and psychiatry research so far, but the development of user-friendly software packages and tutorials has placed it well within reach now. Because Bayesian analyses require a more refined definition of hypothesized probabilities of possible outcomes than the classical approach, going Bayesian may offer the additional benefit of sparkling the development and refinement of theoretical models in our field. © 2016 Association for Child and Adolescent Mental Health.

Image interpolation via graph-based Bayesian label propagation.

Science.gov (United States)

Xianming Liu; Debin Zhao; Jiantao Zhou; Wen Gao; Huifang Sun

2014-03-01

In this paper, we propose a novel image interpolation algorithm via graph-based Bayesian label propagation. The basic idea is to first create a graph with known and unknown pixels as vertices and with edge weights encoding the similarity between vertices, then the problem of interpolation converts to how to effectively propagate the label information from known points to unknown ones. This process can be posed as a Bayesian inference, in which we try to combine the principles of local adaptation and global consistency to obtain accurate and robust estimation. Specially, our algorithm first constructs a set of local interpolation models, which predict the intensity labels of all image samples, and a loss term will be minimized to keep the predicted labels of the available low-resolution (LR) samples sufficiently close to the original ones. Then, all of the losses evaluated in local neighborhoods are accumulated together to measure the global consistency on all samples. Moreover, a graph-Laplacian-based manifold regularization term is incorporated to penalize the global smoothness of intensity labels, such smoothing can alleviate the insufficient training of the local models and make them more robust. Finally, we construct a unified objective function to combine together the global loss of the locally linear regression, square error of prediction bias on the available LR samples, and the manifold regularization term. It can be solved with a closed-form solution as a convex optimization problem. Experimental results demonstrate that the proposed method achieves competitive performance with the state-of-the-art image interpolation algorithms.

Void migration, coalescence and swelling in fusion materials

International Nuclear Information System (INIS)

Cottrell, G.A.

2003-01-01

A recent analysis of the migration of voids and bubbles, produced in neutron irradiated fusion materials, is outlined. The migration, brought about by thermal hopping of atoms on the surface of a void, is normally a random Brownian motion but, in a temperature gradient, can be slightly biassed up the gradient. Two effects of such migrations are the transport of voids and trapped transmutation helium atoms to grain boundaries, where embrittlement may result; and the coalescence of migrating voids, which reduces the number of non-dislocation sites available for the capture of knock-on point defects and thereby enables the dislocation bias process to maintain void swelling. A selection of candidate fusion power plant armour and structural metals have been analysed. The metals most resistant to void migration and its effects are tungsten and molybdenum. Steel and beryllium are least so and vanadium is intermediate

Bayesian artificial intelligence

CERN Document Server

Korb, Kevin B

2010-01-01

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente

Conservatism of present plugging criteria on steam generator tubes and coalescence model of collinear through-wall axial cracks

International Nuclear Information System (INIS)

Lee, Jin Ho; Park, Youn Won; Song, Myung Ho; Kim, Young Jin; Moon, Seong In

1999-01-01

The steam generator tubing covers a major portion of the primary pressure-retaining boundary, so that very conservative approaches were taken in the light of steam generator tube integrity. According to the present criteria, tubes wall-thinned in excess of 40% should be plugged whatever the cause was. However, it is reported that there is no safety problem even with thickness reductions greater than 40%. Recently, the plant specific plugging criteria are introduced in many countries by demonstrating that the cracked tube has a sufficient safety margin. One of the drawbacks of such criteria, even though not yet codified, is that it is developed based on tubes with single cracks regardless of the fact that the appearance of multiple cracks is general. Their failure analyses have been, therefore, carried out using an idealized single crack to reduce complexity till now. The objective of this paper is to review the conservatism of the present plugging criteria of steam generator tubes and to propose a new coalescence criterion for twin through-wall cracks existing in steam generator tubes. Using the existing failure models and experimental results, we review the conservatism of the present plugging criteria. In order to verify the usefulness of the proposed new coalescence criterion, we perform finite element analysis

Bayesian Network Models in Cyber Security: A Systematic Review

OpenAIRE

Chockalingam, S.; Pieters, W.; Herdeiro Teixeira, A.M.; van Gelder, P.H.A.J.M.; Lipmaa, Helger; Mitrokotsa, Aikaterini; Matulevicius, Raimundas

2017-01-01

Bayesian Networks (BNs) are an increasingly popular modelling technique in cyber security especially due to their capability to overcome data limitations. This is also instantiated by the growth of BN models development in cyber security. However, a comprehensive comparison and analysis of these models is missing. In this paper, we conduct a systematic review of the scientific literature and identify 17 standard BN models in cyber security. We analyse these models based on 9 different criteri...

BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES

Energy Technology Data Exchange (ETDEWEB)

Iliadis, C.; Anderson, K. S. [Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255 (United States); Coc, A. [Centre de Sciences Nucléaires et de Sciences de la Matière (CSNSM), CNRS/IN2P3, Univ. Paris-Sud, Université Paris–Saclay, Bâtiment 104, F-91405 Orsay Campus (France); Timmes, F. X.; Starrfield, S., E-mail: iliadis@unc.edu [School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1504 (United States)

2016-11-01

The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.

Assessing species boundaries using multilocus species delimitation in a morphologically conserved group of neotropical freshwater fishes, the Poecilia sphenops species complex (Poeciliidae.

Directory of Open Access Journals (Sweden)

Justin C Bagley

Full Text Available Accurately delimiting species is fundamentally important for understanding species diversity and distributions and devising effective strategies to conserve biodiversity. However, species delimitation is problematic in many taxa, including 'non-adaptive radiations' containing morphologically cryptic lineages. Fortunately, coalescent-based species delimitation methods hold promise for objectively estimating species limits in such radiations, using multilocus genetic data. Using coalescent-based approaches, we delimit species and infer evolutionary relationships in a morphologically conserved group of Central American freshwater fishes, the Poecilia sphenops species complex. Phylogenetic analyses of multiple genetic markers (sequences of two mitochondrial DNA genes and five nuclear loci from 10/15 species and genetic lineages recognized in the group support the P. sphenops species complex as monophyletic with respect to outgroups, with eight mitochondrial 'major-lineages' diverged by ≥2% pairwise genetic distances. From general mixed Yule-coalescent models, we discovered (conservatively 10 species within our concatenated mitochondrial DNA dataset, 9 of which were strongly supported by subsequent multilocus Bayesian species delimitation and species tree analyses. Results suggested species-level diversity is underestimated or overestimated by at least ~15% in different lineages in the complex. Nonparametric statistics and coalescent simulations indicate genealogical discordance among our gene tree results has mainly derived from interspecific hybridization in the nuclear genome. However, mitochondrial DNA show little evidence for introgression, and our species delimitation results appear robust to effects of this process. Overall, our findings support the utility of combining multiple lines of genetic evidence and broad phylogeographical sampling to discover and validate species using coalescent-based methods. Our study also highlights the

Dynamic measurements and simulations of airborne picolitre-droplet coalescence in holographic optical tweezers

International Nuclear Information System (INIS)

Bzdek, Bryan R.; Reid, Jonathan P.; Collard, Liam; Sprittles, James E.; Hudson, Andrew J.

2016-01-01

We report studies of the coalescence of pairs of picolitre aerosol droplets manipulated with holographic optical tweezers, probing the shape relaxation dynamics following coalescence by simultaneously monitoring the intensity of elastic backscattered light (EBL) from the trapping laser beam (time resolution on the order of 100 ns) while recording high frame rate camera images (time resolution <10 μs). The goals of this work are to: resolve the dynamics of droplet coalescence in holographic optical traps; assign the origin of key features in the time-dependent EBL intensity; and validate the use of the EBL alone to precisely determine droplet surface tension and viscosity. For low viscosity droplets, two sequential processes are evident: binary coalescence first results from the overlap of the optical traps on the time scale of microseconds followed by the recapture of the composite droplet in an optical trap on the time scale of milliseconds. As droplet viscosity increases, the relaxation in droplet shape eventually occurs on the same time scale as recapture, resulting in a convoluted evolution of the EBL intensity that inhibits quantitative determination of the relaxation time scale. Droplet coalescence was simulated using a computational framework to validate both experimental approaches. The results indicate that time-dependent monitoring of droplet shape from the EBL intensity allows for robust determination of properties such as surface tension and viscosity. Finally, the potential of high frame rate imaging to examine the coalescence of dissimilar viscosity droplets is discussed.

Dynamic measurements and simulations of airborne picolitre-droplet coalescence in holographic optical tweezers

Energy Technology Data Exchange (ETDEWEB)

Bzdek, Bryan R.; Reid, Jonathan P., E-mail: j.p.reid@bristol.ac.uk [School of Chemistry, University of Bristol, Bristol BS8 1TS (United Kingdom); Collard, Liam [Department of Mathematics, University of Leicester, Leicester LE1 7RH (United Kingdom); Sprittles, James E. [Mathematics Institute, University of Warwick, Coventry CV4 7AL (United Kingdom); Hudson, Andrew J. [Department of Chemistry, University of Leicester, Leicester LE1 7RH (United Kingdom)

2016-08-07

We report studies of the coalescence of pairs of picolitre aerosol droplets manipulated with holographic optical tweezers, probing the shape relaxation dynamics following coalescence by simultaneously monitoring the intensity of elastic backscattered light (EBL) from the trapping laser beam (time resolution on the order of 100 ns) while recording high frame rate camera images (time resolution <10 μs). The goals of this work are to: resolve the dynamics of droplet coalescence in holographic optical traps; assign the origin of key features in the time-dependent EBL intensity; and validate the use of the EBL alone to precisely determine droplet surface tension and viscosity. For low viscosity droplets, two sequential processes are evident: binary coalescence first results from the overlap of the optical traps on the time scale of microseconds followed by the recapture of the composite droplet in an optical trap on the time scale of milliseconds. As droplet viscosity increases, the relaxation in droplet shape eventually occurs on the same time scale as recapture, resulting in a convoluted evolution of the EBL intensity that inhibits quantitative determination of the relaxation time scale. Droplet coalescence was simulated using a computational framework to validate both experimental approaches. The results indicate that time-dependent monitoring of droplet shape from the EBL intensity allows for robust determination of properties such as surface tension and viscosity. Finally, the potential of high frame rate imaging to examine the coalescence of dissimilar viscosity droplets is discussed.

Space Shuttle RTOS Bayesian Network

Science.gov (United States)

Morris, A. Terry; Beling, Peter A.

2001-01-01

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores

Inferring the dynamics of diversification: a coalescent approach.

Science.gov (United States)

Morlon, Hélène; Potts, Matthew D; Plotkin, Joshua B

2010-09-28

Recent analyses of the fossil record and molecular phylogenies suggest that there are fundamental limits to biodiversity, possibly arising from constraints in the availability of space, resources, or ecological niches. Under this hypothesis, speciation rates decay over time and biodiversity eventually saturates, with new species emerging only when others are driven to extinction. This view of macro-evolution contradicts an alternative hypothesis that biodiversity is unbounded, with species ever accumulating as they find new niches to occupy. These contrasting theories of biodiversity dynamics yield fundamentally different explanations for the disparity in species richness across taxa and regions. Here, we test whether speciation rates have decayed or remained constant over time, and whether biodiversity is saturated or still expanding. We first derive a general likelihood expression for internode distances in a phylogeny, based on the well-known coalescent process from population genetics. This expression accounts for either time-constant or time-variable rates, time-constant or time-variable diversity, and completely or incompletely sampled phylogenies. We then compare the performance of different diversification scenarios in explaining a set of 289 phylogenies representing amphibians, arthropods, birds, mammals, mollusks, and flowering plants. Our results indicate that speciation rates typically decay over time, but that diversity is still expanding at present. The evidence for expanding-diversity models suggests that an upper limit to biodiversity has not yet been reached, or that no such limit exists.

Coalescent genealogy samplers: windows into population history.

Science.gov (United States)

Kuhner, Mary K

2009-02-01

Coalescent genealogy samplers attempt to estimate past qualities of a population, such as its size, growth rate, patterns of gene flow or time of divergence from another population, based on samples of molecular data. Genealogy samplers are increasingly popular because of their potential to disentangle complex population histories. In the last decade they have been widely applied to systems ranging from humans to viruses. Findings include detection of unexpected reproductive inequality in fish, new estimates of historical whale abundance, exoneration of humans for the prehistoric decline of bison and inference of a selective sweep on the human Y chromosome. This review summarizes available genealogy-sampler software, including data requirements and limitations on the use of each program.

Characterization of solids deposited on the modular caustic-side solvent extraction unit (MCU) coalescer media removed in October 2014

Energy Technology Data Exchange (ETDEWEB)

Fondeur, F. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2016-03-01

In February 2015, Savannah River National Laboratory (SRNL) received a Strip Effluent (SE) coalescer (FLT-304) from MCU. That coalescer was first installed at MCU in July 2014 and removed in October 2014. While processing approximately 31,400 gallons of strip solution, the pressure drop steadily increased from 1 psi to beyond the administrative limit of 20 psi. The physical and chemical analysis was conducted on this coalescer to determine the mechanism that led to the plugging of this coalescer. Characterization of this coalescer revealed the adsorption of organic containing amines as well as MCU modifier. The amines are probably from the decomposition of the suppressor (TiDG) as well as from bacteria. This adsorption may have changed the surface energetics (characteristics) of the coalescer fibers and therefore, their wetting behavior. A very small amount of inorganic solids were found to have deposited on this coalescer (possibly an artifact of cleaning the coalescer with Boric acid. However, we believe that inorganic precipitation, as has been seen in the past, did not play a role in the high pressure drop rise of this coalescer. With regards to the current practice of reducing the radioactive content of the SE coalescer, it is recommended that future SE coalescer should be flushed with 10 mM boric acid which is currently used at MCU. Plugging of the SE coalescer was most likely due to the formation and accumulation of a water-in-oil emulsion that reduced the overall porosity of the coalescer. There is also evidence that a bimodal oil particle distribution may have entered and deposited in the coalescer and caused the initial increase in pressure drop.

Bayesian Graphical Models

DEFF Research Database (Denmark)

Jensen, Finn Verner; Nielsen, Thomas Dyhre

2016-01-01

Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...

SHORT-TERM SOLAR FLARE LEVEL PREDICTION USING A BAYESIAN NETWORK APPROACH

International Nuclear Information System (INIS)

Yu Daren; Huang Xin; Hu Qinghua; Zhou Rui; Wang Huaning; Cui Yanmei

2010-01-01

A Bayesian network approach for short-term solar flare level prediction has been proposed based on three sequences of photospheric magnetic field parameters extracted from Solar and Heliospheric Observatory/Michelson Doppler Imager longitudinal magnetograms. The magnetic measures, the maximum horizontal gradient, the length of neutral line, and the number of singular points do not have determinate relationships with solar flares, so the solar flare level prediction is considered as an uncertainty reasoning process modeled by the Bayesian network. The qualitative network structure which describes conditional independent relationships among magnetic field parameters and the quantitative conditional probability tables which determine the probabilistic values for each variable are learned from the data set. Seven sequential features-the maximum, the mean, the root mean square, the standard deviation, the shape factor, the crest factor, and the pulse factor-are extracted to reduce the dimensions of the raw sequences. Two Bayesian network models are built using raw sequential data (BN R ) and feature extracted data (BN F ), respectively. The explanations of these models are consistent with physical analyses of experts. The performances of the BN R and the BN F appear comparable with other methods. More importantly, the comprehensibility of the Bayesian network models is better than other methods.

Coalescence and movement of nanobubbles studied with tapping mode AFM and tip-bubble interaction analysis

International Nuclear Information System (INIS)

Bhushan, Bharat; Wang Yuliang; Maali, Abdelhamid

2008-01-01

Imaging of a polystyrene (PS) coated silicon wafer immersed in deionized (DI) water was conducted using atomic force microscopy (AFM) in the tapping mode (TMAFM). As reported earlier, spherical cap-like domains, referred to as nanobubbles, were observed to be distributed on the PS surface. Experiments reveal that, in addition to the well-known parameter of scan load, scan speed is also an important parameter which affects nanobubble coalescence. The process of nanobubble coalescence was studied. It was found that during coalescence, small nanobubbles were easily moved and merged into bigger ones. Based on the interaction between the AFM cantilever tip and a bubble in the so-called force modulation mode of TMAFM, bubble height and adhesive force information for a given bubble was extracted. A viscoelastic model is used to obtain the interaction stiffness and damping coefficient, which provides a method to obtain the mechanical properties of nanobubbles. The model was further used to study the effect of surface tension force on attractive interaction force and contact angle hysteresis on the changes of the interaction damping coefficient during tip-bubble interaction.

A comprehensive study of task coalescing for selecting parallelism granularity in a two-stage bidiagonal reduction

KAUST Repository

Haidar, Azzam

2012-05-01

We present new high performance numerical kernels combined with advanced optimization techniques that significantly increase the performance of parallel bidiagonal reduction. Our approach is based on developing efficient fine-grained computational tasks as well as reducing overheads associated with their high-level scheduling during the so-called bulge chasing procedure that is an essential phase of a scalable bidiagonalization procedure. In essence, we coalesce multiple tasks in a way that reduces the time needed to switch execution context between the scheduler and useful computational tasks. At the same time, we maintain the crucial information about the tasks and their data dependencies between the coalescing groups. This is the necessary condition to preserve numerical correctness of the computation. We show our annihilation strategy based on multiple applications of single orthogonal reflectors. Despite non-trivial characteristics in computational complexity and memory access patterns, our optimization approach smoothly applies to the annihilation scenario. The coalescing positively influences another equally important aspect of the bulge chasing stage: the memory reuse. For the tasks within the coalescing groups, the data is retained in high levels of the cache hierarchy and, as a consequence, operations that are normally memory-bound increase their ratio of computation to off-chip communication and become compute-bound which renders them amenable to efficient execution on multicore architectures. The performance for the new two-stage bidiagonal reduction is staggering. Our implementation results in up to 50-fold and 12-fold improvement (∼130 Gflop/s) compared to the equivalent routines from LAPACK V3.2 and Intel MKL V10.3, respectively, on an eight socket hexa-core AMD Opteron multicore shared-memory system with a matrix size of 24000 x 24000. Last but not least, we provide a comprehensive study on the impact of the coalescing group size in terms of cache

Bayesian Inference for Functional Dynamics Exploring in fMRI Data

Directory of Open Access Journals (Sweden)

Xuan Guo

2016-01-01

Full Text Available This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM, Bayesian Connectivity Change Point Model (BCCPM, and Dynamic Bayesian Variable Partition Model (DBVPM, and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

[Overcoming the limitations of the descriptive and categorical approaches in psychiatric diagnosis: a proposal based on Bayesian networks].

Science.gov (United States)

Sorias, Soli

2015-01-01

Efforts to overcome the problems of descriptive and categorical approaches have not yielded results. In the present article, psychiatric diagnosis using Bayesian networks is proposed. Instead of a yes/no decision, Bayesian networks give the probability of diagnostic category inclusion, thereby yielding both a graded, i.e., dimensional diagnosis, and a value of the certainty of the diagnosis. With the use of Bayesian networks in the diagnosis of mental disorders, information about etiology, associated features, treatment outcome, and laboratory results may be used in addition to clinical signs and symptoms, with each of these factors contributing proportionally to their own specificity and sensitivity. Furthermore, a diagnosis (albeit one with a lower probability) can be made even with incomplete, uncertain, or partially erroneous information, and patients whose symptoms are below the diagnostic threshold can be evaluated. Lastly, there is no need of NOS or "unspecified" categories, and comorbid disorders become different dimensions of the diagnostic evaluation. Bayesian diagnoses allow the preservation of current categories and assessment methods, and may be used concurrently with criteria-based diagnoses. Users need not put in extra effort except to collect more comprehensive information. Unlike the Research Domain Criteria (RDoC) project, the Bayesian approach neither increases the diagnostic validity of existing categories nor explains the pathophysiological mechanisms of mental disorders. It, however, can be readily integrated to present classification systems. Therefore, the Bayesian approach may be an intermediate phase between criteria-based diagnosis and the RDoC ideal.

Dynamic model based on Bayesian method for energy security assessment

International Nuclear Information System (INIS)

Augutis, Juozas; Krikštolaitis, Ričardas; Pečiulytė, Sigita; Žutautaitė, Inga

2015-01-01

Highlights: • Methodology for dynamic indicator model construction and forecasting of indicators. • Application of dynamic indicator model for energy system development scenarios. • Expert judgement involvement using Bayesian method. - Abstract: The methodology for the dynamic indicator model construction and forecasting of indicators for the assessment of energy security level is presented in this article. An indicator is a special index, which provides numerical values to important factors for the investigated area. In real life, models of different processes take into account various factors that are time-dependent and dependent on each other. Thus, it is advisable to construct a dynamic model in order to describe these dependences. The energy security indicators are used as factors in the dynamic model. Usually, the values of indicators are obtained from statistical data. The developed dynamic model enables to forecast indicators’ variation taking into account changes in system configuration. The energy system development is usually based on a new object construction. Since the parameters of changes of the new system are not exactly known, information about their influences on indicators could not be involved in the model by deterministic methods. Thus, dynamic indicators’ model based on historical data is adjusted by probabilistic model with the influence of new factors on indicators using the Bayesian method

Gravitational Waves from Coalescing Binary Black Holes: Theoretical and Experimental Challenges

CERN Multimedia

CERN. Geneva

2010-01-01

(LIGO/VIRGO/GEO/...) is currently taking data near its planned sensitivity. Coalescing black hole binaries are among the most promising, and most exciting, gravitational wave sources for these detectors. The talk will review the theoretical and experimental challenges that must be met in order to successfully detect gravitational waves from coalescing black hole binaries, and to be able to reliably measure the physical parameters of the source (masses, spins, ...).

Critical Void Volume Fraction fc at Void Coalescence for S235JR Steel at Low Initial Stress Triaxiality

Science.gov (United States)

Grzegorz Kossakowski, Paweł; Wciślik, Wiktor

2017-10-01

The paper is concerned with the nucleation, growth and coalescence of microdefects in the form of voids in S235JR steel. The material is known to be one of the basic steel grades commonly used in the construction industry. The theory and methods of damage mechanics were applied to determine and describe the failure mechanisms that occur when the material undergoes deformation. Until now, engineers have generally employed the Gurson-Tvergaard- Needleman model. This material model based on damage mechanics is well suited to define and analyze failure processes taking place in the microstructure of S235JR steel. It is particularly important to determine the critical void volume fraction fc , which is one of the basic parameters of the Gurson-Tvergaard-Needleman material model. As the critical void volume fraction fc refers to the failure stage, it is determined from the data collected for the void coalescence phase. A case of multi-axial stresses is considered taking into account the effects of spatial stress state. In this study, the parameter of stress triaxiality η was used to describe the failure phenomena. Cylindrical tensile specimens with a circumferential notch were analysed to obtain low values of initial stress triaxiality (η = 0.556 of the range) in order to determine the critical void volume fraction fc . It is essential to emphasize how unique the method applied is and how different it is from the other more common methods involving parameter calibration, i.e. curve-fitting methods. The critical void volume fraction fc at void coalescence was established through digital image analysis of surfaces of S235JR steel, which involved studying real, physical results obtained directly from the material tested.

Assessment of Mass Transfer Coefficients in Coalescing Slug Flow in Vertical Pipes and Applications to Tubular Airlift Membrane Bioreactors

DEFF Research Database (Denmark)

Ratkovich, Nicolas Rios; Berube, P.R.; Nopens, I.

2011-01-01

by the gas flow. It was noted that coalescence of bubbles affects the MTH. Coalescence increased the “width” of the peaks (i.e. the estimate of the variability of the mass transfer coefficient) and the height of the peak (i.e. amount of time that a mass transfer coefficient of a given value is maintained......). A semi-empirical relationship based on the Lévêque relationship for the Sherwood number (mass transfer coefficient) was formulated for the laminar regime. A test case comparison between water and activated sludge was performed based on full-scale airlift MBR operational conditions. It was found...

Bayesian artificial intelligence

CERN Document Server

Korb, Kevin B

2003-01-01

As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.

Bayesian models based on test statistics for multiple hypothesis testing problems.

Science.gov (United States)

Ji, Yuan; Lu, Yiling; Mills, Gordon B

2008-04-01

We propose a Bayesian method for the problem of multiple hypothesis testing that is routinely encountered in bioinformatics research, such as the differential gene expression analysis. Our algorithm is based on modeling the distributions of test statistics under both null and alternative hypotheses. We substantially reduce the complexity of the process of defining posterior model probabilities by modeling the test statistics directly instead of modeling the full data. Computationally, we apply a Bayesian FDR approach to control the number of rejections of null hypotheses. To check if our model assumptions for the test statistics are valid for various bioinformatics experiments, we also propose a simple graphical model-assessment tool. Using extensive simulations, we demonstrate the performance of our models and the utility of the model-assessment tool. In the end, we apply the proposed methodology to an siRNA screening and a gene expression experiment.

Bayesian inferences of generation and growth of corrosion defects on energy pipelines based on imperfect inspection data

International Nuclear Information System (INIS)

Qin, H.; Zhou, W.; Zhang, S.

2015-01-01

Stochastic process-based models are developed to characterize the generation and growth of metal-loss corrosion defects on oil and gas steel pipelines. The generation of corrosion defects over time is characterized by the non-homogenous Poisson process, and the growth of depths of individual defects is modeled by the non-homogenous gamma process (NHGP). The defect generation and growth models are formulated in a hierarchical Bayesian framework, whereby the parameters of the models are evaluated from the in-line inspection (ILI) data through the Bayesian updating by accounting for the probability of detection (POD) and measurement errors associated with the ILI data. The Markov Chain Monte Carlo (MCMC) simulation in conjunction with the data augmentation (DA) technique is employed to carry out the Bayesian updating. Numerical examples that involve simulated ILI data are used to illustrate and validate the proposed methodology. - Highlights: • Bayesian updating of growth and generation models of defects on energy pipelines. • Non-homogeneous Poisson process for defect generation. • Non-homogeneous gamma process for defect growth. • Updating based on inspection data with detecting and sizing uncertainties. • MCMC in conjunction with data augmentation technique employed for the updating.

Coalescence of silver unidimensional structures by molecular dynamics simulation

International Nuclear Information System (INIS)

Perez A, M.; Gutierrez W, C.E.; Mondragon, G.; Arenas, J.

2007-01-01

The study of nanoparticles coalescence and silver nano rods phenomena by means of molecular dynamics simulation under the thermodynamic laws is reported. In this work we focus ourselves to see the conditions under which the one can be given one dimension growth of silver nano rods for the coalescence phenomenon among two nano rods or one nano rod and one particle; what allows us to study those structural, dynamic and morphological properties of the silver nano rods to different thermodynamic conditions. The simulations are carried out using the Sutton-Chen potentials of interaction of many bodies that allow to obtain appropriate results with the real physical systems. (Author)

Analysing the length of care episode after hip fracture: a nonparametric and a parametric Bayesian approach.

Science.gov (United States)

Riihimäki, Jaakko; Sund, Reijo; Vehtari, Aki

2010-06-01

Effective utilisation of limited resources is a challenge for health care providers. Accurate and relevant information extracted from the length of stay distributions is useful for management purposes. Patient care episodes can be reconstructed from the comprehensive health registers, and in this paper we develop a Bayesian approach to analyse the length of care episode after a fractured hip. We model the large scale data with a flexible nonparametric multilayer perceptron network and with a parametric Weibull mixture model. To assess the performances of the models, we estimate expected utilities using predictive density as a utility measure. Since the model parameters cannot be directly compared, we focus on observables, and estimate the relevances of patient explanatory variables in predicting the length of stay. To demonstrate how the use of the nonparametric flexible model is advantageous for this complex health care data, we also study joint effects of variables in predictions, and visualise nonlinearities and interactions found in the data.

Hierarchy Bayesian model based services awareness of high-speed optical access networks

Science.gov (United States)

Bai, Hui-feng

2018-03-01

As the speed of optical access networks soars with ever increasing multiple services, the service-supporting ability of optical access networks suffers greatly from the shortage of service awareness. Aiming to solve this problem, a hierarchy Bayesian model based services awareness mechanism is proposed for high-speed optical access networks. This approach builds a so-called hierarchy Bayesian model, according to the structure of typical optical access networks. Moreover, the proposed scheme is able to conduct simple services awareness operation in each optical network unit (ONU) and to perform complex services awareness from the whole view of system in optical line terminal (OLT). Simulation results show that the proposed scheme is able to achieve better quality of services (QoS), in terms of packet loss rate and time delay.

Theory-based Bayesian models of inductive learning and reasoning.

Science.gov (United States)

Tenenbaum, Joshua B; Griffiths, Thomas L; Kemp, Charles

2006-07-01

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Embracing Uncertainty: The Interface of Bayesian Statistics and Cognitive Psychology

Directory of Open Access Journals (Sweden)

Judith L. Anderson

1998-06-01

Full Text Available Ecologists working in conservation and resource management are discovering the importance of using Bayesian analytic methods to deal explicitly with uncertainty in data analyses and decision making. However, Bayesian procedures require, as inputs and outputs, an idea that is problematic for the human brain: the probability of a hypothesis ("single-event probability". I describe several cognitive concepts closely related to single-event probabilities, and discuss how their interchangeability in the human mind results in "cognitive illusions," apparent deficits in reasoning about uncertainty. Each cognitive illusion implies specific possible pitfalls for the use of single-event probabilities in ecology and resource management. I then discuss recent research in cognitive psychology showing that simple tactics of communication, suggested by an evolutionary perspective on human cognition, help people to process uncertain information more effectively as they read and talk about probabilities. In addition, I suggest that carefully considered standards for methodology and conventions for presentation may also make Bayesian analyses easier to understand.

bspmma: An R Package for Bayesian Semiparametric Models for Meta-Analysis

Directory of Open Access Journals (Sweden)

Deborah Burr

2012-07-01

Full Text Available We introduce an R package, bspmma, which implements a Dirichlet-based random effects model specific to meta-analysis. In meta-analysis, when combining effect estimates from several heterogeneous studies, it is common to use a random-effects model. The usual frequentist or Bayesian models specify a normal distribution for the true effects. However, in many situations, the effect distribution is not normal, e.g., it can have thick tails, be skewed, or be multi-modal. A Bayesian nonparametric model based on mixtures of Dirichlet process priors has been proposed in the literature, for the purpose of accommodating the non-normality. We review this model and then describe a competitor, a semiparametric version which has the feature that it allows for a well-defined centrality parameter convenient for determining whether the overall effect is significant. This second Bayesian model is based on a different version of the Dirichlet process prior, and we call it the "conditional Dirichlet model". The package contains functions to carry out analyses based on either the ordinary or the conditional Dirichlet model, functions for calculating certain Bayes factors that provide a check on the appropriateness of the conditional Dirichlet model, and functions that enable an empirical Bayes selection of the precision parameter of the Dirichlet process. We illustrate the use of the package on two examples, and give an interpretation of the results in these two different scenarios.

Daniel Goodman’s empirical approach to Bayesian statistics

Science.gov (United States)

Gerrodette, Tim; Ward, Eric; Taylor, Rebecca L.; Schwarz, Lisa K.; Eguchi, Tomoharu; Wade, Paul; Himes Boor, Gina

2016-01-01

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

Compiling Relational Bayesian Networks for Exact Inference

DEFF Research Database (Denmark)

Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan

2004-01-01

We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...

Free surface flows: coalescence, spreading and dewetting

NARCIS (Netherlands)

Hernandez Sanchez, J.F.

2015-01-01

Capillary and wetting phenomena are an essential part of nature. Its presence is noticed in many circumstances where solid and liquid surfaces come into contact. In this thesis different types of capillary free surface flows are studied. The topics discussed are mainly the coalescence of viscous

Predicting Click-Through Rates of New Advertisements Based on the Bayesian Network

Directory of Open Access Journals (Sweden)

Zhipeng Fang

2014-01-01

Full Text Available Most classical search engines choose and rank advertisements (ads based on their click-through rates (CTRs. To predict an ad’s CTR, historical click information is frequently concerned. To accurately predict the CTR of the new ads is challenging and critical for real world applications, since we do not have plentiful historical data about these ads. Adopting Bayesian network (BN as the effective framework for representing and inferring dependencies and uncertainties among variables, in this paper, we establish a BN-based model to predict the CTRs of new ads. First, we built a Bayesian network of the keywords that are used to describe the ads in a certain domain, called keyword BN and abbreviated as KBN. Second, we proposed an algorithm for approximate inferences of the KBN to find similar keywords with those that describe the new ads. Finally based on the similar keywords, we obtain the similar ads and then calculate the CTR of the new ad by using the CTRs of the ads that are similar with the new ad. Experimental results show the efficiency and accuracy of our method.

A variational void coalescence model for ductile metals

KAUST Repository

Siddiq, Amir

2011-08-17

We present a variational void coalescence model that includes all the essential ingredients of failure in ductile porous metals. The model is an extension of the variational void growth model by Weinberg et al. (Comput Mech 37:142-152, 2006). The extended model contains all the deformation phases in ductile porous materials, i.e. elastic deformation, plastic deformation including deviatoric and volumetric (void growth) plasticity followed by damage initiation and evolution due to void coalescence. Parametric studies have been performed to assess the model\\'s dependence on the different input parameters. The model is then validated against uniaxial loading experiments for different materials. We finally show the model\\'s ability to predict the damage mechanisms and fracture surface profile of a notched round bar under tension as observed in experiments. © Springer-Verlag 2011.

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

Science.gov (United States)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

Bayesian network as a modelling tool for risk management in agriculture

DEFF Research Database (Denmark)

Rasmussen, Svend; Madsen, Anders L.; Lund, Mogens

. In this paper we use Bayesian networks as an integrated modelling approach for representing uncertainty and analysing risk management in agriculture. It is shown how historical farm account data may be efficiently used to estimate conditional probabilities, which are the core elements in Bayesian network models....... We further show how the Bayesian network model RiBay is used for stochastic simulation of farm income, and we demonstrate how RiBay can be used to simulate risk management at the farm level. It is concluded that the key strength of a Bayesian network is the transparency of assumptions......, and that it has the ability to link uncertainty from different external sources to budget figures and to quantify risk at the farm level....

Compiling Relational Bayesian Networks for Exact Inference

DEFF Research Database (Denmark)

Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark

2006-01-01

We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...

A coalescence model for uranium exploration

International Nuclear Information System (INIS)

Stuart-Williams, V.; Taylor, C.M.

1983-01-01

Uranium mineralization was found in the Pristerognathus-Diictodon Assemblage Zone of the Teekloof Formation, Beaufort Group, west of Beaufort West, Cape Province, South Africa. All the anomalies can be related to a single mineralization model. Mineralization is found at the termination of a silt parting between two coalescing sandstones and lies in the lower sandstone as an inclined zone dipping downflow from the termination of the silt parting. The existence of primary Eh-pH gradient is indicated by a uranium-molybdenum zonation, the molybdenum lying above the uranium mineralization. The upper sandstone was an oxidizing fluvial channel in an arid environment through which uranyl carbonate was being transported in solution. Carbonaceous material undergoing anaerobic bacterial breakdown generated a weakly reducing fluid in the lower sandstone. Carbonaceous material at the REDOX front developed between the two mixing fluids at the point of sandstone coalescence reduced uranyl carbonates in solution. Once reduced the uranium minerals remained stable because the conditions in the REDOX front were only very weakly oxidizing. As floodplain aggradation continued, the upper sandstone was buried and the entire sandstone couplet became reducing, permanently stabilizing the uranium mineralization

Bayesian phylogenetic estimation of fossil ages.

Science.gov (United States)

Drummond, Alexei J; Stadler, Tanja

2016-07-19

Recent advances have allowed for both morphological fossil evidence and molecular sequences to be integrated into a single combined inference of divergence dates under the rule of Bayesian probability. In particular, the fossilized birth-death tree prior and the Lewis-Mk model of discrete morphological evolution allow for the estimation of both divergence times and phylogenetic relationships between fossil and extant taxa. We exploit this statistical framework to investigate the internal consistency of these models by producing phylogenetic estimates of the age of each fossil in turn, within two rich and well-characterized datasets of fossil and extant species (penguins and canids). We find that the estimation accuracy of fossil ages is generally high with credible intervals seldom excluding the true age and median relative error in the two datasets of 5.7% and 13.2%, respectively. The median relative standard error (RSD) was 9.2% and 7.2%, respectively, suggesting good precision, although with some outliers. In fact, in the two datasets we analyse, the phylogenetic estimate of fossil age is on average less than 2 Myr from the mid-point age of the geological strata from which it was excavated. The high level of internal consistency found in our analyses suggests that the Bayesian statistical model employed is an adequate fit for both the geological and morphological data, and provides evidence from real data that the framework used can accurately model the evolution of discrete morphological traits coded from fossil and extant taxa. We anticipate that this approach will have diverse applications beyond divergence time dating, including dating fossils that are temporally unconstrained, testing of the 'morphological clock', and for uncovering potential model misspecification and/or data errors when controversial phylogenetic hypotheses are obtained based on combined divergence dating analyses.This article is part of the themed issue 'Dating species divergences using

Bayesian Mediation Analysis

OpenAIRE

Yuan, Ying; MacKinnon, David P.

2009-01-01

This article proposes Bayesian analysis of mediation effects. Compared to conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian mediation analysis, inference is straightforward and exact, which makes it appealing for studies with small samples. Third, the Bayesian approach is conceptua...

Development and validation of models for bubble coalescence and breakup. Final report

International Nuclear Information System (INIS)

Liao, Y.; Lucas, D.

2013-02-01

A new generalized model for bubble coalescence and breakup has been developed. It is based on physical considerations and takes into account various mechanisms that can lead to bubble coalescence and breakup. First, in a detailed literature review, the available models were compiled and analyzed. It turned out that many of them show a contradictory behaviour. None of these models allows the prediction of the evolution of bubble size distributions along a pipe flow for a wide range of combinations of flow rates of the gas and the liquid phase. The new model has been extensively studied in a simplified Test-Solver. Although this does not cover all details of a developing flow along the pipe, it allows - in contrast to a CFD code - to conduct a large number of variational calculations to investigate the influence of individual sizes and models. Coalescence and breakup cannot be considered separately from other phenomena and models that reflect these phenomena. There are close interactions with the turbulence of the liquid phase and the momentum exchange between phases. Since the dissipation rate of turbulent kinetic energy is a direct input parameter for the new model, the turbulence modelling has been studied very carefully. To validate the model, a special experimental series for air-water flows was used, conducted at the TOPFLOW facility in an 8-meter long DN200 pipe. The data are characterized by high quality and were produced within the TOPFLOW-II project. The test series aims to provide a basis for the work presented here. Predicting the evolution of the bubble size distribution along the pipe could be improved significantly in comparison to the previous standard models for bubble coalescence and breakup implemented in CFX. However some quantitative discrepancies remain. The full model equations as well as an implementation as ''User-FORTRAN'' in CFX are available and can be used for further work on the simulation of poly-disperse bubbly flows.

Introduction to Bayesian statistics

CERN Document Server

Bolstad, William M

2017-01-01

There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...

Void growth to coalescence in a non-local material

DEFF Research Database (Denmark)

Niordson, Christian Frithiof

2008-01-01

of different material length parameters in a multi-parameter theory is studied, and it is shown that the important length parameter is the same as under purely hydrostatic loading. It is quantified how micron scale voids grow less rapidly than larger voids, and the implications of this in the overall strength...... of the material is emphasized. The size effect on the onset of coalescence is studied, and results for the void volume fraction and the strain at the onset of coalescence are presented. It is concluded that for cracked specimens not only the void volume fraction, but also the typical void size is of importance...... to the fracture strength of ductile materials....

Coalescence of liquid drops: Different models versus experiment

KAUST Repository

Sprittles, J. E.; Shikhmurzaev, Y. D.

2012-01-01

help to further elucidate the details of the coalescence phenomenon. As a by-product of our research, the range of validity of different "scaling laws" advanced as approximate solutions to the problem formulated using the conventional model

The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction

DEFF Research Database (Denmark)

Sabuncu, Mert R.; Van Leemput, Koen

2011-01-01

This paper presents the Relevance VoxelMachine (RVoxM), a Bayesian multivariate pattern analysis (MVPA) algorithm that is specifically designed for making predictions based on image data. In contrast to generic MVPA algorithms that have often been used for this purpose, the method is designed to ...

Design of Korean nuclear reliability data-base network using a two-stage Bayesian concept

International Nuclear Information System (INIS)

Kim, T.W.; Jeong, K.S.; Chae, S.K.

1987-01-01

In an analysis of probabilistic risk, safety, and reliability of a nuclear power plant, the reliability data base (DB) must be established first. As the importance of the reliability data base increases, event reporting systems such as the US Nuclear Regulatory Commission's Licensee Event Report and the International Atomic Energy Agency's Incident Reporting System have been developed. In Korea, however, the systematic reliability data base is not yet available. Therefore, foreign data bases have been directly quoted in reliability analyses of Korean plants. In order to develop a reliability data base for Korean plants, the problem is which methodology is to be used, and the application limits of the selected method must be solved and clarified. After starting the commercial operation of Korea Nuclear Unit-1 (KNU-1) in 1978, six nuclear power plants have begun operation. Of these, only KNU-3 is a Canada Deuterium Uranium pressurized heavy-water reactor, and the others are all pressurized water reactors. This paper describes the proposed reliability data-base network (KNRDS) for Korean nuclear power plants in the context of two-stage Bayesian (TSB) procedure of Kaplan. It describes the concept of TSB to obtain the Korean-specific plant reliability data base, which is updated with the incorporation of both the reported generic reliability data and the operation experiences of similar plants

Role of microtexture in the interaction and coalescence of hydrogen-induced cracks

Energy Technology Data Exchange (ETDEWEB)

Venegas, V. [Departamento de Ingenieria Metalurgica, IPN-ESIQIE, UPALM Edif. 7, Zacatenco, Mexico D.F. 07738 (Mexico); Caleyo, F. [Departamento de Ingenieria Metalurgica, IPN-ESIQIE, UPALM Edif. 7, Zacatenco, Mexico D.F. 07738 (Mexico)], E-mail: fcaleyo@gmail.com; Baudin, T. [Laboratoire de Physico-Chimie de l' Etat Solide, ICMMO, UMR CNRS 8182, Batiment 410, Universite de Paris Sud, 91405, Orsay, Cedex (France); Hallen, J.M. [Departamento de Ingenieria Metalurgica, IPN-ESIQIE, UPALM Edif. 7, Zacatenco, Mexico D.F. 07738 (Mexico); Penelle, R. [Laboratoire de Physico-Chimie de l' Etat Solide, ICMMO, UMR CNRS 8182, Batiment 410, Universite de Paris Sud, 91405, Orsay, Cedex (France)

2009-05-15

The role of microtexture in hydrogen-induced crack interaction and coalescence is investigated in line pipe steels using electron backscatter diffraction. Experimental evidence shows that, depending on the local grain orientation, crack interaction and coalescence can depart from the conditions predicted by the mixed-mode fracture mechanics of isotropic linear elastic materials. Stress simulation and microtexture analysis are used to explain the experimental observations.

Role of microtexture in the interaction and coalescence of hydrogen-induced cracks

International Nuclear Information System (INIS)

Venegas, V.; Caleyo, F.; Baudin, T.; Hallen, J.M.; Penelle, R.

2009-01-01

The role of microtexture in hydrogen-induced crack interaction and coalescence is investigated in line pipe steels using electron backscatter diffraction. Experimental evidence shows that, depending on the local grain orientation, crack interaction and coalescence can depart from the conditions predicted by the mixed-mode fracture mechanics of isotropic linear elastic materials. Stress simulation and microtexture analysis are used to explain the experimental observations.

Using amphiphilic nanostructures to enable long-range ensemble coalescence and surface rejuvenation in dropwise condensation.

Science.gov (United States)

Anderson, David M; Gupta, Maneesh K; Voevodin, Andrey A; Hunter, Chad N; Putnam, Shawn A; Tsukruk, Vladimir V; Fedorov, Andrei G

2012-04-24

Controlling coalescence events in a heterogeneous ensemble of condensing droplets on a surface is an outstanding fundamental challenge in surface and interfacial sciences, with a broad practical importance in applications ranging from thermal management of high-performance electronic devices to moisture management in high-humidity environments. Nature-inspired superhydrophobic surfaces have been actively explored to enhance heat and mass transfer rates by achieving favorable dynamics during dropwise condensation; however, the effectiveness of such chemically homogeneous surfaces has been limited because condensing droplets tend to form as pinned Wenzel drops rather than mobile Cassie ones. Here, we introduce an amphiphilic nanostructured surface, consisting of a hydrophilic base with hydrophobic tips, which promotes the periodic regeneration of nucleation sites for small droplets, thus rendering the surface self-rejuvenating. This unique amphiphilic nanointerface generates an arrangement of condensed Wenzel droplets that are fluidically linked by a wetted sublayer, promoting previously unobserved coalescence events where numerous droplets simultaneously merge, without direct contact. Such ensemble coalescences rapidly create fresh nucleation sites, thereby shifting the overall population toward smaller droplets and enhancing the rates of mass and heat transfer during condensation.

On localization and void coalescence as a precursor to ductile fracture.

Science.gov (United States)

Tekoğlu, C; Hutchinson, J W; Pardoen, T

2015-03-28

Two modes of plastic flow localization commonly occur in the ductile fracture of structural metals undergoing damage and failure by the mechanism involving void nucleation, growth and coalescence. The first mode consists of a macroscopic localization, usually linked to the softening effect of void nucleation and growth, in either a normal band or a shear band where the thickness of the band is comparable to void spacing. The second mode is coalescence with plastic strain localizing to the ligaments between voids by an internal necking process. The ductility of a material is tied to the strain at macroscopic localization, as this marks the limit of uniform straining at the macroscopic scale. The question addressed is whether macroscopic localization occurs prior to void coalescence or whether the two occur simultaneously. The relation between these two modes of localization is studied quantitatively in this paper using a three-dimensional elastic-plastic computational model representing a doubly periodic array of voids within a band confined between two semi-infinite outer blocks of the same material but without voids. At sufficiently high stress triaxiality, a clear separation exists between the two modes of localization. At lower stress triaxialities, the model predicts that the onset of macroscopic localization and coalescence occur simultaneously. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Electricity Purchase Optimization Decision Based on Data Mining and Bayesian Game

Directory of Open Access Journals (Sweden)

Yajing Gao

2018-04-01

Full Text Available The openness of the electricity retail market results in the power retailers facing fierce competition in the market. This article aims to analyze the electricity purchase optimization decision-making of each power retailer with the background of the big data era. First, in order to guide the power retailer to make a purchase of electricity, this paper considers the users’ historical electricity consumption data and a comprehensive consideration of multiple factors, then uses the wavelet neural network (WNN model based on “meteorological similarity day (MSD” to forecast the user load demand. Second, in order to guide the quotation of the power retailer, this paper considers the multiple factors affecting the electricity price to cluster the sample set, and establishes a Genetic algorithm- back propagation (GA-BP neural network model based on fuzzy clustering (FC to predict the short-term market clearing price (MCP. Thirdly, based on Sealed-bid Auction (SA in game theory, a Bayesian Game Model (BGM of the power retailer’s bidding strategy is constructed, and the optimal bidding strategy is obtained by obtaining the Bayesian Nash Equilibrium (BNE under different probability distributions. Finally, a practical example is proposed to prove that the model and method can provide an effective reference for the decision-making optimization of the sales company.

Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method.

Science.gov (United States)

Zhang, Guanglei; Liu, Fei; Liu, Jie; Luo, Jianwen; Xie, Yaoqin; Bai, Jing; Xing, Lei

2017-01-01

X-ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. Combining the principles of X-ray excitation of luminescence-based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow-beam XLCT, a cone beam XLCT (CB-XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using cone beam X-ray for XLCT is that the inverse problem here is seriously ill-conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB-XLCT reconstruction. The method utilizes a local regularization strategy based on Gaussian Markov random field to mitigate the ill-conditioness of CB-XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm is adopted to reconstruct the image with a voxel-based closed-form solution. Results of numerical simulations and mouse experiments show that the self-adaptive Bayesian method significantly improves the CB-XLCT image quality as compared with conventional methods.

Identifying food deserts and swamps based on relative healthy food access: a spatio-temporal Bayesian approach.

Science.gov (United States)

Luan, Hui; Law, Jane; Quick, Matthew

2015-12-30

Obesity and other adverse health outcomes are influenced by individual- and neighbourhood-scale risk factors, including the food environment. At the small-area scale, past research has analysed spatial patterns of food environments for one time period, overlooking how food environments change over time. Further, past research has infrequently analysed relative healthy food access (RHFA), a measure that is more representative of food purchasing and consumption behaviours than absolute outlet density. This research applies a Bayesian hierarchical model to analyse the spatio-temporal patterns of RHFA in the Region of Waterloo, Canada, from 2011 to 2014 at the small-area level. RHFA is calculated as the proportion of healthy food outlets (healthy outlets/healthy + unhealthy outlets) within 4-km from each small-area. This model measures spatial autocorrelation of RHFA, temporal trend of RHFA for the study region, and spatio-temporal trends of RHFA for small-areas. For the study region, a significant decreasing trend in RHFA is observed (-0.024), suggesting that food swamps have become more prevalent during the study period. For small-areas, significant decreasing temporal trends in RHFA were observed for all small-areas. Specific small-areas located in south Waterloo, north Kitchener, and southeast Cambridge exhibited the steepest decreasing spatio-temporal trends and are classified as spatio-temporal food swamps. This research demonstrates a Bayesian spatio-temporal modelling approach to analyse RHFA at the small-area scale. Results suggest that food swamps are more prevalent than food deserts in the Region of Waterloo. Analysing spatio-temporal trends of RHFA improves understanding of local food environment, highlighting specific small-areas where policies should be targeted to increase RHFA and reduce risk factors of adverse health outcomes such as obesity.

A PIV Study of Drop-interface Coalescence with Surfactants

Science.gov (United States)

Weheliye, Weheliye Hashi; Dong, Teng; Angeli, Panagiota

2017-11-01

In this work, the coalescence of a drop with an aqueous-organic interface was studied by Particle Image Velocimetry (PIV). The effect of surfactants on the drop surface evolution, the vorticity field and the kinetic energy distribution in the drop during coalescence were investigated. The coalescence took place in an acrylic rectangular box with 79% glycerol solution at the bottom and Exxsol D80 oil above. The glycerol solution drop was generated through a nozzle fixed at 2cm above the aqueous/oil interface and was seeded with Rhodamine particles. The whole process was captured by a high-speed camera. Different mass ratios of non-ionic surfactant Span80 to oil were studied. The increase of surfactant concentration promoted deformation of the interface before the rupture of the trapped oil film. At the early stages after film rupture, two counter-rotating vortices appeared at the bottom of the drop which then travelled to the upper part. The propagation rates, as well as the intensities of the vortices decreased at high surfactant concentrations. At early stages, the kinetic energy was mainly distributed near the bottom part of the droplet, while at later stages it was distributed near the upper part of the droplet. Programme Grant MEMPHIS, Chinese Scholarship Council (CSC).

Stochastic coalescence in Lagrangian cloud microphysics

Directory of Open Access Journals (Sweden)

P. Dziekan

2017-11-01

Full Text Available Stochasticity of the collisional growth of cloud droplets is studied using the super-droplet method (SDM of Shima et al.(2009. Statistics are calculated from ensembles of simulations of collision–coalescence in a single well-mixed cell. The SDM is compared with direct numerical simulations and the master equation. It is argued that SDM simulations in which one computational droplet represents one real droplet are at the same level of precision as the master equation. Such simulations are used to study fluctuations in the autoconversion time, the sol–gel transition and the growth rate of lucky droplets, which is compared with a theoretical prediction. The size of the coalescence cell is found to strongly affect system behavior. In small cells, correlations in droplet sizes and droplet depletion slow down rain formation. In large cells, collisions between raindrops are more frequent and this can also slow down rain formation. The increase in the rate of collision between raindrops may be an artifact caused by assuming an overly large well-mixed volume. The highest ratio of rain water to cloud water is found in cells of intermediate sizes. Next, we use these precise simulations to determine the validity of more approximate methods: the Smoluchowski equation and the SDM with multiplicities greater than 1. In the latter, we determine how many computational droplets are necessary to correctly model the expected number and the standard deviation of the autoconversion time. The maximal size of a volume that is turbulently well mixed with respect to coalescence is estimated at Vmix  =  1.5  ×  10−2 cm3. The Smoluchowski equation is not valid in such small volumes. It is argued that larger volumes can be considered approximately well mixed, but such approximation needs to be supported by a comparison with fine-grid simulations that resolve droplet motion.

Spectral analysis of the IntCal98 calibration curve: a Bayesian view

International Nuclear Information System (INIS)

Palonen, V.; Tikkanen, P.

2004-01-01

Preliminary results from a Bayesian approach to find periodicities in the IntCal98 calibration curve are given. It has been shown in the literature that the discrete Fourier transform (Schuster periodogram) corresponds to the use of an approximate Bayesian model of one harmonic frequency and Gaussian noise. Advantages of the Bayesian approach include the possibility to use models for variable, attenuated and multiple frequencies, the capability to analyze unevenly spaced data and the possibility to assess the significance and uncertainties of spectral estimates. In this work, a new Bayesian model using random walk noise to take care of the trend in the data is developed. Both Bayesian models are described and the first results of the new model are reported and compared with results from straightforward discrete-Fourier-transform and maximum-entropy-method spectral analyses

A lattice Boltzmann simulation of coalescence-induced droplet jumping on superhydrophobic surfaces with randomly distributed structures

Science.gov (United States)

Zhang, Li-Zhi; Yuan, Wu-Zhi

2018-04-01

The motion of coalescence-induced condensate droplets on superhydrophobic surface (SHS) has attracted increasing attention in energy-related applications. Previous researches were focused on regularly rough surfaces. Here a new approach, a mesoscale lattice Boltzmann method (LBM), is proposed and used to model the dynamic behavior of coalescence-induced droplet jumping on SHS with randomly distributed rough structures. A Fast Fourier Transformation (FFT) method is used to generate non-Gaussian randomly distributed rough surfaces with the skewness (Sk), kurtosis (K) and root mean square (Rq) obtained from real surfaces. Three typical spreading states of coalesced droplets are observed through LBM modeling on various rough surfaces, which are found to significantly influence the jumping ability of coalesced droplet. The coalesced droplets spreading in Cassie state or in composite state will jump off the rough surfaces, while the ones spreading in Wenzel state would eventually remain on the rough surfaces. It is demonstrated that the rough surfaces with smaller Sks, larger Rqs and a K at 3.0 are beneficial to coalescence-induced droplet jumping. The new approach gives more detailed insights into the design of SHS.

Knitmesh And Duplex-Nylon Type Coalescence Aids Use In Phase Disengagement

Directory of Open Access Journals (Sweden)

Hamit Topuz

2017-10-01

Full Text Available This study shows how dispersions consisted of droplet sizes ranging from 100 microns and above of immiscible liquids in agitated vessels coalesced and settled back to their phases by employing commercially known as knit-mesh made from stainless steel and nylon. These components known as higher surface energy and lower surface energy contained coalesce aids respectively. In addition to compare coalesce aid made purely from commercially known as duplex-nylon also used. The experimental set up was 13 scale of a single stage mixer-settler unit of the already existing unit which was in use at BNFL Springfield Works. The liquid liquid system made from 20 tri-butyl-phosphate TBP technical grade of odorless kerosene forming the light organic phase or solvent phase and 5 M nitric acid forming the heavy aqueous phase. The solvent phase contained 70 gram of uranium per liter. Uranium contained phase was supplied by above mentioned company.

Coalescence in PLA-PBAT blends under shear flow: Effects of blend preparation and PLA molecular weight

Energy Technology Data Exchange (ETDEWEB)

Nofar, M. [Center for High Performance Polymer and Composite Systems (CREPEC), Chemical Engineering Department, Polytechnique Montreal, Montreal, Quebec H3T 1J4, Canada and CREPEC, Department of Chemical Engineering, McGill University, Montreal, Quebec H3A 2B2 (Canada); Heuzey, M. C.; Carreau, P. J., E-mail: pierre.carreau@polymtl.ca [Center for High Performance Polymer and Composite Systems (CREPEC), Chemical Engineering Department, Polytechnique Montreal, Montreal, Quebec H3T 1J4 (Canada); Kamal, M. R. [CREPEC, Department of Chemical Engineering, McGill University, Montreal, Quebec H3A 2B2 (Canada); Randall, J. [NatureWorks LLC, 15305 Minnetonka Boulevard, Minnetonka, Minnesota 55345 (United States)

2016-07-15

Blends containing 75 wt. % of an amorphous polylactide (PLA) with two different molecular weights and 25 wt. % of a poly[(butylene adipate)-co-terephthalate] (PBAT) were prepared using either a Brabender batch mixer or a twin-screw extruder. These compounds were selected because blending PLA with PBAT can overcome various drawbacks of PLA such as its brittleness and processability limitations. In this study, we investigated the effects of varying the molecular weight of the PLA matrix and of two different mixing processes on the blend morphology and, further, on droplet coalescence during shearing. The rheological properties of these blends were investigated and the interfacial properties were analyzed using the Palierne emulsion model. Droplet coalescence was investigated by applying shear flows of 0.05 and 0.20 s{sup −1} at a fixed strain of 60. Subsequently, small amplitude oscillatory shear tests were conducted to investigate changes in the viscoelastic properties. The morphology of the blends was also examined using scanning electron microscope (SEM) micrographs. It was observed that the PBAT droplets were much smaller when twin-screw extrusion was used for the blend preparation. Shearing at 0.05 s{sup −1} induced significant droplet coalescence in all blends, but coalescence and changes in the viscoelastic properties were much more pronounced for the PLA-PBAT blend based on a lower molecular weight PLA. The viscoelastic responses were also somehow affected by the thermal degradation of the PLA matrix during the experiments.

Coalescence in PLA-PBAT blends under shear flow: Effects of blend preparation and PLA molecular weight

International Nuclear Information System (INIS)

Nofar, M.; Heuzey, M. C.; Carreau, P. J.; Kamal, M. R.; Randall, J.

2016-01-01

Blends containing 75 wt. % of an amorphous polylactide (PLA) with two different molecular weights and 25 wt. % of a poly[(butylene adipate)-co-terephthalate] (PBAT) were prepared using either a Brabender batch mixer or a twin-screw extruder. These compounds were selected because blending PLA with PBAT can overcome various drawbacks of PLA such as its brittleness and processability limitations. In this study, we investigated the effects of varying the molecular weight of the PLA matrix and of two different mixing processes on the blend morphology and, further, on droplet coalescence during shearing. The rheological properties of these blends were investigated and the interfacial properties were analyzed using the Palierne emulsion model. Droplet coalescence was investigated by applying shear flows of 0.05 and 0.20 s"−"1 at a fixed strain of 60. Subsequently, small amplitude oscillatory shear tests were conducted to investigate changes in the viscoelastic properties. The morphology of the blends was also examined using scanning electron microscope (SEM) micrographs. It was observed that the PBAT droplets were much smaller when twin-screw extrusion was used for the blend preparation. Shearing at 0.05 s"−"1 induced significant droplet coalescence in all blends, but coalescence and changes in the viscoelastic properties were much more pronounced for the PLA-PBAT blend based on a lower molecular weight PLA. The viscoelastic responses were also somehow affected by the thermal degradation of the PLA matrix during the experiments.

Bayesian Network Assessment Method for Civil Aviation Safety Based on Flight Delays

OpenAIRE

Huawei Wang; Jun Gao

2013-01-01

Flight delays and safety are the principal contradictions in the sound development of civil aviation. Flight delays often come up and induce civil aviation safety risk simultaneously. Based on flight delays, the random characteristics of civil aviation safety risk are analyzed. Flight delays have been deemed to a potential safety hazard. The change rules and characteristics of civil aviation safety risk based on flight delays have been analyzed. Bayesian networks (BN) have been used to build ...

A variational void coalescence model for ductile metals

KAUST Repository

Siddiq, Amir; Arciniega, Roman; El Sayed, Tamer

2011-01-01

We present a variational void coalescence model that includes all the essential ingredients of failure in ductile porous metals. The model is an extension of the variational void growth model by Weinberg et al. (Comput Mech 37:142-152, 2006

Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

Science.gov (United States)

2016-01-05

SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function

Intensity-based bayesian framework for image reconstruction from sparse projection data

International Nuclear Information System (INIS)

Rashed, E.A.; Kudo, Hiroyuki

2009-01-01

This paper presents a Bayesian framework for iterative image reconstruction from projection data measured over a limited number of views. The classical Nyquist sampling rule yields the minimum number of projection views required for accurate reconstruction. However, challenges exist in many medical and industrial imaging applications in which the projection data is undersampled. Classical analytical reconstruction methods such as filtered backprojection (FBP) are not a good choice for use in such cases because the data undersampling in the angular range introduces aliasing and streak artifacts that degrade lesion detectability. In this paper, we propose a Bayesian framework for maximum likelihood-expectation maximization (ML-EM)-based iterative reconstruction methods that incorporates a priori knowledge obtained from expected intensity information. The proposed framework is based on the fact that, in tomographic imaging, it is often possible to expect a set of intensity values of the reconstructed object with relatively high accuracy. The image reconstruction cost function is modified to include the l 1 norm distance to the a priori known information. The proposed method has the potential to regularize the solution to reduce artifacts without missing lesions that cannot be expected from the a priori information. Numerical studies showed a significant improvement in image quality and lesion detectability under the condition of highly undersampled projection data. (author)

GO-Bayes: Gene Ontology-based overrepresentation analysis using a Bayesian approach.

Science.gov (United States)

Zhang, Song; Cao, Jing; Kong, Y Megan; Scheuermann, Richard H

2010-04-01

A typical approach for the interpretation of high-throughput experiments, such as gene expression microarrays, is to produce groups of genes based on certain criteria (e.g. genes that are differentially expressed). To gain more mechanistic insights into the underlying biology, overrepresentation analysis (ORA) is often conducted to investigate whether gene sets associated with particular biological functions, for example, as represented by Gene Ontology (GO) annotations, are statistically overrepresented in the identified gene groups. However, the standard ORA, which is based on the hypergeometric test, analyzes each GO term in isolation and does not take into account the dependence structure of the GO-term hierarchy. We have developed a Bayesian approach (GO-Bayes) to measure overrepresentation of GO terms that incorporates the GO dependence structure by taking into account evidence not only from individual GO terms, but also from their related terms (i.e. parents, children, siblings, etc.). The Bayesian framework borrows information across related GO terms to strengthen the detection of overrepresentation signals. As a result, this method tends to identify sets of closely related GO terms rather than individual isolated GO terms. The advantage of the GO-Bayes approach is demonstrated with a simulation study and an application example.

Mitochondrial phylogeny of the Chrysisignita (Hymenoptera: Chrysididae) species group based on simultaneous Bayesian alignment and phylogeny reconstruction.

Science.gov (United States)

Soon, Villu; Saarma, Urmas

2011-07-01

The ignita species group within the genus Chrysis includes over 100 cuckoo wasp species, which all lead a parasitic lifestyle and exhibit very similar morphology. The lack of robust, diagnostic morphological characters has hindered phylogenetic reconstructions and contributed to frequent misidentification and inconsistent interpretations of species in this group. Therefore, molecular phylogenetic analysis is the most suitable approach for resolving the phylogeny and taxonomy of this group. We present a well-resolved phylogeny of the Chrysis ignita species group based on mitochondrial sequence data from 41 ingroup and six outgroup taxa. Although our emphasis was on European taxa, we included samples from most of the distribution range of the C. ignita species group to test for monophyly. We used a continuous mitochondrial DNA sequence consisting of 16S rRNA, tRNA(Val), 12S rRNA and ND4. The location of the ND4 gene at the 3' end of this continuous sequence, following 12S rRNA, represents a novel mitochondrial gene arrangement for insects. Due to difficulties in aligning rRNA genes, two different Bayesian approaches were employed to reconstruct phylogeny: (1) using a reduced data matrix including only those positions that could be aligned with confidence; or (2) using the full sequence dataset while estimating alignment and phylogeny simultaneously. In addition maximum-parsimony and maximum-likelihood analyses were performed to test the robustness of the Bayesian approaches. Although all approaches yielded trees with similar topology, considerably more nodes were resolved with analyses using the full data matrix. Phylogenetic analysis supported the monophyly of the C. ignita species group and divided its species into well-supported clades. The resultant phylogeny was only partly in accordance with published subgroupings based on morphology. Our results suggest that several taxa currently treated as subspecies or names treated as synonyms may in fact constitute

An introduction to Bayesian statistics in health psychology.

Science.gov (United States)

Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske

2017-09-01

The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

Coalescence of rotating black holes on Eguchi-Hanson space

International Nuclear Information System (INIS)

Matsuno, Ken; Ishihara, Hideki; Kimura, Masashi; Tomizawa, Shinya

2007-01-01

We obtain new charged rotating multi-black hole solutions on the Eguchi-Hanson space in the five-dimensional Einstein-Maxwell system with a Chern-Simons term and a positive cosmological constant. In the two-black holes case, these solutions describe the coalescence of two rotating black holes with the horizon topologies of S 3 into a single rotating black hole with the horizon topology of the lens space L(2;1)=S 3 /Z 2 . We discuss the differences in the horizon areas between our solutions and the two-centered Klemm-Sabra solutions which describe the coalescence of two rotating black holes with the horizon topologies of S 3 into a single rotating black hole with the horizon topology of S 3

Real-time prediction of acute cardiovascular events using hardware-implemented Bayesian networks.

Science.gov (United States)

Tylman, Wojciech; Waszyrowski, Tomasz; Napieralski, Andrzej; Kamiński, Marek; Trafidło, Tamara; Kulesza, Zbigniew; Kotas, Rafał; Marciniak, Paweł; Tomala, Radosław; Wenerski, Maciej

2016-02-01

This paper presents a decision support system that aims to estimate a patient׳s general condition and detect situations which pose an immediate danger to the patient׳s health or life. The use of this system might be especially important in places such as accident and emergency departments or admission wards, where a small medical team has to take care of many patients in various general conditions. Particular stress is laid on cardiovascular and pulmonary conditions, including those leading to sudden cardiac arrest. The proposed system is a stand-alone microprocessor-based device that works in conjunction with a standard vital signs monitor, which provides input signals such as temperature, blood pressure, pulseoxymetry, ECG, and ICG. The signals are preprocessed and analysed by a set of artificial intelligence algorithms, the core of which is based on Bayesian networks. The paper focuses on the construction and evaluation of the Bayesian network, both its structure and numerical specification. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bayesian Multi-Energy Computed Tomography reconstruction approaches based on decomposition models

International Nuclear Information System (INIS)

Cai, Caifang

2013-01-01

Multi-Energy Computed Tomography (MECT) makes it possible to get multiple fractions of basis materials without segmentation. In medical application, one is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical MECT measurements are usually obtained with polychromatic X-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam poly-chromaticity fail to estimate the correct decomposition fractions and result in Beam-Hardening Artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log pre-processing and the water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on non-linear forward models counting the beam poly-chromaticity show great potential for giving accurate fraction images.This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint Maximum A Posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a non-quadratic cost function. To solve it, the use of a monotone Conjugate Gradient (CG) algorithm with suboptimal descent steps is proposed.The performances of the proposed approach are analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also

Dislocation mediated alignment during metal nanoparticle coalescence

International Nuclear Information System (INIS)

Lange, A.P.; Samanta, A.; Majidi, H.; Mahajan, S.; Ging, J.; Olson, T.Y.; Benthem, K. van; Elhadj, S.

2016-01-01

Dislocation mediated alignment processes during gold nanoparticle coalescence were studied at low and high temperatures using molecular dynamics simulations and transmission electron microscopy. Particles underwent rigid body rotations immediately following attachment in both low temperature (500 K) simulated coalescence events and low temperature (∼315 K) transmission electron microscopy beam heating experiments. In many low temperature simulations, some degree of misorientation between particles remained after rigid body rotations, which was accommodated by grain boundary dislocation nodes. These dislocations were either sessile and remained at the interface for the duration of the simulation or dissociated and cross-slipped through the adjacent particles, leading to improved co-alignment. Minimal rigid body rotations were observed during or immediately following attachment in high temperature (1100 K) simulations, which is attributed to enhanced diffusion at the particles' interface. However, rotation was eventually induced by {111} slip on planes parallel to the neck groove. These deformation modes led to the formation of single and multi-fold twins whose structures depended on the initial orientation of the particles. The driving force for {111} slip is attributed to high surface stresses near the intersection of low energy {111} facets in the neck region. The details of this twinning process were examined in detail using simulated trajectories, and the results reveal possible mechanisms for the nucleation and propagation of Shockley partials on consecutive planes. Deformation twinning was also observed in-situ using transmission electron microscopy, which resulted in the co-alignment of a set of the particles' {111} planes across their grain boundary and an increase in their dihedral angle. This constitutes the first detailed experimental observation of deformation twinning during nanoparticle coalescence, validating simulation results presented here and

Séparation par coalescence en lit fixe d'une phase aqueuse émulsionnée dans une phase organique Fixed-Bed Coalescence to Separate an Emulsified Aqueous Phase in an Organic Phase

Directory of Open Access Journals (Sweden)

Calteau J. P.

2006-11-01

Full Text Available Cette publication étudie la séparation de l'eau émulsionnée dans un kérosène par passage à travers un milieu granulaire. Les propriétés physico-chimiques du matériau coalesceur (nature et état de surface sont considérées. L'efficacité de séparation est étudiée en fonction ; - de la mouillabilité; - de la granulométrie; - de l'état de surface du matériau ; - de la vitesse de passage de l'émulsion; - de la hauteur de lit. L'analyse qualitative et quantitative de la rétention de l'eau dans le lit permet de mieux comprendre les mécanismes de coalescence. This article examines the séparation of emulsified water in kerosene by passing it through a granular medium. The physico-chemical properties of the coolescing material, i. e. nature and surface state, are considered. Separation efficiency is analyzed as a fonction of : - wettability; - grain size ; - surface state of the material ; - emulsion pass-through rate; - height of the bed. A qualitative and quantitative analysis of water retention in the bed gives a better understanding of the coalescence mechanisms.

Bayesian Networks and Influence Diagrams

DEFF Research Database (Denmark)

Kjærulff, Uffe Bro; Madsen, Anders Læsø

 Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification......, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended...

A Bayesian-probability-based method for assigning protein backbone dihedral angles based on chemical shifts and local sequences

Energy Technology Data Exchange (ETDEWEB)

Wang Jun; Liu Haiyan [University of Science and Technology of China, Hefei National Laboratory for Physical Sciences at the Microscale, and Key Laboratory of Structural Biology, School of Life Sciences (China)], E-mail: hyliu@ustc.edu.cn

2007-01-15

Chemical shifts contain substantial information about protein local conformations. We present a method to assign individual protein backbone dihedral angles into specific regions on the Ramachandran map based on the amino acid sequences and the chemical shifts of backbone atoms of tripeptide segments. The method uses a scoring function derived from the Bayesian probability for the central residue of a query tripeptide segment to have a particular conformation. The Ramachandran map is partitioned into representative regions at two levels of resolution. The lower resolution partitioning is equivalent to the conventional definitions of different secondary structure regions on the map. At the higher resolution level, the {alpha} and {beta} regions are further divided into subregions. Predictions are attempted at both levels of resolution. We compared our method with TALOS using the original TALOS database, and obtained comparable results. Although TALOS may produce the best results with currently available databases which are much enlarged, the Bayesian-probability-based approach can provide a quantitative measure for the reliability of predictions.

Bayesian experts in exploring reaction kinetics of transcription circuits.

Science.gov (United States)

Yoshida, Ryo; Saito, Masaya M; Nagao, Hiromichi; Higuchi, Tomoyuki

2010-09-15

Biochemical reactions in cells are made of several types of biological circuits. In current systems biology, making differential equation (DE) models simulatable in silico has been an appealing, general approach to uncover a complex world of biochemical reaction dynamics. Despite of a need for simulation-aided studies, our research field has yet provided no clear answers: how to specify kinetic values in models that are difficult to measure from experimental/theoretical analyses on biochemical kinetics. We present a novel non-parametric Bayesian approach to this problem. The key idea lies in the development of a Dirichlet process (DP) prior distribution, called Bayesian experts, which reflects substantive knowledge on reaction mechanisms inherent in given models and experimentally observable kinetic evidences to the subsequent parameter search. The DP prior identifies significant local regions of unknown parameter space before proceeding to the posterior analyses. This article reports that a Bayesian expert-inducing stochastic search can effectively explore unknown parameters of in silico transcription circuits such that solutions of DEs reproduce transcriptomic time course profiles. A sample source code is available at the URL http://daweb.ism.ac.jp/~yoshidar/lisdas/.

Quality assurance of nuclear analytical techniques based on Bayesian characteristic limits

International Nuclear Information System (INIS)

Michel, R.

2000-01-01

Based on Bayesian statistics, characteristic limits such as decision threshold, detection limit and confidence limits can be calculated taking into account all sources of experimental uncertainties. This approach separates the complete evaluation of a measurement according to the ISO Guide to the Expression of Uncertainty in Measurement from the determination of the characteristic limits. Using the principle of maximum entropy the characteristic limits are determined from the complete standard uncertainty of the measurand. (author)

Statistics: a Bayesian perspective

National Research Council Canada - National Science Library

Berry, Donald A

1996-01-01

...: it is the only introductory textbook based on Bayesian ideas, it combines concepts and methods, it presents statistics as a means of integrating data into the significant process, it develops ideas...

Risk-based design of process systems using discrete-time Bayesian networks

International Nuclear Information System (INIS)

Khakzad, Nima; Khan, Faisal; Amyotte, Paul

2013-01-01

Temporal Bayesian networks have gained popularity as a robust technique to model dynamic systems in which the components' sequential dependency, as well as their functional dependency, cannot be ignored. In this regard, discrete-time Bayesian networks have been proposed as a viable alternative to solve dynamic fault trees without resort to Markov chains. This approach overcomes the drawbacks of Markov chains such as the state-space explosion and the error-prone conversion procedure from dynamic fault tree. It also benefits from the inherent advantages of Bayesian networks such as probability updating. However, effective mapping of the dynamic gates of dynamic fault trees into Bayesian networks while avoiding the consequent huge multi-dimensional probability tables has always been a matter of concern. In this paper, a new general formalism has been developed to model two important elements of dynamic fault tree, i.e., cold spare gate and sequential enforcing gate, with any arbitrary probability distribution functions. Also, an innovative Neutral Dependency algorithm has been introduced to model dynamic gates such as priority-AND gate, thus reducing the dimension of conditional probability tables by an order of magnitude. The second part of the paper is devoted to the application of discrete-time Bayesian networks in the risk assessment and safety analysis of complex process systems. It has been shown how dynamic techniques can effectively be applied for optimal allocation of safety systems to obtain maximum risk reduction.

Coalescing colony model: Mean-field, scaling, and geometry

Science.gov (United States)

Carra, Giulia; Mallick, Kirone; Barthelemy, Marc

2017-12-01

We analyze the coalescing model where a `primary' colony grows and randomly emits secondary colonies that spread and eventually coalesce with it. This model describes population proliferation in theoretical ecology, tumor growth, and is also of great interest for modeling urban sprawl. Assuming the primary colony to be always circular of radius r (t ) and the emission rate proportional to r (t) θ , where θ >0 , we derive the mean-field equations governing the dynamics of the primary colony, calculate the scaling exponents versus θ , and compare our results with numerical simulations. We then critically test the validity of the circular approximation for the colony shape and show that it is sound for a constant emission rate (θ =0 ). However, when the emission rate is proportional to the perimeter, the circular approximation breaks down and the roughness of the primary colony cannot be discarded, thus modifying the scaling exponents.

A Bayesian approach to particle identification in ALICE

Energy Technology Data Exchange (ETDEWEB)

Wilkinson, Jeremy [Physikalisches Institut, Ruprecht-Karls-Universitaet Heidelberg (Germany); Collaboration: ALICE-Collaboration

2016-07-01

Particle identification (PID) is one of the major strengths of the ALICE detector at the LHC, and provides essential insight into quark-gluon plasma formation in heavy-ion collisions. PID is most effective when complementary identification techniques (such as specific energy loss in the Time Projection Chamber, or flight times measured by the Time Of Flight detector) are combined, however with standard PID techniques it can be difficult to combine these signals, especially when detectors with non-Gaussian responses are used. Here, an alternative probabilistic PID approach based on Bayes' theorem will be presented. This method facilitates the combination of different detector technologies based on the combined probability of a particle type to produce the signals measured in various detectors. The Bayesian PID approach will be briefly outlined, and benchmark analyses will be presented for high-purity samples of pions, kaons, and protons, as well as for the two-pronged decay D{sup 0} → K{sup -}π{sup +}, comparing the performance of the standard PID approach with that of the Bayesian approach. Finally, prospects for measuring the Λ{sub c} baryon in the three-pronged decay channel Λ{sub c}{sup +} → pK{sup -}π{sup +} are presented.

Self-consistent Bayesian analysis of space-time symmetry studies

International Nuclear Information System (INIS)

Davis, E.D.

1996-01-01

We introduce a Bayesian method for the analysis of epithermal neutron transmission data on space-time symmetries in which unique assignment of the prior is achieved by maximisation of the cross entropy and the imposition of a self-consistency criterion. Unlike the maximum likelihood method used in previous analyses of parity-violation data, our method is freed of an ad hoc cutoff parameter. Monte Carlo studies indicate that our self-consistent Bayesian analysis is superior to the maximum likelihood method when applied to the small data samples typical of symmetry studies. (orig.)

Classifying emotion in Twitter using Bayesian network

Science.gov (United States)

Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

2018-03-01

Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

Phylogenomic analyses data of the avian phylogenomics project

DEFF Research Database (Denmark)

Jarvis, Erich D; Mirarab, Siavash; Aberer, Andre J

2015-01-01

BACKGROUND: Determining the evolutionary relationships among the major lineages of extant birds has been one of the biggest challenges in systematic biology. To address this challenge, we assembled or collected the genomes of 48 avian species spanning most orders of birds, including all Neognathae...... and two of the five Palaeognathae orders. We used these genomes to construct a genome-scale avian phylogenetic tree and perform comparative genomic analyses. FINDINGS: Here we present the datasets associated with the phylogenomic analyses, which include sequence alignment files consisting of nucleotides......ML algorithm or when using statistical binning with the coalescence-based MP-EST algorithm (which we refer to as MP-EST*). Other data sets, such as the coding sequence of some exons, revealed other properties of genome evolution, namely convergence. CONCLUSIONS: The Avian Phylogenomics Project is the largest...

Structure-based bayesian sparse reconstruction

KAUST Repository

Quadeer, Ahmed Abdul

2012-12-01

Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.

Bayesian estimates of linkage disequilibrium

Directory of Open Access Journals (Sweden)

Abad-Grau María M

2007-06-01

Full Text Available Abstract Background The maximum likelihood estimator of D' – a standard measure of linkage disequilibrium – is biased toward disequilibrium, and the bias is particularly evident in small samples and rare haplotypes. Results This paper proposes a Bayesian estimation of D' to address this problem. The reduction of the bias is achieved by using a prior distribution on the pair-wise associations between single nucleotide polymorphisms (SNPs that increases the likelihood of equilibrium with increasing physical distances between pairs of SNPs. We show how to compute the Bayesian estimate using a stochastic estimation based on MCMC methods, and also propose a numerical approximation to the Bayesian estimates that can be used to estimate patterns of LD in large datasets of SNPs. Conclusion Our Bayesian estimator of D' corrects the bias toward disequilibrium that affects the maximum likelihood estimator. A consequence of this feature is a more objective view about the extent of linkage disequilibrium in the human genome, and a more realistic number of tagging SNPs to fully exploit the power of genome wide association studies.

A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL.

Science.gov (United States)

Brown, Stephen; Hutton, Brian; Clifford, Tammy; Coyle, Doug; Grima, Daniel; Wells, George; Cameron, Chris

2014-09-29

The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent

The dynamics of slug trains in volcanic conduits: Evidence for expansion driven slug coalescence

Science.gov (United States)

Pering, T. D.; McGonigle, A. J. S.; James, M. R.; Capponi, A.; Lane, S. J.; Tamburello, G.; Aiuppa, A.

2017-12-01

Strombolian volcanism is a ubiquitous form of activity, driven by the ascent and bursting of bubbles of slug morphology. Whilst considerable attention has been devoted to understanding the behaviour of individual slugs in this regime, relatively little is known about how inter-slug interactions modify flow conditions. Recently, we reported on high temporal frequency strombolian activity on Etna, in which the larger erupted slug masses were followed by longer intervals before the following explosion than the smaller bursts (Pering et al., 2015). We hypothesised that this behaviour arose from the coalescence of ascending slugs causing a prolonged lag before arrival of the next distinct bubble. Here we consider the potential importance of inter-slug interactions for the dynamics of strombolian volcanism, by reporting on the first study into the behaviour of trains of ascending gas slugs, scaled to the expansion rates in volcanic conduits. This laboratory analogue study illustrates that slugs in trains rise faster than individual slugs, and can be associated with aspects of co-current flow. The work also highlights that coalescence and inter-slug interactions play an important role in modulating slug train behaviour. We also report, for the first time, on slug coalescence driven by vertical expansion of the trailing slug, a process which can occur, even where the leading slug base ascent velocity is greater than that of the trailing slug.

Non-homogeneous dynamic Bayesian networks for continuous data

NARCIS (Netherlands)

Grzegorczyk, Marco; Husmeier, Dirk

Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with

Evidence of a major gene from Bayesian segregation analyses of liability to osteochondral diseases in pigs.

Science.gov (United States)

Kadarmideen, Haja N; Janss, Luc L G

2005-11-01

Bayesian segregation analyses were used to investigate the mode of inheritance of osteochondral lesions (osteochondrosis, OC) in pigs. Data consisted of 1163 animals with OC and their pedigrees included 2891 animals. Mixed-inheritance threshold models (MITM) and several variants of MITM, in conjunction with Markov chain Monte Carlo methods, were developed for the analysis of these (categorical) data. Results showed major genes with significant and substantially higher variances (range 1.384-37.81), compared to the polygenic variance (sigmau2). Consequently, heritabilities for a mixed inheritance (range 0.65-0.90) were much higher than the heritabilities from the polygenes. Disease allele frequencies range was 0.38-0.88. Additional analyses estimating the transmission probabilities of the major gene showed clear evidence for Mendelian segregation of a major gene affecting osteochondrosis. The variants, MITM with informative prior on sigmau2, showed significant improvement in marginal distributions and accuracy of parameters. MITM with a "reduced polygenic model" for parameterization of polygenic effects avoided convergence problems and poor mixing encountered in an "individual polygenic model." In all cases, "shrinkage estimators" for fixed effects avoided unidentifiability for these parameters. The mixed-inheritance linear model (MILM) was also applied to all OC lesions and compared with the MITM. This is the first study to report evidence of major genes for osteochondral lesions in pigs; these results may also form a basis for underpinning the genetic inheritance of this disease in other animals as well as in humans.

AGGLOMERATIVE CLUSTERING OF SOUND RECORD SPEECH SEGMENTS BASED ON BAYESIAN INFORMATION CRITERION

Directory of Open Access Journals (Sweden)

O. Yu. Kydashev

2013-01-01

Full Text Available This paper presents the detailed description of agglomerative clustering system implementation for speech segments based on Bayesian information criterion. Numerical experiment results with different acoustic features, as well as the full and diagonal covariance matrices application are given. The error rate DER equal to 6.4% for audio records of radio «Svoboda» was achieved by means of designed system.

Bayesian biostatistics

CERN Document Server

Lesaffre, Emmanuel

2012-01-01

The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

Science.gov (United States)

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-08

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

Directory of Open Access Journals (Sweden)

Ke Li

2016-01-01

Full Text Available A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF and Diagnostic Bayesian Network (DBN is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO. To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA is proposed to evaluate the sensitiveness of symptom parameters (SPs for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

Science.gov (United States)

Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

2016-01-01

A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

A bayesian approach to classification criteria for spectacled eiders

Science.gov (United States)

Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

1996-01-01

To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

Bayesian data analysis for newcomers.

Science.gov (United States)

Kruschke, John K; Liddell, Torrin M

2018-02-01

This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

Science.gov (United States)

Levy, Roy

2014-01-01

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

Light particle probes of expansion and temperature evolution: Coalescence model analyses of heavy ion collisions at 47A MeV

International Nuclear Information System (INIS)

Hagel, K.; Wada, R.; Cibor, J.; Lunardon, M.; Marie, N.; Alfaro, R.; Shen, W.; Xiao, B.; Zhao, Y.; Majka, Z.

2000-01-01

The reactions 12 C+ 116 Sn, 22 Ne+Ag, 40 Ar+ 100 Mo, and 64 Zn+ 89 Y have been studied at 47A MeV projectile energy. For these reactions the most violent collisions lead to increasing amounts of fragment and light particle emission as the projectile mass increases. This is consistent with quantum molecular dynamics (QMD) model simulations of the collisions. Moving source fits to the light charged particle data have been used to gain a global view of the evolution of the particle emission. Comparisons of the multiplicities and spectra of light charged particles emitted in the reactions with the four different projectiles indicate a common emission mechanism for early emitted ejectiles even though the deposited excitation energies differ greatly. The spectra for such ejectiles can be characterized as emission in the nucleon-nucleon frame. Evidence that the 3 He yield is dominated by this type of emission and the role of the collision dynamics in determining the 3 H/ 3 He yield ratio are discussed. Self-consistent coalescence model analyses are applied to the light cluster yields, in an attempt to probe emitter source sizes and to follow the evolution of the temperatures and densities from the time of first particle emission to equilibration. These analyses exploit correlations between ejectile energy and emission time, suggested by the QMD calculations. In this analysis the degree of expansion of the emitting system is found to increase with increasing projectile mass. The double isotope yield ratio temperature drops as the system expands. Average densities as low as 0.36ρ 0 are reached at a time near 100 fm/c after contact. Calorimetric methods were used to derive the mass and excitation energy of the excited nuclei which are present after preequilibrium emission. The derived masses range from 102 to 116 u and the derived excitation energies increase from 2.6 to 6.9 MeV/nucleon with increasing projectile mass. A caloric curve is derived for these expanded A∼110

First- and Second-level Bayesian Inference of Flow Resistivity of Sound Absorber and Room’s Influence

DEFF Research Database (Denmark)

Choi, Sang-Hyeon; Lee, Ikjin; Jeong, Cheol-Ho

2016-01-01

Sabine absorption coefficient is a widely used one deduced from reverberation time measurements via the Sabine equation. First- and second-level Bayesian analysis are used to estimate the flow resistivity of a sound absorber and the influences of the test chambers from Sabine absorption...... coefficients measured in 13 different reverberation chambers. The first-level Bayesian analysis is more general than the second-level Bayesian analysis. Sharper posterior distribution can be acquired by the second-level Bayesian analysis than the one by the first-level Bayesian analysis because more data...... are used to set more reliable prior distribution. The estimated room’s influences by the first- and the second-level Bayesian analyses are similar to the estimated results by the mean absolute error minimization....

Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data.

Science.gov (United States)

Vemulapalli, Vijetha; Qu, Jiaqi; Garren, Jeonifer M; Rodrigues, Leonardo O; Kiebish, Michael A; Sarangarajan, Rangaprasad; Narain, Niven R; Akmaev, Viatcheslav R

2016-11-01

Given the availability of extensive digitized healthcare data from medical records, claims and prescription information, it is now possible to use hypothesis-free, data-driven approaches to mine medical databases for novel insight. The goal of this analysis was to demonstrate the use of artificial intelligence based methods such as Bayesian networks to open up opportunities for creation of new knowledge in management of chronic conditions. Hospital level Medicare claims data containing discharge numbers for most common diagnoses were analyzed in a hypothesis-free manner using Bayesian networks learning methodology. While many interactions identified between discharge rates of diagnoses using this data set are supported by current medical knowledge, a novel interaction linking asthma and renal failure was discovered. This interaction is non-obvious and had not been looked at by the research and clinical communities in epidemiological or clinical data. A plausible pharmacological explanation of this link is proposed together with a verification of the risk significance by conventional statistical analysis. Potential clinical and molecular pathways defining the relationship between commonly used asthma medications and renal disease are discussed. The study underscores the need for further epidemiological research to validate this novel hypothesis. Validation will lead to advancement in clinical treatment of asthma & bronchitis, thereby, improving patient outcomes and leading to long term cost savings. In summary, this study demonstrates that application of advanced artificial intelligence methods in healthcare has the potential to enhance the quality of care by discovering non-obvious, clinically relevant relationships and enabling timely care intervention. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Second harmonic electromagnetic emission via Langmuir wave coalescence

International Nuclear Information System (INIS)

Willes, A.J.; Robinson, P.A.; Melrose, D.B.

1996-01-01

The coalescence of Langmuir waves to produce electromagnetic waves at twice the plasma frequency is considered. A simplified expression for the rate of production of second harmonic electromagnetic waves is obtained for a broad class of Langmuir spectra. In addition, two different analytic approximations are considered. The validity of the commonly used head-on approximation is explored, in which the two coalescing Langmuir waves are assumed to approach from opposite directions. This approximation breaks down at low Langmuir wavenumbers, and for narrow Langmuir wave spectra. A second, more general, approximation is introduced, called the narrow-spectrum approximation, which requires narrow spectral widths of the Langmuir spectra. The advantages of this approximation are that it does not break down at low Langmuir wavenumbers, and that it remains valid for relatively broad Langmuir wave spectra. Finally, the applicability of these approximations in treating harmonic radiation in type III solar radio bursts is discussed. copyright 1996 American Institute of Physics

Genome-wide DNA polymorphism analyses using VariScan

Directory of Open Access Journals (Sweden)

Vilella Albert J

2006-09-01

Full Text Available Abstract Background DNA sequence polymorphisms analysis can provide valuable information on the evolutionary forces shaping nucleotide variation, and provides an insight into the functional significance of genomic regions. The recent ongoing genome projects will radically improve our capabilities to detect specific genomic regions shaped by natural selection. Current available methods and software, however, are unsatisfactory for such genome-wide analysis. Results We have developed methods for the analysis of DNA sequence polymorphisms at the genome-wide scale. These methods, which have been tested on a coalescent-simulated and actual data files from mouse and human, have been implemented in the VariScan software package version 2.0. Additionally, we have also incorporated a graphical-user interface. The main features of this software are: i exhaustive population-genetic analyses including those based on the coalescent theory; ii analysis adapted to the shallow data generated by the high-throughput genome projects; iii use of genome annotations to conduct a comprehensive analyses separately for different functional regions; iv identification of relevant genomic regions by the sliding-window and wavelet-multiresolution approaches; v visualization of the results integrated with current genome annotations in commonly available genome browsers. Conclusion VariScan is a powerful and flexible suite of software for the analysis of DNA polymorphisms. The current version implements new algorithms, methods, and capabilities, providing an important tool for an exhaustive exploratory analysis of genome-wide DNA polymorphism data.

Characteristic dynamics near two coalescing eigenvalues incorporating continuum threshold effects

Science.gov (United States)

Garmon, Savannah; Ordonez, Gonzalo

2017-06-01

It has been reported in the literature that the survival probability P(t) near an exceptional point where two eigenstates coalesce should generally exhibit an evolution P (t ) ˜t2e-Γ t, in which Γ is the decay rate of the coalesced eigenstate; this has been verified in a microwave billiard experiment [B. Dietz et al., Phys. Rev. E 75, 027201 (2007)]. However, the heuristic effective Hamiltonian that is usually employed to obtain this result ignores the possible influence of the continuum threshold on the dynamics. By contrast, in this work we employ an analytical approach starting from the microscopic Hamiltonian representing two simple models in order to show that the continuum threshold has a strong influence on the dynamics near exceptional points in a variety of circumstances. To report our results, we divide the exceptional points in Hermitian open quantum systems into two cases: at an EP2A two virtual bound states coalesce before forming a resonance, anti-resonance pair with complex conjugate eigenvalues, while at an EP2B two resonances coalesce before forming two different resonances. For the EP2B, which is the case studied in the microwave billiard experiment, we verify that the survival probability exhibits the previously reported modified exponential decay on intermediate time scales, but this is replaced with an inverse power law on very long time scales. Meanwhile, for the EP2A the influence from the continuum threshold is so strong that the evolution is non-exponential on all time scales and the heuristic approach fails completely. When the EP2A appears very near the threshold, we obtain the novel evolution P (t ) ˜1 -C1√{t } on intermediate time scales, while further away the parabolic decay (Zeno dynamics) on short time scales is enhanced.

How to practise Bayesian statistics outside the Bayesian church: What philosophy for Bayesian statistical modelling?

NARCIS (Netherlands)

Borsboom, D.; Haig, B.D.

2013-01-01

Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science

A Bayesian alternative for multi-objective ecohydrological model specification

Science.gov (United States)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

An empirical Bayesian approach for model-based inference of cellular signaling networks

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Klinke David J

2009-11-01

Full Text Available Abstract Background A common challenge in systems biology is to infer mechanistic descriptions of biological process given limited observations of a biological system. Mathematical models are frequently used to represent a belief about the causal relationships among proteins within a signaling network. Bayesian methods provide an attractive framework for inferring the validity of those beliefs in the context of the available data. However, efficient sampling of high-dimensional parameter space and appropriate convergence criteria provide barriers for implementing an empirical Bayesian approach. The objective of this study was to apply an Adaptive Markov chain Monte Carlo technique to a typical study of cellular signaling pathways. Results As an illustrative example, a kinetic model for the early signaling events associated with the epidermal growth factor (EGF signaling network was calibrated against dynamic measurements observed in primary rat hepatocytes. A convergence criterion, based upon the Gelman-Rubin potential scale reduction factor, was applied to the model predictions. The posterior distributions of the parameters exhibited complicated structure, including significant covariance between specific parameters and a broad range of variance among the parameters. The model predictions, in contrast, were narrowly distributed and were used to identify areas of agreement among a collection of experimental studies. Conclusion In summary, an empirical Bayesian approach was developed for inferring the confidence that one can place in a particular model that describes signal transduction mechanisms and for inferring inconsistencies in experimental measurements.

Bayesian non- and semi-parametric methods and applications

CERN Document Server

Rossi, Peter

2014-01-01

This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number

Bayesian Probability Theory

Science.gov (United States)

von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo

2014-06-01

Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.

An investigation of bubble coalescence and post-rupture oscillation in non-ionic surfactant solutions using high-speed cinematography.

Science.gov (United States)

Bournival, G; Ata, S; Karakashev, S I; Jameson, G J

2014-01-15

Most processes involving bubbling in a liquid require small bubbles to maximise mass/energy transfer. A common method to prevent bubbles from coalescing is by the addition of surfactants. In order to get an insight into the coalescence process, capillary bubbles were observed using a high speed cinematography. Experiments were performed in solutions of 1-pentanol, 4-methyl-2-pentanol, tri(propylene glycol) methyl ether, and poly(propylene glycol) for which information such as the coalescence time and the deformation of the resultant bubble upon coalescence was extracted. It is shown in this study that the coalescence time increases with surfactant concentration until the appearance of a plateau. The increase in coalescence time with surfactant concentration could not be attributed only to surface elasticity. The oscillation of the resultant bubble was characterised by the damping of the oscillation. The results suggested that a minimum elasticity is required to achieve an increased damping and considerable diffusion has a detrimental effect on the dynamic response of the bubble, thereby reducing the damping. Copyright © 2013 Elsevier Inc. All rights reserved.

The occurrence of in-mouth coalescence of emulsion droplets in relation to perception of fat

NARCIS (Netherlands)

Dresselhuis, D.M.; Hoog, de E.H.A.; Cohen Stuart, M.A.; Vingerhoeds, M.H.; Aken, van G.A.

2008-01-01

We studied the relation between sensitivity of emulsions for in-mouth coalescence and perception of fat-related attributes, such as creaminess as well as the relation with in vivo perceived and ex vivo measured friction. Emulsions with varying expected sensitivity towards in-mouth coalescence were

Nickel, copper and cobalt coalescence in copper cliff converter slag

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Wolf A.

2016-01-01

Full Text Available The aim of this investigation is to assess the effect of various additives on coalescence of nickel, copper and cobalt from slags generated during nickel extraction. The analyzed fluxes were silica and lime while examined reductants were pig iron, ferrosilicon and copper-silicon compound. Slag was settled at the different holding temperatures for various times in conditions that simulated the industrial environment. The newly formed matte and slag were characterized by their chemical composition and morphology. Silica flux generated higher partition coefficients for nickel and copper than the addition of lime. Additives used as reducing agents had higher valuable metal recovery rates and corresponding partition coefficients than fluxes. Microstructural studies showed that slag formed after adding reductants consisted of primarily fayalite, with some minute traces of magnetite as the secondary phase. Addition of 5 wt% of pig iron, ferrosilicon and copper-silicon alloys favored the formation of a metallized matte which increased Cu, Ni and Co recoveries. Addition of copper-silicon alloys with low silicon content was efficient in copper recovery but coalescence of the other metals was low. Slag treated with the ferrosilicon facilitated the highest cobalt recovery while copper-silicon alloys with silicon content above 10 wt% resulted in high coalescence of nickel and copper, 87 % and 72 % respectively.

Statistical Bayesian method for reliability evaluation based on ADT data

Science.gov (United States)

Lu, Dawei; Wang, Lizhi; Sun, Yusheng; Wang, Xiaohong

2018-05-01

Accelerated degradation testing (ADT) is frequently conducted in the laboratory to predict the products’ reliability under normal operating conditions. Two kinds of methods, degradation path models and stochastic process models, are utilized to analyze degradation data and the latter one is the most popular method. However, some limitations like imprecise solution process and estimation result of degradation ratio still exist, which may affect the accuracy of the acceleration model and the extrapolation value. Moreover, the conducted solution of this problem, Bayesian method, lose key information when unifying the degradation data. In this paper, a new data processing and parameter inference method based on Bayesian method is proposed to handle degradation data and solve the problems above. First, Wiener process and acceleration model is chosen; Second, the initial values of degradation model and parameters of prior and posterior distribution under each level is calculated with updating and iteration of estimation values; Third, the lifetime and reliability values are estimated on the basis of the estimation parameters; Finally, a case study is provided to demonstrate the validity of the proposed method. The results illustrate that the proposed method is quite effective and accuracy in estimating the lifetime and reliability of a product.

Bayesian grid matching

DEFF Research Database (Denmark)

Hartelius, Karsten; Carstensen, Jens Michael

2003-01-01

A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which r...

A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression.

Science.gov (United States)

Liu, Fang; Eugenio, Evercita C

2018-04-01

Beta regression is an increasingly popular statistical technique in medical research for modeling of outcomes that assume values in (0, 1), such as proportions and patient reported outcomes. When outcomes take values in the intervals [0,1), (0,1], or [0,1], zero-or-one-inflated beta (zoib) regression can be used. We provide a thorough review on beta regression and zoib regression in the modeling, inferential, and computational aspects via the likelihood-based and Bayesian approaches. We demonstrate the statistical and practical importance of correctly modeling the inflation at zero/one rather than ad hoc replacing them with values close to zero/one via simulation studies; the latter approach can lead to biased estimates and invalid inferences. We show via simulation studies that the likelihood-based approach is computationally faster in general than MCMC algorithms used in the Bayesian inferences, but runs the risk of non-convergence, large biases, and sensitivity to starting values in the optimization algorithm especially with clustered/correlated data, data with sparse inflation at zero and one, and data that warrant regularization of the likelihood. The disadvantages of the regular likelihood-based approach make the Bayesian approach an attractive alternative in these cases. Software packages and tools for fitting beta and zoib regressions in both the likelihood-based and Bayesian frameworks are also reviewed.

Complexity analysis of accelerated MCMC methods for Bayesian inversion

International Nuclear Information System (INIS)

Hoang, Viet Ha; Schwab, Christoph; Stuart, Andrew M

2013-01-01

The Bayesian approach to inverse problems, in which the posterior probability distribution on an unknown field is sampled for the purposes of computing posterior expectations of quantities of interest, is starting to become computationally feasible for partial differential equation (PDE) inverse problems. Balancing the sources of error arising from finite-dimensional approximation of the unknown field, the PDE forward solution map and the sampling of the probability space under the posterior distribution are essential for the design of efficient computational Bayesian methods for PDE inverse problems. We study Bayesian inversion for a model elliptic PDE with an unknown diffusion coefficient. We provide complexity analyses of several Markov chain Monte Carlo (MCMC) methods for the efficient numerical evaluation of expectations under the Bayesian posterior distribution, given data δ. Particular attention is given to bounds on the overall work required to achieve a prescribed error level ε. Specifically, we first bound the computational complexity of ‘plain’ MCMC, based on combining MCMC sampling with linear complexity multi-level solvers for elliptic PDE. Our (new) work versus accuracy bounds show that the complexity of this approach can be quite prohibitive. Two strategies for reducing the computational complexity are then proposed and analyzed: first, a sparse, parametric and deterministic generalized polynomial chaos (gpc) ‘surrogate’ representation of the forward response map of the PDE over the entire parameter space, and, second, a novel multi-level Markov chain Monte Carlo strategy which utilizes sampling from a multi-level discretization of the posterior and the forward PDE. For both of these strategies, we derive asymptotic bounds on work versus accuracy, and hence asymptotic bounds on the computational complexity of the algorithms. In particular, we provide sufficient conditions on the regularity of the unknown coefficients of the PDE and on the

Linking Bayesian and agent-based models to simulate complex social-ecological systems in semi-arid regions

Directory of Open Access Journals (Sweden)

Aloah J Pope

2015-08-01

Full Text Available Interdependencies of ecologic, hydrologic, and social systems challenge traditional approaches to natural resource management in semi-arid regions. As a complex social-ecological system, water demands in the Sonoran Desert from agricultural and urban users often conflicts with water needs for its ecologically-significant riparian corridors. To explore this system, we developed an agent-based model to simulate complex feedbacks between human decisions and environmental conditions in the Rio Sonora Watershed. Cognitive mapping in conjunction with stakeholder participation produced a Bayesian model of conditional probabilities of local human decision-making processes resulting to changes in water demand. Probabilities created in the Bayesian model were incorporated into the agent-based model, so that each agent had a unique probability to make a positive decision based on its perceived environment at each point in time and space. By using a Bayesian approach, uncertainty in the human decision-making process could be incorporated. The spatially-explicit agent-based model simulated changes in depth-to-groundwater by well pumping based on an agent’s water demand. Changes in depth-to-groundwater feedback to influence agent behavior, as well as determine unique vegetation classes within the riparian corridor. Each vegetation class then provides varying stakeholder-defined quality values of ecosystem services. Using this modeling approach allowed us to examine effects on both the ecological and social system of semi-arid riparian corridors under various scenarios. The insight provided by the model contributes to understanding how specific interventions may alter the complex social-ecological system in the future.

Model-based dispersive wave processing: A recursive Bayesian solution

International Nuclear Information System (INIS)

Candy, J.V.; Chambers, D.H.

1999-01-01

Wave propagation through dispersive media represents a significant problem in many acoustic applications, especially in ocean acoustics, seismology, and nondestructive evaluation. In this paper we propose a propagation model that can easily represent many classes of dispersive waves and proceed to develop the model-based solution to the wave processing problem. It is shown that the underlying wave system is nonlinear and time-variable requiring a recursive processor. Thus the general solution to the model-based dispersive wave enhancement problem is developed using a Bayesian maximum a posteriori (MAP) approach and shown to lead to the recursive, nonlinear extended Kalman filter (EKF) processor. The problem of internal wave estimation is cast within this framework. The specific processor is developed and applied to data synthesized by a sophisticated simulator demonstrating the feasibility of this approach. copyright 1999 Acoustical Society of America.

AIC, BIC, Bayesian evidence against the interacting dark energy model

International Nuclear Information System (INIS)

Szydlowski, Marek; Krawiec, Adam; Kurek, Aleksandra; Kamionka, Michal

2015-01-01

Recent astronomical observations have indicated that the Universe is in a phase of accelerated expansion. While there are many cosmological models which try to explain this phenomenon, we focus on the interacting ΛCDM model where an interaction between the dark energy and dark matter sectors takes place. This model is compared to its simpler alternative - the ΛCDM model. To choose between these models the likelihood ratio test was applied as well as the model comparison methods (employing Occam's principle): the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the Bayesian evidence. Using the current astronomical data: type Ia supernova (Union2.1), h(z), baryon acoustic oscillation, the Alcock- Paczynski test, and the cosmic microwave background data, we evaluated both models. The analyses based on the AIC indicated that there is less support for the interacting ΛCDM model when compared to the ΛCDM model, while those based on the BIC indicated that there is strong evidence against it in favor of the ΛCDM model. Given the weak or almost non-existing support for the interacting ΛCDM model and bearing in mind Occam's razor we are inclined to reject this model. (orig.)

Arrested of coalescence of emulsion droplets of arbitrary size

Science.gov (United States)

Mbanga, Badel L.; Burke, Christopher; Blair, Donald W.; Atherton, Timothy J.

2013-03-01

With applications ranging from food products to cosmetics via targeted drug delivery systems, structured anisotropic colloids provide an efficient way to control the structure, properties and functions of emulsions. When two fluid emulsion droplets are brought in contact, a reduction of the interfacial tension drives their coalescence into a larger droplet of the same total volume and reduced exposed area. This coalescence can be partially or totally hindered by the presence of nano or micron-size particles that coat the interface as in Pickering emulsions. We investigate numerically the dependance of the mechanical stability of these arrested shapes on the particles size, their shape anisotropy, their polydispersity, their interaction with the solvent, and the particle-particle interactions. We discuss structural shape changes that can be induced by tuning the particles interactions after arrest occurs, and provide design parameters for the relevant experiments.

A modified GO-FLOW methodology with common cause failure based on Discrete Time Bayesian Network

International Nuclear Information System (INIS)

Fan, Dongming; Wang, Zili; Liu, Linlin; Ren, Yi

2016-01-01

Highlights: • Identification of particular causes of failure for common cause failure analysis. • Comparison two formalisms (GO-FLOW and Discrete Time Bayesian network) and establish the correlation between them. • Mapping the GO-FLOW model into Bayesian network model. • Calculated GO-FLOW model with common cause failures based on DTBN. - Abstract: The GO-FLOW methodology is a success-oriented system reliability modelling technique for multi-phase missions involving complex time-dependent, multi-state and common cause failure (CCF) features. However, the analysis algorithm cannot easily handle the multiple shared signals and CCFs. In addition, the simulative algorithm is time consuming when vast multi-state components exist in the model, and the multiple time points of phased mission problems increases the difficulty of the analysis method. In this paper, the Discrete Time Bayesian Network (DTBN) and the GO-FLOW methodology are integrated by the unified mapping rules. Based on these rules, the multi operators can be mapped into DTBN followed by, a complete GO-FLOW model with complex characteristics (e.g. phased mission, multi-state, and CCF) can be converted to the isomorphic DTBN and easily analyzed by utilizing the DTBN. With mature algorithms and tools, the multi-phase mission reliability parameter can be efficiently obtained via the proposed approach without considering the shared signals and the various complex logic operation. Meanwhile, CCF can also arise in the computing process.

Asymptotic Properties of the Number of Matching Coalescent Histories for Caterpillar-Like Families of Species Trees.

Science.gov (United States)

Disanto, Filippo; Rosenberg, Noah A

2016-01-01

Coalescent histories provide lists of species tree branches on which gene tree coalescences can take place, and their enumerative properties assist in understanding the computational complexity of calculations central in the study of gene trees and species trees. Here, we solve an enumerative problem left open by Rosenberg (IEEE/ACM Transactions on Computational Biology and Bioinformatics 10: 1253-1262, 2013) concerning the number of coalescent histories for gene trees and species trees with a matching labeled topology that belongs to a generic caterpillar-like family. By bringing a generating function approach to the study of coalescent histories, we prove that for any caterpillar-like family with seed tree t , the sequence (h n ) n ≥ 0 describing the number of matching coalescent histories of the n th tree of the family grows asymptotically as a constant multiple of the Catalan numbers. Thus, h n  ∼ β t c n , where the asymptotic constant β t > 0 depends on the shape of the seed tree t. The result extends a claim demonstrated only for seed trees with at most eight taxa to arbitrary seed trees, expanding the set of cases for which detailed enumerative properties of coalescent histories can be determined. We introduce a procedure that computes from t the constant β t as well as the algebraic expression for the generating function of the sequence (h n ) n ≥ 0 .

Bayesian methods for data analysis

CERN Document Server

Carlin, Bradley P.

2009-01-01

Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models The Bayes approach Introduction Prior Distributions Bayesian Inference Hierarchical Modeling Model Assessment Nonparametric Methods Bayesian computation Introduction Asymptotic Methods Noniterative Monte Carlo Methods Markov Chain Monte Carlo Methods Model criticism and selection Bayesian Modeling Bayesian Robustness Model Assessment Bayes Factors via Marginal Density Estimation Bayes Factors

Impact of 50% Synthesized Iso-Paraffins (SIP) on Middle Distillate Fuel Filtration and Coalescence

Science.gov (United States)

2014-10-30

Paraffins DEFINITIONS Coalescence - the ability to shed water Conventional Material Source - crude oil , natural gas liquid condensates...Impact of 50% Synthesized Iso-Paraffins (SIP) on Middle Distillate Fuel Filtration and Coalescence NF&LCFT REPORT 441/15-003 30 October 2014...heavy oil , shale oil , and oil sands Effluent - stream leaving a system Influent - stream entering a system Turnover - time required to flow the

Theory of peak coalescence in Fourier transform ion cyclotron resonance mass spectrometry.

Science.gov (United States)

Boldin, Ivan A; Nikolaev, Eugene N

2009-10-01

Peak coalescence, i.e. the merging of two close peaks in a Fourier transform ion cyclotron resonance (FTICR) mass spectrum at a high number of ions, plays an important role in various FTICR experiments. In order to describe the coalescence phenomenon we would like to propose a new theory of motion for ion clouds with close mass-to-charge ratios, driven by a uniform magnetic field and Coulomb interactions between the clouds. We describe the motion of the ion clouds in terms of their averaged drift motion in crossed magnetic and electric fields. The ion clouds are considered to be of constant size and their motion is studied in two dimensions. The theory deals with the first-order approximation of the equations of motion in relation to dm/m, where dm is the mass difference and m is the mass of a single ion. The analysis was done for an arbitrary inter-cloud interaction potential, which makes it possible to analyze finite-size ion clouds of any shape. The final analytical expression for the condition of the onset of coalescence is found for the case of uniformly charged spheres. An algorithm for finding this condition for an arbitrary interaction potential is proposed. The critical number of ions for the peak coalescence to take place is shown to depend quadratically on the magnetic field strength and to be proportional to the cyclotron radius and inversely proportional to the ion masses. Copyright (c) 2009 John Wiley & Sons, Ltd.

Theoretical Coalescence: A Method to Develop Qualitative Theory: The Example of Enduring.

Science.gov (United States)

Morse, Janice M

Qualitative research is frequently context bound, lacks generalizability, and is limited in scope. The purpose of this article was to describe a method, theoretical coalescence, that provides a strategy for analyzing complex, high-level concepts and for developing generalizable theory. Theoretical coalescence is a method of theoretical expansion, inductive inquiry, of theory development, that uses data (rather than themes, categories, and published extracts of data) as the primary source for analysis. Here, using the development of the lay concept of enduring as an example, I explore the scientific development of the concept in multiple settings over many projects and link it within the Praxis Theory of Suffering. As comprehension emerges when conducting theoretical coalescence, it is essential that raw data from various different situations be available for reinterpretation/reanalysis and comparison to identify the essential features of the concept. The concept is then reconstructed, with additional inquiry that builds description, and evidence is conducted and conceptualized to create a more expansive concept and theory. By utilizing apparently diverse data sets from different contexts that are linked by certain characteristics, the essential features of the concept emerge. Such inquiry is divergent and less bound by context yet purposeful, logical, and with significant pragmatic implications for practice in nursing and beyond our discipline. Theoretical coalescence is a means by which qualitative inquiry is broadened to make an impact, to accommodate new theoretical shifts and concepts, and to make qualitative research applied and accessible in new ways.

Evaluation of plugging criteria on steam generator tubes and coalescence model of collinear axial through-wall cracks

International Nuclear Information System (INIS)

Lee, Jin Ho; Park, Youn Won; Song, Myung Ho; Kim, Young Jin; Moon, Seong In

2000-01-01

In a nuclear power plant, steam generator tubes cover a major portion of the primary pressure-retaining boundary. Thus very conservative approaches have been taken in the light of steam generator tube integrity. According to the present criteria, tubes wall-thinned in excess 40% should be plugged whatever causes are. However, many analytical and experimental results have shown that no safety problems exist even with thickness reductions greater than 40%. The present criterion was developed about twenty years ago when wear and pitting were dominant causes for steam generator tube degradation. And it is based on tubes with single cracks regardless of the fact that the appearance of multiple cracks is more common in general. The objective of this study is to review the conservatism of the present plugging criteria of steam generator tubes and to propose a new coalescence model for two adjacent through-wall cracks existing in steam generator tubes. Using the existing failure models and experimental results, we reviewed the conservatism of the present plugging criteria. In order to verify the usefulness of the proposed new coalescence model, we performed finite element analysis and some parametric studies. Then, we developed a coalescence evaluation diagram

Bayesian Alternation During Tactile Augmentation

Directory of Open Access Journals (Sweden)

Caspar Mathias Goeke

2016-10-01

untrained humans is combined via a subjective Bayesian alternation process. Therefore we conclude that behavior in our bimodal condition is explained better by top down-subjective weighting than by bottom-up weighting based upon objective cue reliability.

Lane-Level Road Information Mining from Vehicle GPS Trajectories Based on Naïve Bayesian Classification

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Luliang Tang

2015-11-01

Full Text Available In this paper, we propose a novel approach for mining lane-level road network information from low-precision vehicle GPS trajectories (MLIT, which includes the number and turn rules of traffic lanes based on naïve Bayesian classification. First, the proposed method (MLIT uses an adaptive density optimization method to remove outliers from the raw GPS trajectories based on their space-time distribution and density clustering. Second, MLIT acquires the number of lanes in two steps. The first step establishes a naïve Bayesian classifier according to the trace features of the road plane and road profiles and the real number of lanes, as found in the training samples. The second step confirms the number of lanes using test samples in reference to the naïve Bayesian classifier using the known trace features of test sample. Third, MLIT infers the turn rules of each lane through tracking GPS trajectories. Experiments were conducted using the GPS trajectories of taxis in Wuhan, China. Compared with human-interpreted results, the automatically generated lane-level road network information was demonstrated to be of higher quality in terms of displaying detailed road networks with the number of lanes and turn rules of each lane.

Bayesian cost-effectiveness analysis with the R package BCEA

CERN Document Server

Baio, Gianluca; Heath, Anna

2017-01-01

The book provides a description of the process of health economic evaluation and modelling for cost-effectiveness analysis, particularly from the perspective of a Bayesian statistical approach. Some relevant theory and introductory concepts are presented using practical examples and two running case studies. The book also describes in detail how to perform health economic evaluations using the R package BCEA (Bayesian Cost-Effectiveness Analysis). BCEA can be used to post-process the results of a Bayesian cost-effectiveness model and perform advanced analyses producing standardised and highly customisable outputs. It presents all the features of the package, including its many functions and their practical application, as well as its user-friendly web interface. The book is a valuable resource for statisticians and practitioners working in the field of health economics wanting to simplify and standardise their workflow, for example in the preparation of dossiers in support of marketing authorisation, or acade...

The use of conflicts in searching Bayesian networks

OpenAIRE

Poole, David L.

2013-01-01

This paper discusses how conflicts (as used by the consistency-based diagnosis community) can be adapted to be used in a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency; this algorith...

Relative position control and coalescence of independent microparticles using ultrasonic waves

Science.gov (United States)

Deng, Shuang; Jia, Kun; Chen, Jian; Mei, Deqing; Yang, Keji

2017-05-01

Controlling the relative positions and coalescence of independent cells or microparticles is of particular importance for studying many physical phenomena, biological research, pharmaceutical tests, and chemical material processing. In this work, contactless maneuvering of two independent microparticles initially lying on a rigid surface was performed at a stable levitation height within a water-filled ultrasonic chamber. Three lead zirconate titanate transducers with 2 MHz thickness resonance frequency were obliquely mounted in a homemade device to form a sound field in a half space. By modulating the excitation voltage of a single transducer and the subsequent combination of amplitude and phase modulation, two separate 80 μm diameter silica beads were picked up from the chamber bottom, approached, and then coalesced to form a cluster in different ways. Both particles simultaneously migrated towards each other in the former process, while more dexterous movement with single-particle migration was realized for the other process. There is good agreement between the measured trajectories and theoretical predictions based on the theory of the first-order acoustic radiation force. The method introduced here also has the ability to form a cluster at any desired location in the chamber, which is promising for macromolecule processing ranging from the life sciences to biochemistry and clinical practice.

Bayesian spatial prediction of the site index in the study of the Missouri Ozark Forest Ecosystem Project

Science.gov (United States)

Xiaoqian Sun; Zhuoqiong He; John Kabrick

2008-01-01

This paper presents a Bayesian spatial method for analysing the site index data from the Missouri Ozark Forest Ecosystem Project (MOFEP). Based on ecological background and availability, we select three variables, the aspect class, the soil depth and the land type association as covariates for analysis. To allow great flexibility of the smoothness of the random field,...

Bayesian inference of earthquake parameters from buoy data using a polynomial chaos-based surrogate

KAUST Repository

Giraldi, Loic

2017-04-07

This work addresses the estimation of the parameters of an earthquake model by the consequent tsunami, with an application to the Chile 2010 event. We are particularly interested in the Bayesian inference of the location, the orientation, and the slip of an Okada-based model of the earthquake ocean floor displacement. The tsunami numerical model is based on the GeoClaw software while the observational data is provided by a single DARTⓇ buoy. We propose in this paper a methodology based on polynomial chaos expansion to construct a surrogate model of the wave height at the buoy location. A correlated noise model is first proposed in order to represent the discrepancy between the computational model and the data. This step is necessary, as a classical independent Gaussian noise is shown to be unsuitable for modeling the error, and to prevent convergence of the Markov Chain Monte Carlo sampler. Second, the polynomial chaos model is subsequently improved to handle the variability of the arrival time of the wave, using a preconditioned non-intrusive spectral method. Finally, the construction of a reduced model dedicated to Bayesian inference is proposed. Numerical results are presented and discussed.

Peak Bagging of red giant stars observed by Kepler: first results with a new method based on Bayesian nested sampling

Science.gov (United States)

Corsaro, Enrico; De Ridder, Joris

2015-09-01

The peak bagging analysis, namely the fitting and identification of single oscillation modes in stars' power spectra, coupled to the very high-quality light curves of red giant stars observed by Kepler, can play a crucial role for studying stellar oscillations of different flavor with an unprecedented level of detail. A thorough study of stellar oscillations would thus allow for deeper testing of stellar structure models and new insights in stellar evolution theory. However, peak bagging inferences are in general very challenging problems due to the large number of observed oscillation modes, hence of free parameters that can be involved in the fitting models. Efficiency and robustness in performing the analysis is what may be needed to proceed further. For this purpose, we developed a new code implementing the Nested Sampling Monte Carlo (NSMC) algorithm, a powerful statistical method well suited for Bayesian analyses of complex problems. In this talk we show the peak bagging of a sample of high signal-to-noise red giant stars by exploiting recent Kepler datasets and a new criterion for the detection of an oscillation mode based on the computation of the Bayesian evidence. Preliminary results for frequencies and lifetimes for single oscillation modes, together with acoustic glitches, are therefore presented.

Peak Bagging of red giant stars observed by Kepler: first results with a new method based on Bayesian nested sampling

Directory of Open Access Journals (Sweden)

Corsaro Enrico

2015-01-01

Full Text Available The peak bagging analysis, namely the fitting and identification of single oscillation modes in stars’ power spectra, coupled to the very high-quality light curves of red giant stars observed by Kepler, can play a crucial role for studying stellar oscillations of different flavor with an unprecedented level of detail. A thorough study of stellar oscillations would thus allow for deeper testing of stellar structure models and new insights in stellar evolution theory. However, peak bagging inferences are in general very challenging problems due to the large number of observed oscillation modes, hence of free parameters that can be involved in the fitting models. Efficiency and robustness in performing the analysis is what may be needed to proceed further. For this purpose, we developed a new code implementing the Nested Sampling Monte Carlo (NSMC algorithm, a powerful statistical method well suited for Bayesian analyses of complex problems. In this talk we show the peak bagging of a sample of high signal-to-noise red giant stars by exploiting recent Kepler datasets and a new criterion for the detection of an oscillation mode based on the computation of the Bayesian evidence. Preliminary results for frequencies and lifetimes for single oscillation modes, together with acoustic glitches, are therefore presented.

A dynamic Bayesian network based approach to safety decision support in tunnel construction

International Nuclear Information System (INIS)

Wu, Xianguo; Liu, Huitao; Zhang, Limao; Skibniewski, Miroslaw J.; Deng, Qianli; Teng, Jiaying

2015-01-01

This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time. The proposed DBN-based approach can accurately illustrate the dynamic and updated feature of geological, design and mechanical variables as the construction progress evolves, in order to overcome deficiencies of traditional fault analysis methods. Adopting the predictive, sensitivity and diagnostic analysis techniques in the DBN inference, this approach is able to perform feed-forward, concurrent and back-forward control respectively on a quantitative basis, and provide real-time support before and after an accident. A case study in relating to dynamic safety analysis in the construction of Wuhan Yangtze Metro Tunnel in China is used to verify the feasibility of the proposed approach, as well as its application potential. The relationships between the DBN-based and BN-based approaches are further discussed according to analysis results. The proposed approach can be used as a decision tool to provide support for safety analysis in tunnel construction, and thus increase the likelihood of a successful project in a dynamic project environment. - Highlights: • A dynamic Bayesian network (DBN) based approach for safety decision support is developed. • This approach is able to perform feed-forward, concurrent and back-forward analysis and control. • A case concerning dynamic safety analysis in Wuhan Yangtze Metro Tunnel in China is presented. • DBN-based approach can perform a higher accuracy than traditional static BN-based approach

Bayesian benefits with JASP

NARCIS (Netherlands)

Marsman, M.; Wagenmakers, E.-J.

2017-01-01

We illustrate the Bayesian approach to data analysis using the newly developed statistical software program JASP. With JASP, researchers are able to take advantage of the benefits that the Bayesian framework has to offer in terms of parameter estimation and hypothesis testing. The Bayesian

Bayesian modeling using WinBUGS

CERN Document Server

Ntzoufras, Ioannis

2009-01-01

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...

Hierarchical Bayesian Modeling of Fluid-Induced Seismicity

Science.gov (United States)

Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.

2017-11-01

In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.

Universal Darwinism As a Process of Bayesian Inference.

Science.gov (United States)

Campbell, John O

2016-01-01

Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

A unifying Bayesian account of contextual effects in value-based choice.

Directory of Open Access Journals (Sweden)

Francesco Rigoli

2017-10-01

Full Text Available Empirical evidence suggests the incentive value of an option is affected by other options available during choice and by options presented in the past. These contextual effects are hard to reconcile with classical theories and have inspired accounts where contextual influences play a crucial role. However, each account only addresses one or the other of the empirical findings and a unifying perspective has been elusive. Here, we offer a unifying theory of context effects on incentive value attribution and choice based on normative Bayesian principles. This formulation assumes that incentive value corresponds to a precision-weighted prediction error, where predictions are based upon expectations about reward. We show that this scheme explains a wide range of contextual effects, such as those elicited by other options available during choice (or within-choice context effects. These include both conditions in which choice requires an integration of multiple attributes and conditions where a multi-attribute integration is not necessary. Moreover, the same scheme explains context effects elicited by options presented in the past or between-choice context effects. Our formulation encompasses a wide range of contextual influences (comprising both within- and between-choice effects by calling on Bayesian principles, without invoking ad-hoc assumptions. This helps clarify the contextual nature of incentive value and choice behaviour and may offer insights into psychopathologies characterized by dysfunctional decision-making, such as addiction and pathological gambling.

Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model.

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Gerhard Moser

2015-04-01

Full Text Available Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96% had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches.

Impact of deep coalescence on the reliability of species tree inference from different types of DNA markers in mammals.

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Alejandro Sánchez-Gracia

Full Text Available An important challenge for phylogenetic studies of closely related species is the existence of deep coalescence and gene tree heterogeneity. However, their effects can vary between species and they are often neglected in phylogenetic analyses. In addition, a practical problem in the reconstruction of shallow phylogenies is to determine the most efficient set of DNA markers for a reliable estimation. To address these questions, we conducted a multilocus simulation study using empirical values of nucleotide diversity and substitution rates obtained from a wide range of mammals and evaluated the performance of both gene tree and species tree approaches to recover the known speciation times and topological relationships. We first show that deep coalescence can be a serious problem, more than usually assumed, for the estimation of speciation times in mammals using traditional gene trees. Furthermore, we tested the performance of different sets of DNA markers in the determination of species trees using a coalescent approach. Although the best estimates of speciation times were obtained, as expected, with the use of an increasing number of nuclear loci, our results show that similar estimations can be obtained with a much lower number of genes and the incorporation of a mitochondrial marker, with its high information content. Thus, the use of the combined information of both nuclear and mitochondrial markers in a species tree framework is the most efficient option to estimate recent speciation times and, consequently, the underlying species tree.

Inverse problems in the Bayesian framework

International Nuclear Information System (INIS)

Calvetti, Daniela; Somersalo, Erkki; Kaipio, Jari P

2014-01-01

The history of Bayesian methods dates back to the original works of Reverend Thomas Bayes and Pierre-Simon Laplace: the former laid down some of the basic principles on inverse probability in his classic article ‘An essay towards solving a problem in the doctrine of chances’ that was read posthumously in the Royal Society in 1763. Laplace, on the other hand, in his ‘Memoirs on inverse probability’ of 1774 developed the idea of updating beliefs and wrote down the celebrated Bayes’ formula in the form we know today. Although not identified yet as a framework for investigating inverse problems, Laplace used the formalism very much in the spirit it is used today in the context of inverse problems, e.g., in his study of the distribution of comets. With the evolution of computational tools, Bayesian methods have become increasingly popular in all fields of human knowledge in which conclusions need to be drawn based on incomplete and noisy data. Needless to say, inverse problems, almost by definition, fall into this category. Systematic work for developing a Bayesian inverse problem framework can arguably be traced back to the 1980s, (the original first edition being published by Elsevier in 1987), although articles on Bayesian methodology applied to inverse problems, in particular in geophysics, had appeared much earlier. Today, as testified by the articles in this special issue, the Bayesian methodology as a framework for considering inverse problems has gained a lot of popularity, and it has integrated very successfully with many traditional inverse problems ideas and techniques, providing novel ways to interpret and implement traditional procedures in numerical analysis, computational statistics, signal analysis and data assimilation. The range of applications where the Bayesian framework has been fundamental goes from geophysics, engineering and imaging to astronomy, life sciences and economy, and continues to grow. There is no question that Bayesian

Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy

Science.gov (United States)

Sharma, Sanjib

2017-08-01

Markov Chain Monte Carlo based Bayesian data analysis has now become the method of choice for analyzing and interpreting data in almost all disciplines of science. In astronomy, over the last decade, we have also seen a steady increase in the number of papers that employ Monte Carlo based Bayesian analysis. New, efficient Monte Carlo based methods are continuously being developed and explored. In this review, we first explain the basics of Bayesian theory and discuss how to set up data analysis problems within this framework. Next, we provide an overview of various Monte Carlo based methods for performing Bayesian data analysis. Finally, we discuss advanced ideas that enable us to tackle complex problems and thus hold great promise for the future. We also distribute downloadable computer software (available at https://github.com/sanjibs/bmcmc/ ) that implements some of the algorithms and examples discussed here.

Study of shock coalescence in laser-irradiated targets

International Nuclear Information System (INIS)

Coe, S.E.; Willi, O.; Afshar-Rad, T.; Rose, S.J.

1988-01-01

We report on the first direct experimental observation of the coalescence of two shocks induced by a shaped laser pulse. Optical streak photography of the rear surface of aluminum multiple step targets was used to study the breakout of these shocks and observe their behavior. The experimental results are compared with simulations by a one-dimensional Lagrangian hydrodynamic code

Fast gradient-based methods for Bayesian reconstruction of transmission and emission PET images

International Nuclear Information System (INIS)

Mumcuglu, E.U.; Leahy, R.; Zhou, Z.; Cherry, S.R.

1994-01-01

The authors describe conjugate gradient algorithms for reconstruction of transmission and emission PET images. The reconstructions are based on a Bayesian formulation, where the data are modeled as a collection of independent Poisson random variables and the image is modeled using a Markov random field. A conjugate gradient algorithm is used to compute a maximum a posteriori (MAP) estimate of the image by maximizing over the posterior density. To ensure nonnegativity of the solution, a penalty function is used to convert the problem to one of unconstrained optimization. Preconditioners are used to enhance convergence rates. These methods generally achieve effective convergence in 15--25 iterations. Reconstructions are presented of an 18 FDG whole body scan from data collected using a Siemens/CTI ECAT931 whole body system. These results indicate significant improvements in emission image quality using the Bayesian approach, in comparison to filtered backprojection, particularly when reprojections of the MAP transmission image are used in place of the standard attenuation correction factors

Development of an internet based system for modeling biotin metabolism using Bayesian networks.

Science.gov (United States)

Zhou, Jinglei; Wang, Dong; Schlegel, Vicki; Zempleni, Janos

2011-11-01

Biotin is an essential water-soluble vitamin crucial for maintaining normal body functions. The importance of biotin for human health has been under-appreciated but there is plenty of opportunity for future research with great importance for human health. Currently, carrying out predictions of biotin metabolism involves tedious manual manipulations. In this paper, we report the development of BiotinNet, an internet based program that uses Bayesian networks to integrate published data on various aspects of biotin metabolism. Users can provide a combination of values on the levels of biotin related metabolites to obtain the predictions on other metabolites that are not specified. As an inherent feature of Bayesian networks, the uncertainty of the prediction is also quantified and reported to the user. This program enables convenient in silico experiments regarding biotin metabolism, which can help researchers design future experiments while new data can be continuously incorporated. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

An approach based on Hierarchical Bayesian Graphical Models for measurement interpretation under uncertainty

Science.gov (United States)

Skataric, Maja; Bose, Sandip; Zeroug, Smaine; Tilke, Peter

2017-02-01

It is not uncommon in the field of non-destructive evaluation that multiple measurements encompassing a variety of modalities are available for analysis and interpretation for determining the underlying states of nature of the materials or parts being tested. Despite and sometimes due to the richness of data, significant challenges arise in the interpretation manifested as ambiguities and inconsistencies due to various uncertain factors in the physical properties (inputs), environment, measurement device properties, human errors, and the measurement data (outputs). Most of these uncertainties cannot be described by any rigorous mathematical means, and modeling of all possibilities is usually infeasible for many real time applications. In this work, we will discuss an approach based on Hierarchical Bayesian Graphical Models (HBGM) for the improved interpretation of complex (multi-dimensional) problems with parametric uncertainties that lack usable physical models. In this setting, the input space of the physical properties is specified through prior distributions based on domain knowledge and expertise, which are represented as Gaussian mixtures to model the various possible scenarios of interest for non-destructive testing applications. Forward models are then used offline to generate the expected distribution of the proposed measurements which are used to train a hierarchical Bayesian network. In Bayesian analysis, all model parameters are treated as random variables, and inference of the parameters is made on the basis of posterior distribution given the observed data. Learned parameters of the posterior distribution obtained after the training can therefore be used to build an efficient classifier for differentiating new observed data in real time on the basis of pre-trained models. We will illustrate the implementation of the HBGM approach to ultrasonic measurements used for cement evaluation of cased wells in the oil industry.

Kaolin Quality Prediction from Samples: A Bayesian Network Approach

International Nuclear Information System (INIS)

Rivas, T.; Taboada, J.; Ordonez, C.; Matias, J. M.

2009-01-01

We describe the results of an expert system applied to the evaluation of samples of kaolin for industrial use in paper or ceramic manufacture. Different machine learning techniques - classification trees, support vector machines and Bayesian networks - were applied with the aim of evaluating and comparing their interpretability and prediction capacities. The predictive capacity of these models for the samples analyzed was highly satisfactory, both for ceramic quality and paper quality. However, Bayesian networks generally proved to be the most useful technique for our study, as this approach combines good predictive capacity with excellent interpretability of the kaolin quality structure, as it graphically represents relationships between variables and facilitates what-if analyses.

Ground-Based Remote Sensing of Volcanic CO2 Fluxes at Solfatara (Italy—Direct Versus Inverse Bayesian Retrieval

Directory of Open Access Journals (Sweden)

Manuel Queißer

2018-01-01

Full Text Available CO2 is the second most abundant volatile species of degassing magma. CO2 fluxes carry information of incredible value, such as periods of volcanic unrest. Ground-based laser remote sensing is a powerful technique to measure CO2 fluxes in a spatially integrated manner, quickly and from a safe distance, but it needs accurate knowledge of the plume speed. The latter is often difficult to estimate, particularly for complex topographies. So, a supplementary or even alternative way of retrieving fluxes would be beneficial. Here, we assess Bayesian inversion as a potential technique for the case of the volcanic crater of Solfatara (Italy, a complex terrain hosting two major CO2 degassing fumarolic vents close to a steep slope. Direct integration of remotely sensed CO2 concentrations of these vents using plume speed derived from optical flow analysis yielded a flux of 717 ± 121 t day−1, in agreement with independent measurements. The flux from Bayesian inversion based on a simple Gaussian plume model was in excellent agreement under certain conditions. In conclusion, Bayesian inversion is a promising retrieval tool for CO2 fluxes, especially in situations where plume speed estimation methods fail, e.g., optical flow for transparent plumes. The results have implications beyond volcanology, including ground-based remote sensing of greenhouse gases and verification of satellite soundings.

Effects of waveform model systematics on the interpretation of GW150914

NARCIS (Netherlands)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Phythian-Adams, A.T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Aggarwal, N.T.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allocca, A.; Altin, P. A.; Ananyeva, A.; Anderson, S. B.; Anderson, W. G.; Appert, S.; Arai, K.; Araya, M. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Avila-Alvarez, A.; Babak, S.; Bacon, P.; Bader, M. K.M.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, R.D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Baune, C.; Bavigadda, V.; Bazzan, M.; Beer, C.; Bejger, M.; Belahcene, I.; Belgin, M.; Bell, A. S.; Berger, B. K.; Bergmann, G.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Billman, C. R.; Birch, M.J.; Birney, R.; Birnholtz, O.; Biscans, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blackman, J.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, A.L.S.; Bock, O.; Boer, M.; Bogaert, J.G.; Bohe, A.; Bondu, F.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Broida, J. E.; Brooks, A. F.; Brown, A.D.; Brown, D.; Brown, N. M.; Brunett, S.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cabero, M.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Calderón Bustillo, J.; Callister, T. A.; Calloni, E.; Camp, J. B.; Cannon, K. C.; Cao, H.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Casanueva Diaz, J.; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Cerboni Baiardi, L.; Cerretani, G.; Cesarini, E.; Chamberlin, S. J.; Chan, M.; Chao, D. S.; Charlton, P.; Chassande-Mottin, E.; Cheeseboro, B. D.; Chen, H. Y.; Chen, Y; Cheng, H. -P.; Chincarini, A.; Chiummo, A.; Chmiel, T.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Qian; Chua, A. J. K.; Chua, S. S. Y.; Chung, E.S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Cocchieri, C.; Coccia, E.; Cohadon, P. -F.; Colla, A.; Collette, C. G.; Cominsky, L.; Constancio, M., Jr.; Conti, L.; Cooper, S. J.; Corbitt, T. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, A.C.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J. -P.; Countryman, S. T.; Couvares, P.; Covas, P. B.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Creighton, J. D. E.; Creighton, T. D.; Cripe, J.; Crowder, S. G.; Cullen, T. J.; Cumming, A.; Cunningham, Laura; Cuoco, E.; Dal Canton, T.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dasgupta, A.; Da Silva Costa, C. F.; Dattilo, V.; Dave, I.; Davier, M.; Davies, G. S.; Davis, D.; Daw, E. J.; Day, B.; Day, R.; De, S.; Debra, D.; Debreczeni, G.; Degallaix, J.; De laurentis, M.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dergachev, V.A.; Rosa, R.; DeRosa, R. T.; DeSalvo, R.; Devenson, J.; Devine, R. C.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Giovanni, M. Di; Di Girolamo, T.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Virgilio, A.; Doctor, Z.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dorrington, I.; Douglas, R.; Dovale Álvarez, M.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H. -B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Eisenstein, R. A.; Essick, R. C.; Etienne, Z.; Etzel, T.; Evans, T. M.; Evans, T. M.; Everett, R.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.M.; Farinon, S.; Farr, B.; Farr, W. M.; Fauchon-Jones, E. J.; Favata, M.; Fays, M.; Fehrmann, H.; Fejer, M. M.; Fernández Galiana, A.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Fiori, I.; Fiorucci, D.; Fisher, R. P.; Flaminio, R.; Fletcher, M; Fong, H.; Forsyth, S. S.; Fournier, J. -D.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fries, E. M.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H.; Gadre, B. U.; Gaebel, S. M.; Gair, J. R.; Gammaitoni, L.; Gaonkar, S. G.; Garufi, F.; Gaur, G.; Gayathri, V.; Gehrels, N.; Gemme, G.; Genin, E.; Gennai, A.; George, J.; Gergely, L.; Germain, V.; Ghonge, S.; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.P.; Glaefke, A.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gonzalez Castro, J. M.; Gopakumar, A.; Gorodetsky, M. L.; Gossan, S. E.; Lee-Gosselin, M.; Gouaty, R.; Grado, A.; Graef, C.; Granata, M.; Grant, A.; Gras, S.; Gray, C.M.; Greco, G.; Green, A. C.; Groot, P.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hacker, J. J.; Buffoni-Hall, R.; Hall, E. D.; Hammond, G.L.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hanson, P.J.; Hardwick, T.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Hartman, M. T.; Haster, C. -J.; Haughian, K.; Healy, J.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Henry, J.A.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hofman, D.; Holt, K.; Holz, D. E.; Hopkins, P.; Hough, J.; Houston, E. A.; Howell, E. J.; Hu, Y. M.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Indik, N.; Ingram, D. R.; Inta, R.; Isa, H. N.; Isac, J. -M.; Isi, M.; Isogai, T.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W.; Jones, I.D.; Jones, R.; Jonker, R. J.G.; Ju, L.; Junker, J.; Kalaghatgi, C. V.; Kalogera, V.; Kandhasamy, S.; Kang, G.H.; Kanner, J. B.; Karki, S.; Karvinen, K. S.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kaur, T.; Kawabe, K.; Kéfélian, F.; Keitel, D.; Kelley, D. B.; Kennedy, R.E.; Key, J. S.; Khalili, F. Y.; Khan, I.; Khan., S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, Chunglee; Kim, J. C.; Kim, Whansun; Kim, W.; Kim, Y.M.; Kimbrell, S. J.; King, E. J.; King, P. J.; Kirchhoff, R.; Kissel, J. S.; Klein, B.; Kleybolte, L.; Klimenko, S.; Koch, P.; Koehlenbeck, S. M.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Krämer, C.; Kringel, V.; Krishnan, B.; Królak, A.; Kuehn, G.; Kumar, P.; Kumar, R.; Kuo, L.; Kutynia, A.; Lackey, B. D.; Landry, M.; Lang, R. N.; Lange, J.; Lantz, B.; Lanza, R. K.; Lartaux-Vollard, A.; Lasky, P. D.; Laxen, M.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C.H.; Lee, K.H.; Lee, M.H.; Lee, K.; Lehmann, J.; Lenon, A.; Leonardi, M.; Leong, J. R.; Leroy, N.; Letendre, N.; Levin, Y.; Li, T. G.F.; Libson, A.; Littenberg, T. B.; Liu, J.; Lockerbie, N. A.; Lombardi, A. L.; London, L. T.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lovelace, G.; Lück, H.; Lundgren, A. P.; Lynch, R.; Ma, Y.; Macfoy, S.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña-Sandoval, F.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Martelli, F.; Martellini, L.; Martin, I. W.; Martynov, D. V.; Mason, K.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Mastrogiovanni, S.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McGrath Hoareau, C.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McRae, T.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Meidam, J.; Melatos, A.; Mendell, G.; Mendoza-Gandara, D.; Mercer, R. A.; Merilh, E. L.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Metzdorff, R.; Meyers, P. M.; Mezzani, F.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, A. L.; Miller, A. L.; Miller, B.; Miller, J.; Millhouse, M.; Minenkov, Y.; Ming, J.; Mirshekari, S.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moggi, A.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, B.C.; Moore, Brian C J; Moraru, D.; Gutierrez Moreno, M.; Morriss, S. R.; Mours, B.; Mow-Lowry, C. M.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, S.D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Muniz, E. A. M.; Murray, P.G.; Mytidis, A.; Napier, K.; Nardecchia, I.; Naticchioni, L.; Nelemans, G.; Nelson, T. J. N.; Gutierrez-Neri, M.; Nery, M.; Neunzert, A.; Newport, J. M.; Newton-Howes, G.; Nguyen, T. T.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Noack, A.; Nocera, F.; Nolting, D.; Normandin, M. E. N.; Nuttall, L. K.; Oberling, J.; Ochsner, E.; Oelker, E.; Ogin, G. H.; Oh, J.; Oh, S. H.; Ohme, F.; Oliver, M. B.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; O'Shaughnessy, R.; Ottaway, D. J.; Overmier, H.; Owen, B. J.; Pace, A. E.; Page, J.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Paris, H. R.; Parker, W.S; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patricelli, B.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Castro-Perez, J.; Perreca, A.; Perri, L. M.; Pfeiffer, H. P.; Phelps, M.; Piccinni, O. J.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poe, M.; Poggiani, R.; Popolizio, P.; Post, A.; Powell, J.; Prasad, J.; Pratt, J. W. W.; Predoi, V.; Prestegard, T.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L. G.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Qin, J.; Qiu, S.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rajan, C.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Rhoades, E.; Ricci, F.; Riles, K.; Rizzo, D.M.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, J. D.; Romano, R.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.A.; Sachdev, Perminder S; Sadecki, T.; Sadeghian, L.; Sakellariadou, M.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sampson, L. M.; Sanchez, E. J.; Sandberg, V.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Scheuer, J.; Schmidt, E.; Schmidt, J; Schmidt, P.; Schnabel, R.B.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, K.E.C.; Schuette, D.; Schutz, B. F.; Schwalbe, S. G.; Scott, J.; Scott, M.S.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Setyawati, Y.; Shaddock, D. A.; Shaffer, T. J.; Shahriar, M. S.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sieniawska, M.; Sigg, D.; Silva, António Dias da; Singer, A; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, B.; Smith, R. J. E.; Smith, R. J. E.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Spencer, A. P.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stevenson-Moore, P.; Stone, J.R.; Strain, K. A.; Straniero, N.; Stratta, G.; Strigin, S. E.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sunil, S.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.D.; Talukder, D.; Tanner, D. B.; Tápai, M.; Taracchini, A.; Taylor, W.R.; Theeg, T.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thrane, E.; Tippens, T.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Toland, K.; Tomlinson, C.; Tonelli, M.; Tornasi, Z.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifir, D.; Trinastic, J.; Tringali, M. C.; Trozzo, L.; Tse, M.; Tso, R.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; Van Bakel, N.; Van Beuzekom, Martin; Van Den Brand, J. F.J.; Van Den Broeck, C.F.F.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Varma, V.; Vass, S.; Vasúth, M.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P.J.; Venkateswara, K.; Venugopalan, G.; Verkindt, D.; Vetrano, F.; Viceré, A.; Viets, A. D.; Vinciguerra, S.; Vine, D. J.; Vinet, J. -Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D. V.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, MT; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Watchi, J.; Weaver, B.; Wei, L. -W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.M.; Weßels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; Whiting, B. F.; Whittle, C.; Williams, D.; Williams, D.R.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Woehler, J.; Worden, J.; Wright, J.L.; Wu, D.S.; Wu, G.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yap, M. J.; Yu, Hang; Yu, Haocun; Yvert, M.; Zadrożny, A.; Zangrando, L.; Zanolin, M.; Zendri, J. -P.; Zevin, M.; Zhang, L.; Zhang, M.; Zhang, T.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, S.J.; Zhu, X. J.; Zucker, M. E.; Zweizig, J.; Boyle, M.; Chu, I.W.T.; Hemberger, D.; Hinder, I.; Kidder, L. E.; Ossokine, S.; Scheel, M.; Szilagyi, B.; Teukolsky, S.; Vano-Vinuales, A.

2017-01-01

Parameter estimates of GW150914 were obtained using Bayesian inference, based on three semi-analytic waveform models for binary black hole coalescences. These waveform models differ from each other in their treatment of black hole spins, and all three models make some simplifying assumptions,

Bayesian optimization for materials science

CERN Document Server

Packwood, Daniel

2017-01-01

This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science. Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While re...

Bayesian Exponential Smoothing.

OpenAIRE

Forbes, C.S.; Snyder, R.D.; Shami, R.S.

2000-01-01

In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows us to improve current practices surrounding exponential smoothing by providing both point predictions and measures of the uncertainty surrounding them.

The historical biogeography of Pteroglossus aracaris (Aves, Piciformes, Ramphastidae based on Bayesian analysis of mitochondrial DNA sequences

Directory of Open Access Journals (Sweden)

Sérgio L. Pereira

2008-01-01

Full Text Available Most Neotropical birds, including Pteroglossus aracaris, do not have an adequate fossil record to be used as time constraints in molecular dating. Hence, the evolutionary timeframe of the avian biota can only be inferred using alternative time constraints. We applied a Bayesian relaxed clock approach to propose an alternative interpretation for the historical biogeography of Pteroglossus based on mitochondrial DNA sequences, using different combinations of outgroups and time constraints obtained from outgroup fossils, vicariant barriers and molecular time estimates. The results indicated that outgroup choice has little effect on the Bayesian posterior distribution of divergence times within Pteroglossus , that geological and molecular time constraints seem equally suitable to estimate the Bayesian posterior distribution of divergence times for Pteroglossus , and that the fossil record alone overestimates divergence times within the fossil-lacking ingroup. The Bayesian estimates of divergence times suggest that the radiation of Pteroglossus occurred from the Late Miocene to the Pliocene (three times older than estimated by the “standard” mitochondrial rate of 2% sequence divergence per million years, likely triggered by Andean uplift, multiple episodes of marine transgressions in South America, and formation of present-day river basins. The time estimates are in agreement with other Neotropical taxa with similar geographic distributions.

Characterization of Solids Deposited on the Modular Caustic-Side Solvent Extraction Unit (MCU) Strip Effluent (SE) Coalescer Media Removed in April 2015

Energy Technology Data Exchange (ETDEWEB)

Fondeur, F. F. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

2016-06-13

On June 2015, Savannah River National Laboratory (SRNL) received a Strip Effluent (SE) coalescer (FLT-304) from MCU. That coalescer was first installed at MCU in late October 2014 and removed in April 2015. While processing approximately 48,700 gallons of strip solution, the pressure drop steadily increased linearly from 1 psi to near 16 psi (the administrative limit is 17 psi) with the total filtrate volume (2.1E-4 psi/gal of filtrate). The linear behavior is due to the combined effect of a constant deposition of material that starts from the closed-end to the mid-section of the coalescer reducing the available surface area of the coalescer for fluid passage (linearly with filtrate volume) and the formation of a secondary emulsion (water in NG-CSSX) on the fibers of the coalescer media. Both effects reduced the coalescer porosity by at least 13% (after processing 48,700 gallons). Before the coalescer was removed, it was flushed with a 10 mM boric acid solution to reduce the dose level. To determine the nature of the deposited material, a physical and chemical analysis of the coalescer was conducted. Characterization of this coalescer revealed the adsorption of organic containing amines (secondary amides and primary amines), TiDG, degraded modifier (with no hydroxyl group), MaxCalix, and oxidized hydrocarbon (possibly from Isopar™L or from lubricant used at MCU) onto the coalescer media. The amide and amines are possibly from the decomposition of the suppressor (TiDG). The modifier and MaxCalix were the largest components of the deposited organic material, as determined from leaching the coalescer with dichloromethane. Both the Fourier-Transformed Infrared (FTIR) and Fourier-Transformed Hydrogen Nuclear Magnetic Resonance (FT-HNMR) results indicated that some of the modifier was degraded (missing their OH groups). The modifier was observed everywhere in the examined coalescer pieces (FTIR), while the TiDG and its decomposition products were observed at the

Bayesian statistic methods and theri application in probabilistic simulation models

Directory of Open Access Journals (Sweden)

Sergio Iannazzo

2007-03-01

Full Text Available Bayesian statistic methods are facing a rapidly growing level of interest and acceptance in the field of health economics. The reasons of this success are probably to be found on the theoretical fundaments of the discipline that make these techniques more appealing to decision analysis. To this point should be added the modern IT progress that has developed different flexible and powerful statistical software framework. Among them probably one of the most noticeably is the BUGS language project and its standalone application for MS Windows WinBUGS. Scope of this paper is to introduce the subject and to show some interesting applications of WinBUGS in developing complex economical models based on Markov chains. The advantages of this approach reside on the elegance of the code produced and in its capability to easily develop probabilistic simulations. Moreover an example of the integration of bayesian inference models in a Markov model is shown. This last feature let the analyst conduce statistical analyses on the available sources of evidence and exploit them directly as inputs in the economic model.

BATSE gamma-ray burst line search. 2: Bayesian consistency methodology

Science.gov (United States)

Band, D. L.; Ford, L. A.; Matteson, J. L.; Briggs, M.; Paciesas, W.; Pendleton, G.; Preece, R.; Palmer, D.; Teegarden, B.; Schaefer, B.

1994-01-01

We describe a Bayesian methodology to evaluate the consistency between the reported Ginga and Burst and Transient Source Experiment (BATSE) detections of absorption features in gamma-ray burst spectra. Currently no features have been detected by BATSE, but this methodology will still be applicable if and when such features are discovered. The Bayesian methodology permits the comparison of hypotheses regarding the two detectors' observations and makes explicit the subjective aspects of our analysis (e.g., the quantification of our confidence in detector performance). We also present non-Bayesian consistency statistics. Based on preliminary calculations of line detectability, we find that both the Bayesian and non-Bayesian techniques show that the BATSE and Ginga observations are consistent given our understanding of these detectors.

Modelling of population dynamics of red king crab using Bayesian approach

Directory of Open Access Journals (Sweden)

Bakanev Sergey ...

2012-10-01

Modeling population dynamics based on the Bayesian approach enables to successfully resolve the above issues. The integration of the data from various studies into a unified model based on Bayesian parameter estimation method provides a much more detailed description of the processes occurring in the population.

An Intuitive Dashboard for Bayesian Network Inference

International Nuclear Information System (INIS)

Reddy, Vikas; Farr, Anna Charisse; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K D V

2014-01-01

Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++

An Intuitive Dashboard for Bayesian Network Inference

Science.gov (United States)

Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.

2014-03-01

Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.

Performance comparison of multi-detector detection statistics in targeted compact binary coalescence GW search

OpenAIRE

Haris, K; Pai, Archana

2016-01-01

Global network of advanced Interferometric gravitational wave (GW) detectors are expected to be on-line soon. Coherent observation of GW from a distant compact binary coalescence (CBC) with a network of interferometers located in different continents give crucial information about the source such as source location and polarization information. In this paper we compare different multi-detector network detection statistics for CBC search. In maximum likelihood ratio (MLR) based detection appro...

Study on ex-vessel cooling of RPV (behavior of coalesced bubbles and trigger condition of critical heat flux on inclined plate)

International Nuclear Information System (INIS)

Ohtake, H.; Koizumi, Y.; Takano, K.I.

2001-01-01

The Ex-vessel cooling of Reactor-Pressure-Vessel in Light-Water-Reactor at the severe accident have been proposed for future nuclear reactors. The estimation of Critical-Heat-Flux on a downward-facing curvilinear surface, like a hemisphere, is important to the assessment of the cooling. In this study, the CHFs on inclined surfaces were examined experimentally focusing on orientation of the heating surface. In order to discuss detailed mechanism of the CHF, the behaviors of coalesced bubbles near the heating surface were investigated through visual observations. The critical heat flux obtained in the present experiments increased with the inclined angle over the present experimental range. The dependence of the inclined angle on the critical heat flux was q CHF,R-113 [q] = f (q 0.33 ) for the present experimental results. The effect of the surface orientation on the critical heat flux was roughly explained by using the simple analytical model based on the macro-layer model and Kelvin-Helmholtz instability. From visual observations for behavior of bubbles near the heating surface, whereas the coalesced bubble covered over the heating surface for the inclined angle of 0 degree, the coalesced bubble moved upward to avoid packing the bubble on the surface above 5 degree. As the inclined angle increased, the velocity of the coalesced bubble was high, the period covered the heater and the bubble length were small. The results suggested that the CHF was closely related to forming the coalesced bubble and the behavior of the bubble. (author)

A heat transfer model for evaporating micro-channel coalescing bubble flow

International Nuclear Information System (INIS)

Consolini, L.; Thome, J.R.

2009-01-01

The current study presents a one-dimensional model of confined coalescing bubble flow for the prediction of micro-channel convective boiling heat transfer. Coalescing bubble flow has recently been identified as one of the characteristic flow patterns to be found in micro-scale systems, occurring at intermediate vapor qualities between the isolated bubble and the fully annular regimes. As two or more bubbles bond under the action of inertia and surface tension, the passage frequency of the bubble liquid slug pair declines, with a redistribution of liquid among the remaining flow structures. Assuming heat transfer to occur only by conduction through the thin evaporating liquid film surrounding individual bubbles, the present model includes a simplified description of the dynamics of the thin film evaporation process that takes into account the added mass transfer by breakup of the bridging liquid slugs. The new model has been confronted against experimental data taken within the coalescing bubble flow mode that have been identified by a diabatic micro-scale flow pattern map. The comparisons for three different fluids (R-134a, R-236fa and R-245fa) gave encouraging results with 83% of the database predicted within a ± 30% error band. (author)

Bayesian networks with examples in R

CERN Document Server

Scutari, Marco

2014-01-01

Introduction. The Discrete Case: Multinomial Bayesian Networks. The Continuous Case: Gaussian Bayesian Networks. More Complex Cases. Theory and Algorithms for Bayesian Networks. Real-World Applications of Bayesian Networks. Appendices. Bibliography.

Universal Darwinism as a process of Bayesian inference

Directory of Open Access Journals (Sweden)

John Oberon Campbell

2016-06-01

Full Text Available Many of the mathematical frameworks describing natural selection are equivalent to Bayes’ Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians. As Bayesian inference can always be cast in terms of (variational free energy minimization, natural selection can be viewed as comprising two components: a generative model of an ‘experiment’ in the external world environment, and the results of that 'experiment' or the 'surprise' entailed by predicted and actual outcomes of the ‘experiment’. Minimization of free energy implies that the implicit measure of 'surprise' experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

Science.gov (United States)

Khawaja, Taimoor Saleem

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

Bayesian Correlation Analysis for Sequence Count Data.

Directory of Open Access Journals (Sweden)

Daniel Sánchez-Taltavull

Full Text Available Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.

A particle system with cooperative branching and coalescence

Czech Academy of Sciences Publication Activity Database

Sturm, A.; Swart, Jan M.

2015-01-01

Roč. 25, č. 3 (2015), s. 1616-1649 ISSN 1050-5164 R&D Projects: GA ČR GAP201/10/0752 Institutional support: RVO:67985556 Keywords : interacting particle system * cooperative branching * coalescence * phase transition * upper invariant law * survival * extinction Subject RIV: BA - General Mathematics Impact factor: 1.755, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/swart-0442871.pdf

A discrete-time Bayesian network reliability modeling and analysis framework

International Nuclear Information System (INIS)

Boudali, H.; Dugan, J.B.

2005-01-01

Dependability tools are becoming an indispensable tool for modeling and analyzing (critical) systems. However the growing complexity of such systems calls for increasing sophistication of these tools. Dependability tools need to not only capture the complex dynamic behavior of the system components, but they must be also easy to use, intuitive, and computationally efficient. In general, current tools have a number of shortcomings including lack of modeling power, incapacity to efficiently handle general component failure distributions, and ineffectiveness in solving large models that exhibit complex dependencies between their components. We propose a novel reliability modeling and analysis framework based on the Bayesian network (BN) formalism. The overall approach is to investigate timed Bayesian networks and to find a suitable reliability framework for dynamic systems. We have applied our methodology to two example systems and preliminary results are promising. We have defined a discrete-time BN reliability formalism and demonstrated its capabilities from a modeling and analysis point of view. This research shows that a BN based reliability formalism is a powerful potential solution to modeling and analyzing various kinds of system components behaviors and interactions. Moreover, being based on the BN formalism, the framework is easy to use and intuitive for non-experts, and provides a basis for more advanced and useful analyses such as system diagnosis

AIC, BIC, Bayesian evidence against the interacting dark energy model

Energy Technology Data Exchange (ETDEWEB)

Szydlowski, Marek [Jagiellonian University, Astronomical Observatory, Krakow (Poland); Jagiellonian University, Mark Kac Complex Systems Research Centre, Krakow (Poland); Krawiec, Adam [Jagiellonian University, Institute of Economics, Finance and Management, Krakow (Poland); Jagiellonian University, Mark Kac Complex Systems Research Centre, Krakow (Poland); Kurek, Aleksandra [Jagiellonian University, Astronomical Observatory, Krakow (Poland); Kamionka, Michal [University of Wroclaw, Astronomical Institute, Wroclaw (Poland)

2015-01-01

Recent astronomical observations have indicated that the Universe is in a phase of accelerated expansion. While there are many cosmological models which try to explain this phenomenon, we focus on the interacting ΛCDM model where an interaction between the dark energy and dark matter sectors takes place. This model is compared to its simpler alternative - the ΛCDM model. To choose between these models the likelihood ratio test was applied as well as the model comparison methods (employing Occam's principle): the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the Bayesian evidence. Using the current astronomical data: type Ia supernova (Union2.1), h(z), baryon acoustic oscillation, the Alcock- Paczynski test, and the cosmic microwave background data, we evaluated both models. The analyses based on the AIC indicated that there is less support for the interacting ΛCDM model when compared to the ΛCDM model, while those based on the BIC indicated that there is strong evidence against it in favor of the ΛCDM model. Given the weak or almost non-existing support for the interacting ΛCDM model and bearing in mind Occam's razor we are inclined to reject this model. (orig.)

AIC, BIC, Bayesian evidence against the interacting dark energy model

Energy Technology Data Exchange (ETDEWEB)

Szydłowski, Marek, E-mail: marek.szydlowski@uj.edu.pl [Astronomical Observatory, Jagiellonian University, Orla 171, 30-244, Kraków (Poland); Mark Kac Complex Systems Research Centre, Jagiellonian University, Reymonta 4, 30-059, Kraków (Poland); Krawiec, Adam, E-mail: adam.krawiec@uj.edu.pl [Institute of Economics, Finance and Management, Jagiellonian University, Łojasiewicza 4, 30-348, Kraków (Poland); Mark Kac Complex Systems Research Centre, Jagiellonian University, Reymonta 4, 30-059, Kraków (Poland); Kurek, Aleksandra, E-mail: alex@oa.uj.edu.pl [Astronomical Observatory, Jagiellonian University, Orla 171, 30-244, Kraków (Poland); Kamionka, Michał, E-mail: kamionka@astro.uni.wroc.pl [Astronomical Institute, University of Wrocław, ul. Kopernika 11, 51-622, Wrocław (Poland)

2015-01-14

Recent astronomical observations have indicated that the Universe is in a phase of accelerated expansion. While there are many cosmological models which try to explain this phenomenon, we focus on the interacting ΛCDM model where an interaction between the dark energy and dark matter sectors takes place. This model is compared to its simpler alternative—the ΛCDM model. To choose between these models the likelihood ratio test was applied as well as the model comparison methods (employing Occam’s principle): the Akaike information criterion (AIC), the Bayesian information criterion (BIC) and the Bayesian evidence. Using the current astronomical data: type Ia supernova (Union2.1), h(z), baryon acoustic oscillation, the Alcock–Paczynski test, and the cosmic microwave background data, we evaluated both models. The analyses based on the AIC indicated that there is less support for the interacting ΛCDM model when compared to the ΛCDM model, while those based on the BIC indicated that there is strong evidence against it in favor of the ΛCDM model. Given the weak or almost non-existing support for the interacting ΛCDM model and bearing in mind Occam’s razor we are inclined to reject this model.

Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department

Directory of Open Access Journals (Sweden)

Wills Rachael A

2009-05-01

Full Text Available Abstract Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones, rather than objective reality. Bayesian analysis is (arguably a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

A multilocus evaluation of ermine (Mustela erminea) across the Holarctic, testing hypotheses of Pleistocene diversification in response to climate change

Science.gov (United States)

Dawson, Natalie G.; Hope, Andrew G.; Talbot, Sandra L.; Cook, Joseph A.

2013-01-01

Aim: We examined data for ermine (Mustela erminea) to test two sets of diversification hypotheses concerning the number and location of late Pleistocene refugia, the timing and mode of diversification, and the evolutionary influence of insularization. Location: Temperate and sub-Arctic Northern Hemisphere. Methods: We used up to two mitochondrial and four nuclear loci from 237 specimens for statistical phylogeographical and demographic analyses. Coalescent species-tree estimation used a Bayesian approach for clade divergence based on external mutation rate calibrations. Approximate Bayesian methods were used to assess population size, timing of divergence and gene flow. Results: Limited structure coupled with evidence of population growth across broad regions, including previously ice-covered areas, indicated expansion from multiple centres of differentiation, but high endemism along the North Pacific coast (NPC). A bifurcating model of diversification with recent growth spanning three glacial cycles best explained the empirical data. Main conclusions: A newly identified clade in North America indicated a fourth refugial area for ermine. The shallow coalescence of all extant ermine reflects a recent history of diversification overlying a deeper fossil record. Post-glacial colonization has led to potential contact zones for multiple lineages in north-western North America. A model of diversification of ermine accompanied by recent gene flow was marginally less well supported than a model of divergence of major clades in response to the most recent glacial cycles.

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging.

Science.gov (United States)

Zhang, Shuanghui; Liu, Yongxiang; Li, Xiang; Bi, Guoan

2016-04-28

This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.

Logarithmic Laplacian Prior Based Bayesian Inverse Synthetic Aperture Radar Imaging

Directory of Open Access Journals (Sweden)

Shuanghui Zhang

2016-04-01

Full Text Available This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP estimation and the maximum likelihood estimation (MLE are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression.

Factors governing partial coalescence in oil-in-water emulsions

NARCIS (Netherlands)

Fredrick, E.; Walstra, P.; Dewettinck, K.

2010-01-01

The consequences of the instability mechanism partial coalescence in oil-in-water food emulsions show a discrepancy. On the one hand, it needs to be avoided in order to achieve an extended shelf life in food products like sauces, creams and several milk products. On the other hand, during the

Coalescing black hole solution in the De-Sitter universe

International Nuclear Information System (INIS)

Ahmed, Mainuddin

2005-01-01

A new coalescing black hole solution of Einstein-Maxwell equation in general relativity is given. The new solution is also found to support the 'Nerst Theorem' of thermodynamics in the case of black hole. Thus this solution poses to solve an outstanding problem of thermodynamics and black hole physics. (author)

BEWARE database: A Bayesian-based system to assess wave-driven flooding hazards on coral reef-lined coasts

Data.gov (United States)

Department of the Interior — A process-based wave-resolving hydrodynamic model (XBeach Non-Hydrostatic, ‘XBNH’) was used to create a large synthetic database for use in a “Bayesian...

Bayesian salamanders: analysing the demography of an underground population of the European plethodontid Speleomantes strinatii with state-space modelling

Directory of Open Access Journals (Sweden)

Salvidio Sebastiano

2010-02-01

Full Text Available Abstract Background It has been suggested that Plethodontid salamanders are excellent candidates for indicating ecosystem health. However, detailed, long-term data sets of their populations are rare, limiting our understanding of the demographic processes underlying their population fluctuations. Here we present a demographic analysis based on a 1996 - 2008 data set on an underground population of Speleomantes strinatii (Aellen in NW Italy. We utilised a Bayesian state-space approach allowing us to parameterise a stage-structured Lefkovitch model. We used all the available population data from annual temporary removal experiments to provide us with the baseline data on the numbers of juveniles, subadults and adult males and females present at any given time. Results Sampling the posterior chains of the converged state-space model gives us the likelihood distributions of the state-specific demographic rates and the associated uncertainty of these estimates. Analysing the resulting parameterised Lefkovitch matrices shows that the population growth is very close to 1, and that at population equilibrium we expect half of the individuals present to be adults of reproductive age which is what we also observe in the data. Elasticity analysis shows that adult survival is the key determinant for population growth. Conclusion This analysis demonstrates how an understanding of population demography can be gained from structured population data even in a case where following marked individuals over their whole lifespan is not practical.

Forecasting neutrino masses from combining KATRIN and the CMB observations: Frequentist and Bayesian analyses

Science.gov (United States)

Host, Ole; Lahav, Ofer; Abdalla, Filipe B.; Eitel, Klaus

2007-12-01

We present a showcase for deriving bounds on the neutrino masses from laboratory experiments and cosmological observations. We compare the frequentist and Bayesian bounds on the effective electron neutrino mass mβ which the KATRIN neutrino mass experiment is expected to obtain, using both an analytical likelihood function and Monte Carlo simulations of KATRIN. Assuming a uniform prior in mβ, we find that a null result yields an upper bound of about 0.17 eV at 90% confidence in the Bayesian analysis, to be compared with the frequentist KATRIN reference value of 0.20 eV. This is a significant difference when judged relative to the systematic and statistical uncertainties of the experiment. On the other hand, an input mβ=0.35eV, which is the KATRIN 5σ detection threshold, would be detected at virtually the same level. Finally, we combine the simulated KATRIN results with cosmological data in the form of present (post-WMAP) and future (simulated Planck) observations. If an input of mβ=0.2eV is assumed in our simulations, KATRIN alone excludes a zero neutrino mass at 2.2σ. Adding Planck data increases the probability of detection to a median 2.7σ. The analysis highlights the importance of combining cosmological and laboratory data on an equal footing.

Forecasting neutrino masses from combining KATRIN and the CMB observations: Frequentist and Bayesian analyses

International Nuclear Information System (INIS)

Host, Ole; Lahav, Ofer; Abdalla, Filipe B.; Eitel, Klaus

2007-01-01

We present a showcase for deriving bounds on the neutrino masses from laboratory experiments and cosmological observations. We compare the frequentist and Bayesian bounds on the effective electron neutrino mass m β which the KATRIN neutrino mass experiment is expected to obtain, using both an analytical likelihood function and Monte Carlo simulations of KATRIN. Assuming a uniform prior in m β , we find that a null result yields an upper bound of about 0.17 eV at 90% confidence in the Bayesian analysis, to be compared with the frequentist KATRIN reference value of 0.20 eV. This is a significant difference when judged relative to the systematic and statistical uncertainties of the experiment. On the other hand, an input m β =0.35 eV, which is the KATRIN 5σ detection threshold, would be detected at virtually the same level. Finally, we combine the simulated KATRIN results with cosmological data in the form of present (post-WMAP) and future (simulated Planck) observations. If an input of m β =0.2 eV is assumed in our simulations, KATRIN alone excludes a zero neutrino mass at 2.2σ. Adding Planck data increases the probability of detection to a median 2.7σ. The analysis highlights the importance of combining cosmological and laboratory data on an equal footing

A Bayesian account of quantum histories

International Nuclear Information System (INIS)

Marlow, Thomas

2006-01-01

We investigate whether quantum history theories can be consistent with Bayesian reasoning and whether such an analysis helps clarify the interpretation of such theories. First, we summarise and extend recent work categorising two different approaches to formalising multi-time measurements in quantum theory. The standard approach consists of describing an ordered series of measurements in terms of history propositions with non-additive 'probabilities.' The non-standard approach consists of defining multi-time measurements to consist of sets of exclusive and exhaustive history propositions and recovering the single-time exclusivity of results when discussing single-time history propositions. We analyse whether such history propositions can be consistent with Bayes' rule. We show that certain class of histories are given a natural Bayesian interpretation, namely, the linearly positive histories originally introduced by Goldstein and Page. Thus, we argue that this gives a certain amount of interpretational clarity to the non-standard approach. We also attempt a justification of our analysis using Cox's axioms of probability theory

Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations

Energy Technology Data Exchange (ETDEWEB)

Chen, Peng, E-mail: peng@ices.utexas.edu [The Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th Street, Stop C0200, Austin, TX 78712-1229 (United States); Schwab, Christoph, E-mail: christoph.schwab@sam.math.ethz.ch [Seminar für Angewandte Mathematik, Eidgenössische Technische Hochschule, Römistrasse 101, CH-8092 Zürich (Switzerland)

2016-07-01

We extend the reduced basis (RB) accelerated Bayesian inversion methods for affine-parametric, linear operator equations which are considered in [16,17] to non-affine, nonlinear parametric operator equations. We generalize the analysis of sparsity of parametric forward solution maps in [20] and of Bayesian inversion in [48,49] to the fully discrete setting, including Petrov–Galerkin high-fidelity (“HiFi”) discretization of the forward maps. We develop adaptive, stochastic collocation based reduction methods for the efficient computation of reduced bases on the parametric solution manifold. The nonaffinity and nonlinearity with respect to (w.r.t.) the distributed, uncertain parameters and the unknown solution is collocated; specifically, by the so-called Empirical Interpolation Method (EIM). For the corresponding Bayesian inversion problems, computational efficiency is enhanced in two ways: first, expectations w.r.t. the posterior are computed by adaptive quadratures with dimension-independent convergence rates proposed in [49]; the present work generalizes [49] to account for the impact of the PG discretization in the forward maps on the convergence rates of the Quantities of Interest (QoI for short). Second, we propose to perform the Bayesian estimation only w.r.t. a parsimonious, RB approximation of the posterior density. Based on the approximation results in [49], the infinite-dimensional parametric, deterministic forward map and operator admit N-term RB and EIM approximations which converge at rates which depend only on the sparsity of the parametric forward map. In several numerical experiments, the proposed algorithms exhibit dimension-independent convergence rates which equal, at least, the currently known rate estimates for N-term approximation. We propose to accelerate Bayesian estimation by first offline construction of reduced basis surrogates of the Bayesian posterior density. The parsimonious surrogates can then be employed for online data

Bayesian analysis of rare events

Energy Technology Data Exchange (ETDEWEB)

Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

2016-06-01

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

Augmenting Data with Published Results in Bayesian Linear Regression

Science.gov (United States)

de Leeuw, Christiaan; Klugkist, Irene

2012-01-01

In most research, linear regression analyses are performed without taking into account published results (i.e., reported summary statistics) of similar previous studies. Although the prior density in Bayesian linear regression could accommodate such prior knowledge, formal models for doing so are absent from the literature. The goal of this…

Bayesian signal processing classical, modern, and particle filtering methods

CERN Document Server

Candy, James V

2016-01-01

This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters" as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to "fill-in-the gaps" of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical "sanity testing" lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed an...

Bayesian Modeling of a Human MMORPG Player

Science.gov (United States)

Synnaeve, Gabriel; Bessière, Pierre

2011-03-01

This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.

Heuristics as Bayesian inference under extreme priors.

Science.gov (United States)

Parpart, Paula; Jones, Matt; Love, Bradley C

2018-05-01

Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Estimating mental states of a depressed person with bayesian networks

NARCIS (Netherlands)

Klein, Michel C.A.; Modena, Gabriele

2013-01-01

In this work in progress paper we present an approach based on Bayesian Networks to model the relationship between mental states and empirical observations in a depressed person. We encode relationships and domain expertise as a Hierarchical Bayesian Network. Mental states are represented as latent

Bayesian Mediation Analysis

Science.gov (United States)

Yuan, Ying; MacKinnon, David P.

2009-01-01

In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…

Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms

Directory of Open Access Journals (Sweden)

Masoud Asgarpour

2018-01-01

Full Text Available Operation and maintenance costs are a major contributor to the Levelized Cost of Energy for electricity produced by offshore wind and can be significantly reduced if existing corrective actions are performed as efficiently as possible and if future corrective actions are avoided by performing sufficient preventive actions. This paper presents an applied and generic diagnostic model for fault detection and condition based maintenance of offshore wind components. The diagnostic model is based on two probabilistic matrices; first, a confidence matrix, representing the probability of detection using each fault detection method, and second, a diagnosis matrix, representing the individual outcome of each fault detection method. Once the confidence and diagnosis matrices of a component are defined, the individual diagnoses of each fault detection method are combined into a final verdict on the fault state of that component. Furthermore, this paper introduces a Bayesian updating model based on observations collected by inspections to decrease the uncertainty of initial confidence matrix. The framework and implementation of the presented diagnostic model are further explained within a case study for a wind turbine component based on vibration, temperature, and oil particle fault detection methods. The last part of the paper will have a discussion of the case study results and present conclusions.

Bayesian Networks and Influence Diagrams

DEFF Research Database (Denmark)

Kjærulff, Uffe Bro; Madsen, Anders Læsø

Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new...

Void growth and coalescence in metals deformed at elevated temperature

DEFF Research Database (Denmark)

Klöcker, H.; Tvergaard, Viggo

2000-01-01

For metals deformed at elevated temperatures the growth of voids to coalescence is studied numerically. The voids are assumed to be present from the beginning of deformation, and the rate of deformation considered is so high that void growth is dominated by power law creep of the material, without...... any noticeable effect of surface diffusion. Axisymmetric unit cell model computations are used to study void growth in a material containing a periodic array of voids, and the onset of the coalescence process is defined as the stage where plastic flow localizes in the ligaments between neighbouring...... voids. The focus of the study is on various relatively high stress triaxialties. In order to represent the results in terms of a porous ductile material model a set of constitutive relations are used, which have been proposed for void growth in a material undergoing power law creep....

Landscape genetics, historical isolation and cross-Andean gene flow in the wax palm, Ceroxylon echinulatum (Arecaceae)

DEFF Research Database (Denmark)

Trénel, P.; Hansen, Michael Møller; Nordmand, S.

2008-01-01

-Huancabamba zone (AHZ) of southern Ecuador/northern Peru, as indicated by observations on fruit morphology. Second, we assessed the timeframe of cross-Andean divergence, and third, we investigated the impact of contemporary and historical landscape features on observed spatio-genetic patterns. Individual-based...... Bayesian clustering (BC) identified a northeastern, southeastern, southwestern, and northwestern cluster, with areas of genetic discontinuity coinciding with the Andes and the Giron-Paute deflection. F-statistics derived from BC suggested an east-to-west dispersal history. Population-based analyses...... impact of climatic friction. Coalescent analysis revealed that cross-Andean divergence took place during the Quaternary. Significant historical isolation (RST > FST) was found in the southwestern population. The current study illustrates a joint effect of founder dynamics, divergence by distance...

Bayesian Estimation of Wave Spectra – Proper Formulation of ABIC

DEFF Research Database (Denmark)

Nielsen, Ulrik Dam

2007-01-01

It is possible to estimate on-site wave spectra using measured ship responses applied to Bayesian Modelling based on two prior information: the wave spectrum must be smooth both directional-wise and frequency-wise. This paper introduces two hyperparameters into Bayesian Modelling and, hence, a pr...

Systematic search of Bayesian statistics in the field of psychotraumatology

NARCIS (Netherlands)

van de Schoot, Rens; Schalken, Naomi; Olff, Miranda

2017-01-01

In many different disciplines there is a recent increase in interest of Bayesian analysis. Bayesian methods implement Bayes' theorem, which states that prior beliefs are updated with data, and this process produces updated beliefs about model parameters. The prior is based on how much information we

Perceptual stimulus-A Bayesian-based integration of multi-visual-cue approach and its application

Institute of Scientific and Technical Information of China (English)

XUE JianRu; ZHENG NanNing; ZHONG XiaoPin; PING LinJiang

2008-01-01

With the view that visual cue could be taken as a kind of stimulus, the study of the mechanism in the visual perception process by using visual cues in their probabilistic representation eventually leads to a class of statistical integration of multiple visual cues (IMVC) methods which have been applied widely in perceptual grouping, video analysis, and other basic problems in computer vision. In this paper, a survey on the basic ideas and recent advances of IMVC methods is presented, and much focus is on the models and algorithms of IMVC for video analysis within the framework of Bayesian estimation. Furthermore, two typical problems in video analysis, robust visual tracking and "switching problem" in multi-target tracking (MTT) are taken as test beds to verify a series of Bayesian-based IMVC methods proposed by the authors. Furthermore, the relations between the statistical IMVC and the visual per-ception process, as well as potential future research work for IMVC, are discussed.

Polynomial Chaos–Based Bayesian Inference of K-Profile Parameterization in a General Circulation Model of the Tropical Pacific

KAUST Repository

Sraj, Ihab; Zedler, Sarah E.; Knio, Omar; Jackson, Charles S.; Hoteit, Ibrahim

2016-01-01

The authors present a polynomial chaos (PC)-based Bayesian inference method for quantifying the uncertainties of the K-profile parameterization (KPP) within the MIT general circulation model (MITgcm) of the tropical Pacific. The inference

Extraction of features from sleep EEG for Bayesian assessment of brain development.

Directory of Open Access Journals (Sweden)

Vitaly Schetinin

Full Text Available Brain development can be evaluated by experts analysing age-related patterns in sleep electroencephalograms (EEG. Natural variations in the patterns, noise, and artefacts affect the evaluation accuracy as well as experts' agreement. The knowledge of predictive posterior distribution allows experts to estimate confidence intervals within which decisions are distributed. Bayesian approach to probabilistic inference has provided accurate estimates of intervals of interest. In this paper we propose a new feature extraction technique for Bayesian assessment and estimation of predictive distribution in a case of newborn brain development assessment. The new EEG features are verified within the Bayesian framework on a large EEG data set including 1,100 recordings made from newborns in 10 age groups. The proposed features are highly correlated with brain maturation and their use increases the assessment accuracy.

The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction

DEFF Research Database (Denmark)

Sabuncu, Mert R.; Van Leemput, Koen

2012-01-01

This paper presents the relevance voxel machine (RVoxM), a dedicated Bayesian model for making predictions based on medical imaging data. In contrast to the generic machine learning algorithms that have often been used for this purpose, the method is designed to utilize a small number of spatially...

Bayesian phylogeography finds its roots.

Directory of Open Access Journals (Sweden)

Philippe Lemey

2009-09-01

Full Text Available As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms.

Magnetic neutral point stretching and coalescence in tearing-generated magnetohydrodynamic structures

International Nuclear Information System (INIS)

Malara, F.; Veltri, P.; Carbone, V.

1991-01-01

The time evolution of the instability of a sheet pinch is numerically studied using a sufficiently high ratio of system length to width in order to allow the simultaneous growth of several unstable wavelengths. This numerical simulation provides new insights into the nonlinear development of the tearing instability. Before the instability saturates, the nonlinear interactions among the unstable modes produce local coalescence phenomena that destroy the weaker current pinches and reduce the number of magnetic islands. In contrast with the usual picture, this coalescence is not due to the attraction between the current maxima, but is due to the stretching of the X-neutral points associated with the most intense current pinches. The global perturbation growth rate remains essentially unchanged in time, being of the order of the resistive instability growth rate

Risk-Based Operation and Maintenance of Offshore Wind Turbines using Bayesian Networks

DEFF Research Database (Denmark)

Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

2011-01-01

the lifetime. Two different approaches are used; one uses a threshold value of the failure probability, and one uses a Limited Memory Influence Diagram. Both methods are tested for an application example using MonteCarlo sampling, and they are both found to be efficient and equally good.......For offshore wind farms, the costs due to operation and maintenance are large, and more optimal planning has the potential of reducing these costs. This paper presents how Bayesian networks can be used for risk-based inspection planning, where the inspection plans are updated each year through...

Physics-based, Bayesian sequential detection method and system for radioactive contraband

Science.gov (United States)

Candy, James V; Axelrod, Michael C; Breitfeller, Eric F; Chambers, David H; Guidry, Brian L; Manatt, Douglas R; Meyer, Alan W; Sale, Kenneth E

2014-03-18

A distributed sequential method and system for detecting and identifying radioactive contraband from highly uncertain (noisy) low-count, radionuclide measurements, i.e. an event mode sequence (EMS), using a statistical approach based on Bayesian inference and physics-model-based signal processing based on the representation of a radionuclide as a monoenergetic decomposition of monoenergetic sources. For a given photon event of the EMS, the appropriate monoenergy processing channel is determined using a confidence interval condition-based discriminator for the energy amplitude and interarrival time and parameter estimates are used to update a measured probability density function estimate for a target radionuclide. A sequential likelihood ratio test is then used to determine one of two threshold conditions signifying that the EMS is either identified as the target radionuclide or not, and if not, then repeating the process for the next sequential photon event of the EMS until one of the two threshold conditions is satisfied.

Feasibility of streamlining an interactive Bayesian-based diagnostic support tool designed for clinical practice

Science.gov (United States)

Chen, Po-Hao; Botzolakis, Emmanuel; Mohan, Suyash; Bryan, R. N.; Cook, Tessa

2016-03-01

In radiology, diagnostic errors occur either through the failure of detection or incorrect interpretation. Errors are estimated to occur in 30-35% of all exams and contribute to 40-54% of medical malpractice litigations. In this work, we focus on reducing incorrect interpretation of known imaging features. Existing literature categorizes cognitive bias leading a radiologist to an incorrect diagnosis despite having correctly recognized the abnormal imaging features: anchoring bias, framing effect, availability bias, and premature closure. Computational methods make a unique contribution, as they do not exhibit the same cognitive biases as a human. Bayesian networks formalize the diagnostic process. They modify pre-test diagnostic probabilities using clinical and imaging features, arriving at a post-test probability for each possible diagnosis. To translate Bayesian networks to clinical practice, we implemented an entirely web-based open-source software tool. In this tool, the radiologist first selects a network of choice (e.g. basal ganglia). Then, large, clearly labeled buttons displaying salient imaging features are displayed on the screen serving both as a checklist and for input. As the radiologist inputs the value of an extracted imaging feature, the conditional probabilities of each possible diagnosis are updated. The software presents its level of diagnostic discrimination using a Pareto distribution chart, updated with each additional imaging feature. Active collaboration with the clinical radiologist is a feasible approach to software design and leads to design decisions closely coupling the complex mathematics of conditional probability in Bayesian networks with practice.

Airline Sustainability Modeling: A New Framework with Application of Bayesian Structural Equation Modeling

Directory of Open Access Journals (Sweden)

Hashem Salarzadeh Jenatabadi

2016-11-01

Full Text Available There are many factors which could influence the sustainability of airlines. The main purpose of this study is to introduce a framework for a financial sustainability index and model it based on structural equation modeling (SEM with maximum likelihood and Bayesian predictors. The introduced framework includes economic performance, operational performance, cost performance, and financial performance. Based on both Bayesian SEM (Bayesian-SEM and Classical SEM (Classical-SEM, it was found that economic performance with both operational performance and cost performance are significantly related to the financial performance index. The four mathematical indices employed are root mean square error, coefficient of determination, mean absolute error, and mean absolute percentage error to compare the efficiency of Bayesian-SEM and Classical-SEM in predicting the airline financial performance. The outputs confirmed that the framework with Bayesian prediction delivered a good fit with the data, although the framework predicted with a Classical-SEM approach did not prepare a well-fitting model. The reasons for this discrepancy between Classical and Bayesian predictions, as well as the potential advantages and caveats with the application of Bayesian approach in airline sustainability studies, are debated.

Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues.

Science.gov (United States)

Smadbeck, Patrick; Stumpf, Michael P H

2016-04-01

Development is a process that needs to be tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny. A generic feature of developing and homeostatic tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end quickly. Computational and theoretical biologists/physicists have, in response, developed a range of modelling approaches, most notably agent-based modelling. These models seem to capture features observed in experiments, but can also become computationally expensive. Here, we develop complementary genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We show that with both bounded and unbounded growth simple, but universal scaling relationships allow us to connect coalescent theory with the fractal growth models extensively used in developmental biology. Using our genealogical perspective, it is possible to study bulk statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations. © 2016 The Authors.

Bayesian estimation of the discrete coefficient of determination.

Science.gov (United States)

Chen, Ting; Braga-Neto, Ulisses M

2016-12-01

The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

qPR: An adaptive partial-report procedure based on Bayesian inference.

Science.gov (United States)

Baek, Jongsoo; Lesmes, Luis Andres; Lu, Zhong-Lin

2016-08-01

Iconic memory is best assessed with the partial report procedure in which an array of letters appears briefly on the screen and a poststimulus cue directs the observer to report the identity of the cued letter(s). Typically, 6-8 cue delays or 600-800 trials are tested to measure the iconic memory decay function. Here we develop a quick partial report, or qPR, procedure based on a Bayesian adaptive framework to estimate the iconic memory decay function with much reduced testing time. The iconic memory decay function is characterized by an exponential function and a joint probability distribution of its three parameters. Starting with a prior of the parameters, the method selects the stimulus to maximize the expected information gain in the next test trial. It then updates the posterior probability distribution of the parameters based on the observer's response using Bayesian inference. The procedure is reiterated until either the total number of trials or the precision of the parameter estimates reaches a certain criterion. Simulation studies showed that only 100 trials were necessary to reach an average absolute bias of 0.026 and a precision of 0.070 (both in terms of probability correct). A psychophysical validation experiment showed that estimates of the iconic memory decay function obtained with 100 qPR trials exhibited good precision (the half width of the 68.2% credible interval = 0.055) and excellent agreement with those obtained with 1,600 trials of the conventional method of constant stimuli procedure (RMSE = 0.063). Quick partial-report relieves the data collection burden in characterizing iconic memory and makes it possible to assess iconic memory in clinical populations.

Construction of Monitoring Model and Algorithm Design on Passenger Security during Shipping Based on Improved Bayesian Network

Science.gov (United States)

Wang, Jiali; Zhang, Qingnian; Ji, Wenfeng

2014-01-01

A large number of data is needed by the computation of the objective Bayesian network, but the data is hard to get in actual computation. The calculation method of Bayesian network was improved in this paper, and the fuzzy-precise Bayesian network was obtained. Then, the fuzzy-precise Bayesian network was used to reason Bayesian network model when the data is limited. The security of passengers during shipping is affected by various factors, and it is hard to predict and control. The index system that has the impact on the passenger safety during shipping was established on basis of the multifield coupling theory in this paper. Meanwhile, the fuzzy-precise Bayesian network was applied to monitor the security of passengers in the shipping process. The model was applied to monitor the passenger safety during shipping of a shipping company in Hainan, and the effectiveness of this model was examined. This research work provides guidance for guaranteeing security of passengers during shipping. PMID:25254227

Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments.

Science.gov (United States)

Kaplan, David; Lee, Chansoon

2018-01-01

This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model's posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

Bayesian Inference on Gravitational Waves

Directory of Open Access Journals (Sweden)

Asad Ali

2015-12-01

Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an  overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.

SU-E-T-144: Bayesian Inference of Local Relapse Data Using a Poisson-Based Tumour Control Probability Model

Energy Technology Data Exchange (ETDEWEB)

La Russa, D [The Ottawa Hospital Cancer Centre, Ottawa, ON (Canada)

2015-06-15

Purpose: The purpose of this project is to develop a robust method of parameter estimation for a Poisson-based TCP model using Bayesian inference. Methods: Bayesian inference was performed using the PyMC3 probabilistic programming framework written in Python. A Poisson-based TCP regression model that accounts for clonogen proliferation was fit to observed rates of local relapse as a function of equivalent dose in 2 Gy fractions for a population of 623 stage-I non-small-cell lung cancer patients. The Slice Markov Chain Monte Carlo sampling algorithm was used to sample the posterior distributions, and was initiated using the maximum of the posterior distributions found by optimization. The calculation of TCP with each sample step required integration over the free parameter α, which was performed using an adaptive 24-point Gauss-Legendre quadrature. Convergence was verified via inspection of the trace plot and posterior distribution for each of the fit parameters, as well as with comparisons of the most probable parameter values with their respective maximum likelihood estimates. Results: Posterior distributions for α, the standard deviation of α (σ), the average tumour cell-doubling time (Td), and the repopulation delay time (Tk), were generated assuming α/β = 10 Gy, and a fixed clonogen density of 10{sup 7} cm−{sup 3}. Posterior predictive plots generated from samples from these posterior distributions are in excellent agreement with the observed rates of local relapse used in the Bayesian inference. The most probable values of the model parameters also agree well with maximum likelihood estimates. Conclusion: A robust method of performing Bayesian inference of TCP data using a complex TCP model has been established.

Bubble Coalescence: Effect of Bubble Approach Velocity and Liquid Viscosity

Czech Academy of Sciences Publication Activity Database

Orvalho, Sandra; Růžička, Marek; Olivieri, G.; Marzocchella, A.

2015-01-01

Roč. 134, SEP 29 (2015), s. 205-216 ISSN 0009-2509 R&D Projects: GA MŠk(CZ) LD13018 Institutional support: RVO:67985858 Keywords : bubble coalescence * bubble approach velocity * liquid viscosity Subject RIV: CI - Industrial Chemistry, Chemical Engineering Impact factor: 2.750, year: 2015

Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood.

Science.gov (United States)

Wu, Yufeng

2012-03-01

Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this article, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS (which stands for Species Tree InfErence with Likelihood for Lineage Sorting), has been implemented in a program that is downloadable from the author's web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets. © 2011 The Author. Evolution© 2011 The Society for the Study of Evolution.

Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.

Science.gov (United States)

Kutschireiter, Anna; Surace, Simone Carlo; Sprekeler, Henning; Pfister, Jean-Pascal

2017-08-18

The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that animals' performance in many tasks is consistent with such a Bayesian statistical interpretation. However, it is presently unclear how a nonlinear Bayesian filter can be efficiently implemented in a network of neurons that satisfies some minimum constraints of biological plausibility. Here, we propose the Neural Particle Filter (NPF), a sampling-based nonlinear Bayesian filter, which does not rely on importance weights. We show that this filter can be interpreted as the neuronal dynamics of a recurrently connected rate-based neural network receiving feed-forward input from sensory neurons. Further, it captures properties of temporal and multi-sensory integration that are crucial for perception, and it allows for online parameter learning with a maximum likelihood approach. The NPF holds the promise to avoid the 'curse of dimensionality', and we demonstrate numerically its capability to outperform weighted particle filters in higher dimensions and when the number of particles is limited.

Multiscale Simulations of Magnetic Island Coalescence

Science.gov (United States)

Dorelli, John C.

2010-01-01

We describe a new interactive parallel Adaptive Mesh Refinement (AMR) framework written in the Python programming language. This new framework, PyAMR, hides the details of parallel AMR data structures and algorithms (e.g., domain decomposition, grid partition, and inter-process communication), allowing the user to focus on the development of algorithms for advancing the solution of a systems of partial differential equations on a single uniform mesh. We demonstrate the use of PyAMR by simulating the pairwise coalescence of magnetic islands using the resistive Hall MHD equations. Techniques for coupling different physics models on different levels of the AMR grid hierarchy are discussed.

Bayesian Classification of Image Structures

DEFF Research Database (Denmark)

Goswami, Dibyendu; Kalkan, Sinan; Krüger, Norbert

2009-01-01

In this paper, we describe work on Bayesian classi ers for distinguishing between homogeneous structures, textures, edges and junctions. We build semi-local classiers from hand-labeled images to distinguish between these four different kinds of structures based on the concept of intrinsic dimensi...

A Web-Based System for Bayesian Benchmark Dose Estimation.

Science.gov (United States)

Shao, Kan; Shapiro, Andrew J

2018-01-11

Benchmark dose (BMD) modeling is an important step in human health risk assessment and is used as the default approach to identify the point of departure for risk assessment. A probabilistic framework for dose-response assessment has been proposed and advocated by various institutions and organizations; therefore, a reliable tool is needed to provide distributional estimates for BMD and other important quantities in dose-response assessment. We developed an online system for Bayesian BMD (BBMD) estimation and compared results from this software with U.S. Environmental Protection Agency's (EPA's) Benchmark Dose Software (BMDS). The system is built on a Bayesian framework featuring the application of Markov chain Monte Carlo (MCMC) sampling for model parameter estimation and BMD calculation, which makes the BBMD system fundamentally different from the currently prevailing BMD software packages. In addition to estimating the traditional BMDs for dichotomous and continuous data, the developed system is also capable of computing model-averaged BMD estimates. A total of 518 dichotomous and 108 continuous data sets extracted from the U.S. EPA's Integrated Risk Information System (IRIS) database (and similar databases) were used as testing data to compare the estimates from the BBMD and BMDS programs. The results suggest that the BBMD system may outperform the BMDS program in a number of aspects, including fewer failed BMD and BMDL calculations and estimates. The BBMD system is a useful alternative tool for estimating BMD with additional functionalities for BMD analysis based on most recent research. Most importantly, the BBMD has the potential to incorporate prior information to make dose-response modeling more reliable and can provide distributional estimates for important quantities in dose-response assessment, which greatly facilitates the current trend for probabilistic risk assessment. https://doi.org/10.1289/EHP1289.

Model-based Bayesian signal extraction algorithm for peripheral nerves

Science.gov (United States)

Eggers, Thomas E.; Dweiri, Yazan M.; McCallum, Grant A.; Durand, Dominique M.

2017-10-01

Objective. Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time. Approach. To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, hybrid Bayesian signal extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms. Main results. The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10-20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity. Significance. HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of

GALAXY ROTATION AND RAPID SUPERMASSIVE BINARY COALESCENCE

Energy Technology Data Exchange (ETDEWEB)

Holley-Bockelmann, Kelly [Vanderbilt University, Nashville, TN (United States); Khan, Fazeel Mahmood, E-mail: k.holley@vanderbilt.edu [Institute of Space Technology (IST), Islamabad (Pakistan)

2015-09-10

Galaxy mergers usher the supermassive black hole (SMBH) in each galaxy to the center of the potential, where they form an SMBH binary. The binary orbit shrinks by ejecting stars via three-body scattering, but ample work has shown that in spherical galaxy models, the binary separation stalls after ejecting all the stars in its loss cone—this is the well-known final parsec problem. However, it has been shown that SMBH binaries in non-spherical galactic nuclei harden at a nearly constant rate until reaching the gravitational wave regime. Here we use a suite of direct N-body simulations to follow SMBH binary evolution in both corotating and counterrotating flattened galaxy models. For N > 500 K, we find that the evolution of the SMBH binary is convergent and is independent of the particle number. Rotation in general increases the hardening rate of SMBH binaries even more effectively than galaxy geometry alone. SMBH binary hardening rates are similar for co- and counterrotating galaxies. In the corotating case, the center of mass of the SMBH binary settles into an orbit that is in corotation resonance with the background rotating model, and the coalescence time is roughly a few 100 Myr faster than a non-rotating flattened model. We find that counterrotation drives SMBHs to coalesce on a nearly radial orbit promptly after forming a hard binary. We discuss the implications for gravitational wave astronomy, hypervelocity star production, and the effect on the structure of the host galaxy.

GALAXY ROTATION AND RAPID SUPERMASSIVE BINARY COALESCENCE

International Nuclear Information System (INIS)

Holley-Bockelmann, Kelly; Khan, Fazeel Mahmood

2015-01-01

Galaxy mergers usher the supermassive black hole (SMBH) in each galaxy to the center of the potential, where they form an SMBH binary. The binary orbit shrinks by ejecting stars via three-body scattering, but ample work has shown that in spherical galaxy models, the binary separation stalls after ejecting all the stars in its loss cone—this is the well-known final parsec problem. However, it has been shown that SMBH binaries in non-spherical galactic nuclei harden at a nearly constant rate until reaching the gravitational wave regime. Here we use a suite of direct N-body simulations to follow SMBH binary evolution in both corotating and counterrotating flattened galaxy models. For N > 500 K, we find that the evolution of the SMBH binary is convergent and is independent of the particle number. Rotation in general increases the hardening rate of SMBH binaries even more effectively than galaxy geometry alone. SMBH binary hardening rates are similar for co- and counterrotating galaxies. In the corotating case, the center of mass of the SMBH binary settles into an orbit that is in corotation resonance with the background rotating model, and the coalescence time is roughly a few 100 Myr faster than a non-rotating flattened model. We find that counterrotation drives SMBHs to coalesce on a nearly radial orbit promptly after forming a hard binary. We discuss the implications for gravitational wave astronomy, hypervelocity star production, and the effect on the structure of the host galaxy

Bayesian analysis in plant pathology.

Science.gov (United States)

Mila, A L; Carriquiry, A L

2004-09-01

ABSTRACT Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.

A Bayesian approach to model uncertainty

International Nuclear Information System (INIS)

Buslik, A.

1994-01-01

A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given

On the prior probabilities for two-stage Bayesian estimates

International Nuclear Information System (INIS)

Kohut, P.

1992-01-01

The method of Bayesian inference is reexamined for its applicability and for the required underlying assumptions in obtaining and using prior probability estimates. Two different approaches are suggested to determine the first-stage priors in the two-stage Bayesian analysis which avoid certain assumptions required for other techniques. In the first scheme, the prior is obtained through a true frequency based distribution generated at selected intervals utilizing actual sampling of the failure rate distributions. The population variability distribution is generated as the weighed average of the frequency distributions. The second method is based on a non-parametric Bayesian approach using the Maximum Entropy Principle. Specific features such as integral properties or selected parameters of prior distributions may be obtained with minimal assumptions. It is indicated how various quantiles may also be generated with a least square technique

Difference in growth and coalescing patterns of droplets on bi-philic surfaces with varying spatial distribution.

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Garimella, Martand Mayukh; Koppu, Sudheer; Kadlaskar, Shantanu Shrikant; Pillutla, Venkata; Abhijeet; Choi, Wonjae

2017-11-01

This paper reports the condensation and subsequent motion of water droplets on bi-philic surfaces, surfaces that are patterned with regions of different wettability. Bi-philic surfaces can enhance the water collection efficiency: droplets condensing on hydrophobic regions wick into hydrophilic drain channels when droplets grow to a certain size, renewing the condensation on the dry hydrophobic region. The onset of drain phenomenon can be triggered by multiple events with distinct nature ranging from gravity, direct contact between a droplet and a drain channel, to a mutual coalescence between droplets. This paper focuses on the effect of the length scale of hydrophobic regions on the dynamics of mutual coalescence between droplets and subsequent drainage. The main hypothesis was that, when the drop size is sufficient, the kinetic energy associated with a coalescence of droplets may cause dynamic advancing of a newly formed drop, leading to further coalescence with nearby droplets and ultimately to a chain reaction. We fabricate bi-philic surfaces with hydrophilic and hydrophobic stripes, and the result confirms that coalescing droplets, when the length scale of droplets increases beyond 0.2mm, indeed display dynamic expansion and chain reaction. Multiple droplets can thus migrate to hydrophilic drain simultaneously even when the initial motion of the droplets was not triggered by the direct contact between the droplet and the hydrophilic drain. Efficiency of drain due to mutual coalescence of droplets varies depending on the length scale of bi-philic patterns, and the drain phenomenon reaches its peak when the width of hydrophobic stripes is between 800μm and 1mm. The Ohnesorge number of droplets draining on noted surfaces is between 0.0042 and 0.0037 respectively. The observed length scale of bi-philic patterns matches that on the Stenocara beetle's fog harvesting back surface. This match between length scales suggests that the surface of the insect is optimized

Constitutively expressed Protocadherin-α regulates the coalescence and elimination of homotypic olfactory axons through its cytoplasmic region

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Sonoko eHasegawa

2012-10-01

Full Text Available Olfactory sensory neuron (OSN axons coalesce into specific glomeruli in the olfactory bulb (OB according to their odorant receptor (OR expression. Several guidance molecules enhance the coalescence of homotypic OSN projections, in an OR-specific- and neural-activity-dependent manner. However, the mechanism by which homotypic OSN axons are organized into glomeruli is unsolved. We previously reported that the clustered protocadherin-α (Pcdh-α family of diverse cadherin-related molecules plays roles in the coalescence and elimination of homotypic OSN axons throughout development. Here we showed that the elimination of small ectopic homotypic glomeruli required the constitutive expression of a Pcdh-α isoform and Pcdh-α’s cytoplasmic region, but not OR specificity or neural activity. These results suggest that Pcdh-α proteins provide a cytoplasmic signal to regulate repulsive activity for homotypic OSN axons independently of OR expression and neural activity. The counterbalancing effect of Pcdh-α proteins for the axonal coalescence mechanisms mediated by other olfactory guidance molecules indicate a possible mechanism for the organization of homotypic OSN axons into glomeruli during development.

Coalescence of two equal cylinders: exact results for creeping viscous plane flow driven by capillarity

International Nuclear Information System (INIS)

Hopper, R.W.

1984-01-01

The coalescence of two equal viscous cylinders under the influence of capillarity is of interest in the theory of sintering. Although the flow in typical cylinder coalescence experiments is not planar, the plane-flow case is of general interest and is a good approximation in the early stage. An essentially exact analytic solution giving the shape as a function of time for slow plane flow is presented in simple closed form. 16 references, 2 figures, 1 table

Breakup and coalescence characteristics of a hollow cone swirling spray

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Saha, Abhishek; Lee, Joshua D.; Basu, Saptarshi; Kumar, Ranganathan

2012-12-01

This paper deals with an experimental study of the breakup characteristics of water emanating from hollow cone hydraulic injector nozzles induced by pressure-swirling. The experiments were conducted using two nozzles with different orifice diameters 0.3 mm and 0.5 mm and injection pressures (0.3-4 MPa) which correspond to Rep = 7000-26 000. Two types of laser diagnostic techniques were utilized: shadowgraph and phase Doppler particle anemometry for a complete study of the atomization process. Measurements that were made in the spray in both axial and radial directions indicate that both velocity and average droplet diameter profiles are highly dependent on the nozzle characteristics, Weber number and Reynolds number. The spatial variation of diameter and velocity arises principally due to primary breakup of liquid films and subsequent secondary breakup of large droplets due to aerodynamic shear. Downstream of the nozzle, coalescence of droplets due to collision was also found to be significant. Different types of liquid film breakup were considered and found to match well with the theory. Secondary breakup due to shear was also studied theoretically and compared to the experimental data. Coalescence probability at different axial and radial locations was computed to explain the experimental results. The spray is subdivided into three zones: near the nozzle, a zone consisting of film and ligament regime, where primary breakup and some secondary breakup take place; a second zone where the secondary breakup process continues, but weakens, and the centrifugal dispersion becomes dominant; and a third zone away from the spray where coalescence is dominant. Each regime has been analyzed in detail, characterized by timescale and Weber number and validated using experimental data.

Bayesian estimation inherent in a Mexican-hat-type neural network

Science.gov (United States)

Takiyama, Ken

2016-05-01

Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

GPU-accelerated low-latency real-time searches for gravitational waves from compact binary coalescence

International Nuclear Information System (INIS)

Liu Yuan; Du Zhihui; Chung, Shin Kee; Hooper, Shaun; Blair, David; Wen Linqing

2012-01-01

We present a graphics processing unit (GPU)-accelerated time-domain low-latency algorithm to search for gravitational waves (GWs) from coalescing binaries of compact objects based on the summed parallel infinite impulse response (SPIIR) filtering technique. The aim is to facilitate fast detection of GWs with a minimum delay to allow prompt electromagnetic follow-up observations. To maximize the GPU acceleration, we apply an efficient batched parallel computing model that significantly reduces the number of synchronizations in SPIIR and optimizes the usage of the memory and hardware resource. Our code is tested on the CUDA ‘Fermi’ architecture in a GTX 480 graphics card and its performance is compared with a single core of Intel Core i7 920 (2.67 GHz). A 58-fold speedup is achieved while giving results in close agreement with the CPU implementation. Our result indicates that it is possible to conduct a full search for GWs from compact binary coalescence in real time with only one desktop computer equipped with a Fermi GPU card for the initial LIGO detectors which in the past required more than 100 CPUs. (paper)

GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence

Science.gov (United States)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Camp, Jordan B.;

GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence.

Science.gov (United States)

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Blair, C D; Blair, D G; Blair, R M; Bloemen, S; Bock, O; Boer, M; Bogaert, G; Bogan, C; Bohe, A; Bond, C; Bondu, F; Bonnand, R; Boom, B A; Bork, R; Boschi, V; Bose, S; Bouffanais, Y; Bozzi, A; Bradaschia, C; Brady, P R; Braginsky, V B; Branchesi, M; Brau, J E; Briant, T; Brillet, A; Brinkmann, M; Brisson, V; Brockill, P; Broida, J E; Brooks, A F; Brown, D A; Brown, D D; Brown, N M; Brunett, S; Buchanan, C C; Buikema, A; Bulik, T; Bulten, H J; Buonanno, A; Buskulic, D; Buy, C; Byer, R L; Cabero, M; Cadonati, L; Cagnoli, G; Cahillane, C; Calderón Bustillo, J; Callister, T; Calloni, E; Camp, J B; Cannon, K C; Cao, J; Capano, C D; Capocasa, E; Carbognani, F; Caride, S; Casanueva Diaz, J; Casentini, C; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Cella, G; Cepeda, C B; Cerboni Baiardi, L; Cerretani, G; Cesarini, E; Chamberlin, S J; Chan, M; Chao, S; Charlton, P; Chassande-Mottin, E; Cheeseboro, B D; Chen, H Y; Chen, Y; Cheng, C; Chincarini, A; Chiummo, A; Cho, H S; Cho, M; Chow, J H; Christensen, N; Chu, Q; Chua, S; Chung, S; Ciani, G; Clara, F; Clark, J A; Cleva, F; Coccia, E; Cohadon, P-F; Colla, A; Collette, C G; Cominsky, L; Constancio, M; Conte, A; Conti, L; Cook, D; Corbitt, T R; Cornish, N; Corsi, A; Cortese, S; Costa, C A; Coughlin, M W; Coughlin, S B; Coulon, J-P; Countryman, S T; Couvares, P; Cowan, E E; Coward, D M; Cowart, M J; Coyne, D C; Coyne, R; Craig, K; Creighton, J D E; Cripe, J; Crowder, S G; Cumming, A; Cunningham, L; Cuoco, E; Dal Canton, T; Danilishin, S L; D'Antonio, S; Danzmann, K; Darman, N S; Dasgupta, A; Da Silva Costa, C F; Dattilo, V; Dave, I; Davier, M; Davies, G S; Daw, E J; Day, R; De, S; DeBra, D; Debreczeni, G; Degallaix, J; De Laurentis, M; Deléglise, S; Del Pozzo, W; Denker, T; Dent, T; Dergachev, V; De Rosa, R; DeRosa, R T; DeSalvo, R; Devine, R C; Dhurandhar, S; Díaz, M C; Di Fiore, L; Di Giovanni, M; Di Girolamo, T; Di Lieto, A; Di Pace, S; Di Palma, I; Di Virgilio, A; Dolique, V; Donovan, F; Dooley, K L; Doravari, S; Douglas, R; Downes, T P; Drago, M; Drever, R W P; Driggers, J C; Ducrot, M; Dwyer, S E; Edo, T B; Edwards, M C; Effler, A; Eggenstein, H-B; Ehrens, P; Eichholz, J; Eikenberry, S S; Engels, W; Essick, R C; Etzel, T; Evans, M; Evans, T M; Everett, R; Factourovich, M; Fafone, V; Fair, H; Fairhurst, S; Fan, X; Fang, Q; Farinon, S; Farr, B; Farr, W M; Favata, M; Fays, M; Fehrmann, H; Fejer, M M; Fenyvesi, E; Ferrante, I; Ferreira, E C; Ferrini, F; Fidecaro, F; Fiori, I; Fiorucci, D; Fisher, R P; Flaminio, R; Fletcher, M; Fong, H; Fournier, J-D; Frasca, S; Frasconi, F; Frei, Z; Freise, A; Frey, R; Frey, V; Fritschel, P; Frolov, V V; Fulda, P; Fyffe, M; Gabbard, H A G; Gair, J R; Gammaitoni, L; Gaonkar, S G; Garufi, F; Gaur, G; Gehrels, N; Gemme, G; Geng, P; Genin, E; Gennai, A; George, J; Gergely, L; Germain, V; Ghosh, Abhirup; Ghosh, Archisman; Ghosh, S; Giaime, J A; Giardina, K D; Giazotto, A; Gill, K; Glaefke, A; Goetz, E; Goetz, R; Gondan, L; González, G; Gonzalez Castro, J M; Gopakumar, A; Gordon, N A; Gorodetsky, M L; Gossan, S E; Gosselin, M; Gouaty, R; Grado, A; Graef, C; Graff, P B; Granata, M; Grant, A; Gras, S; Gray, C; Greco, G; Green, A C; Groot, P; Grote, H; Grunewald, S; Guidi, G M; Guo, X; Gupta, A; Gupta, M K; Gushwa, K E; Gustafson, E K; Gustafson, R; Hacker, J J; Hall, B R; Hall, E D; Hamilton, H; Hammond, G; Haney, M; Hanke, M M; Hanks, J; Hanna, C; Hannam, M D; Hanson, J; Hardwick, T; Harms, J; Harry, G M; Harry, I W; Hart, M J; Hartman, M T; Haster, C-J; Haughian, K; Healy, J; Heidmann, A; Heintze, M C; Heitmann, H; Hello, P; Hemming, G; Hendry, M; Heng, I S; Hennig, J; Henry, J; Heptonstall, A W; Heurs, M; Hild, S; Hoak, D; Hofman, D; Holt, K; Holz, D E; Hopkins, P; Hough, J; Houston, E A; Howell, E J; Hu, Y M; Huang, S; Huerta, E A; Huet, D; Hughey, B; Husa, S; Huttner, S H; Huynh-Dinh, T; Indik, N; Ingram, D R; Inta, R; Isa, H N; Isac, J-M; Isi, M; Isogai, T; Iyer, B R; Izumi, K; Jacqmin, T; Jang, H; Jani, K; Jaranowski, P; Jawahar, S; Jian, L; Jiménez-Forteza, F; Johnson, W W; Johnson-McDaniel, N K; Jones, D I; Jones, R; Jonker, R J G; Ju, L; K, Haris; Kalaghatgi, C V; Kalogera, V; Kandhasamy, S; Kang, G; Kanner, J B; Kapadia, S J; Karki, S; Karvinen, K S; Kasprzack, M; Katsavounidis, E; Katzman, W; Kaufer, S; Kaur, T; Kawabe, K; Kéfélian, F; Kehl, M S; Keitel, D; Kelley, D B; Kells, W; Kennedy, R; Key, J S; Khalili, F Y; Khan, I; Khan, S; Khan, Z; Khazanov, E A; Kijbunchoo, N; Kim, Chi-Woong; Kim, Chunglee; Kim, J; Kim, K; Kim, N; Kim, W; Kim, Y-M; Kimbrell, S J; King, E J; King, P J; Kissel, J S; Klein, B; Kleybolte, L; Klimenko, S; Koehlenbeck, S M; Koley, S; Kondrashov, V; Kontos, A; Korobko, M; Korth, W Z; Kowalska, I; Kozak, D B; Kringel, V; Krishnan, B; Królak, A; Krueger, C; Kuehn, G; Kumar, P; Kumar, R; Kuo, L; Kutynia, A; Lackey, B D; Landry, M; Lange, J; Lantz, B; Lasky, P D; Laxen, M; Lazzarini, A; Lazzaro, C; Leaci, P; Leavey, S; Lebigot, E O; Lee, C H; Lee, H K; Lee, H M; Lee, K; Lenon, A; Leonardi, M; Leong, J R; Leroy, N; Letendre, N; Levin, Y; Lewis, J B; Li, T G F; Libson, A; Littenberg, T B; Lockerbie, N A; Lombardi, A L; London, L T; Lord, J E; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lough, J D; Lousto, C O; Lück, H; Lundgren, A P; Lynch, R; Ma, Y; Machenschalk, B; MacInnis, M; Macleod, D M; Magaña-Sandoval, F; Magaña Zertuche, L; Magee, R M; Majorana, E; Maksimovic, I; Malvezzi, V; Man, N; Mandel, I; Mandic, V; Mangano, V; Mansell, G L; Manske, M; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markosyan, A S; Maros, E; Martelli, F; Martellini, L; Martin, I W; Martynov, D V; Marx, J N; Mason, K; Masserot, A; Massinger, T J; Masso-Reid, M; Mastrogiovanni, S; Matichard, F; Matone, L; Mavalvala, N; Mazumder, N; McCarthy, R; McClelland, D E; McCormick, S; McGuire, S C; McIntyre, G; McIver, J; McManus, D J; McRae, T; McWilliams, S T; Meacher, D; Meadors, G D; Meidam, J; Melatos, A; Mendell, G; Mercer, R A; Merilh, E L; Merzougui, M; Meshkov, S; Messenger, C; Messick, C; Metzdorff, R; Meyers, P M; Mezzani, F; Miao, H; Michel, C; Middleton, H; Mikhailov, E E; Milano, L; Miller, A L; Miller, A; Miller, B B; Miller, J; Millhouse, M; Minenkov, Y; Ming, J; Mirshekari, S; Mishra, C; Mitra, S; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Moggi, A; Mohan, M; Mohapatra, S R P; Montani, M; Moore, B C; Moore, C J; Moraru, D; Moreno, G; Morriss, S R; Mossavi, K; Mours, B; Mow-Lowry, C M; Mueller, G; Muir, A W; Mukherjee, Arunava; Mukherjee, D; Mukherjee, S; Mukund, N; Mullavey, A; Munch, J; Murphy, D J; Murray, P G; Mytidis, A; Nardecchia, I; Naticchioni, L; Nayak, R K; Nedkova, K; Nelemans, G; Nelson, T J N; Neri, M; Neunzert, A; Newton, G; Nguyen, T T; Nielsen, A B; Nissanke, S; Nitz, A; Nocera, F; Nolting, D; Normandin, M E N; Nuttall, L K; Oberling, J; Ochsner, E; O'Dell, J; Oelker, E; Ogin, G H; Oh, J J; Oh, S H; Ohme, F; Oliver, M; Oppermann, P; Oram, Richard J; O'Reilly, B; O'Shaughnessy, R; Ottaway, D J; Overmier, H; Owen, B J; Pai, A; Pai, S A; Palamos, J R; Palashov, O; Palomba, C; Pal-Singh, A; Pan, H; Pankow, C; Pannarale, F; Pant, B C; Paoletti, F; Paoli, A; Papa, M A; Paris, H R; Parker, W; Pascucci, D; Pasqualetti, A; Passaquieti, R; Passuello, D; Patricelli, B; Patrick, Z; Pearlstone, B L; Pedraza, M; Pedurand, R; Pekowsky, L; Pele, A; Penn, S; Perreca, A; Perri, L M; Pfeiffer, H P; Phelps, M; Piccinni, O J; Pichot, M; Piergiovanni, F; Pierro, V; Pillant, G; Pinard, L; Pinto, I M; Pitkin, M; Poe, M; Poggiani, R; Popolizio, P; Post, A; Powell, J; Prasad, J; Predoi, V; Prestegard, T; Price, L R; Prijatelj, M; Principe, M; Privitera, S; Prix, R; Prodi, G A; Prokhorov, L; Puncken, O; Punturo, M; Puppo, P; Pürrer, M; Qi, H; Qin, J; Qiu, S; Quetschke, V; Quintero, E A; Quitzow-James, R; Raab, F J; Rabeling, D S; Radkins, H; Raffai, P; Raja, S; Rajan, C; Rakhmanov, M; Rapagnani, P; Raymond, V; Razzano, M; Re, V; Read, J; Reed, C M; Regimbau, T; Rei, L; Reid, S; Reitze, D H; Rew, H; Reyes, S D; Ricci, F; Riles, K; Rizzo, M; Robertson, N A; Robie, R; Robinet, F; Rocchi, A; Rolland, L; Rollins, J G; Roma, V J; Romano, J D; Romano, R; Romanov, G; Romie, J H; Rosińska, D; Rowan, S; Rüdiger, A; Ruggi, P; Ryan, K; Sachdev, S; Sadecki, T; Sadeghian, L; Sakellariadou, M; Salconi, L; Saleem, M; Salemi, F; Samajdar, A; Sammut, L; Sanchez, E J; Sandberg, V; Sandeen, B; Sanders, J R; Sassolas, B; Sathyaprakash, B S; Saulson, P R; Sauter, O E S; Savage, R L; Sawadsky, A; Schale, P; Schilling, R; Schmidt, J; Schmidt, P; Schnabel, R; Schofield, R M S; Schönbeck, A; Schreiber, E; Schuette, D; Schutz, B F; Scott, J; Scott, S M; Sellers, D; Sengupta, A S; Sentenac, D; Sequino, V; Sergeev, A; Setyawati, Y; Shaddock, D A; Shaffer, T; Shahriar, M S; Shaltev, M; Shapiro, B; Shawhan, P; Sheperd, A; Shoemaker, D H; Shoemaker, D M; Siellez, K; Siemens, X; Sieniawska, M; Sigg, D; Silva, A D; Singer, A; Singer, L P; Singh, A; Singh, R; Singhal, A; Sintes, A M; Slagmolen, B J J; Smith, J R; Smith, N D; Smith, R J E; Son, E J; Sorazu, B; Sorrentino, F; Souradeep, T; Srivastava, A K; Staley, A; Steinke, M; Steinlechner, J; Steinlechner, S; Steinmeyer, D; Stephens, B C; Stevenson, S P; Stone, R; Strain, K A; Straniero, N; Stratta, G; Strauss, N A; Strigin, S; Sturani, R; Stuver, A L; Summerscales, T Z; Sun, L; Sunil, S; Sutton, P J; Swinkels, B L; Szczepańczyk, M J; Tacca, M; Talukder, D; Tanner, D B; Tápai, M; Tarabrin, S P; Taracchini, A; Taylor, R; Theeg, T; Thirugnanasambandam, M P; Thomas, E G; Thomas, M; Thomas, P; Thorne, K A; Thrane, E; Tiwari, S; Tiwari, V; Tokmakov, K V; Toland, K; Tomlinson, C; Tonelli, M; Tornasi, Z; Torres, C V; Torrie, C I; Töyrä, D; Travasso, F; Traylor, G; Trifirò, D; Tringali, M C; Trozzo, L; Tse, M; Turconi, M; Tuyenbayev, D; Ugolini, D; Unnikrishnan, C S; Urban, A L; Usman, S A; Vahlbruch, H; Vajente, G; Valdes, G; Vallisneri, M; van Bakel, N; van Beuzekom, M; van den Brand, J F J; Van Den Broeck, C; Vander-Hyde, D C; van der Schaaf, L; van Heijningen, J V; van Veggel, A A; Vardaro, M; Vass, S; Vasúth, M; Vaulin, R; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Verkindt, D; Vetrano, F; Viceré, A; Vinciguerra, S; Vine, D J; Vinet, J-Y; Vitale, S; Vo, T; Vocca, H; Vorvick, C; Voss, D V; Vousden, W D; Vyatchanin, S P; Wade, A R; Wade, L E; Wade, M; Walker, M; Wallace, L; Walsh, S; Wang, G; Wang, H; Wang, M; Wang, X; Wang, Y; Ward, R L; Warner, J; Was, M; Weaver, B; Wei, L-W; Weinert, M; Weinstein, A J; Weiss, R; Wen, L; Weßels, P; Westphal, T; Wette, K; Whelan, J T; Whiting, B F; Williams, R D; Williamson, A R; Willis, J L; Willke, B; Wimmer, M H; Winkler, W; Wipf, C C; Wittel, H; Woan, G; Woehler, J; Worden, J; Wright, J L; Wu, D S; Wu, G; Yablon, J; Yam, W; Yamamoto, H; Yancey, C C; Yu, H; Yvert, M; Zadrożny, A; Zangrando, L; Zanolin, M; Zendri, J-P; Zevin, M; Zhang, L; Zhang, M; Zhang, Y; Zhao, C; Zhou, M; Zhou, Z; Zhu, X J; Zucker, M E; Zuraw, S E; Zweizig, J; Boyle, M; Hemberger, D; Kidder, L E; Lovelace, G; Ossokine, S; Scheel, M; Szilagyi, B; Teukolsky, S

2016-06-17

We report the observation of a gravitational-wave signal produced by the coalescence of two stellar-mass black holes. The signal, GW151226, was observed by the twin detectors of the Laser Interferometer Gravitational-Wave Observatory (LIGO) on December 26, 2015 at 03:38:53 UTC. The signal was initially identified within 70 s by an online matched-filter search targeting binary coalescences. Subsequent off-line analyses recovered GW151226 with a network signal-to-noise ratio of 13 and a significance greater than 5σ. The signal persisted in the LIGO frequency band for approximately 1 s, increasing in frequency and amplitude over about 55 cycles from 35 to 450 Hz, and reached a peak gravitational strain of 3.4_{-0.9}^{+0.7}×10^{-22}. The inferred source-frame initial black hole masses are 14.2_{-3.7}^{+8.3}M_{⊙} and 7.5_{-2.3}^{+2.3}M_{⊙}, and the final black hole mass is 20.8_{-1.7}^{+6.1}M_{⊙}. We find that at least one of the component black holes has spin greater than 0.2. This source is located at a luminosity distance of 440_{-190}^{+180}  Mpc corresponding to a redshift of 0.09_{-0.04}^{+0.03}. All uncertainties define a 90% credible interval. This second gravitational-wave observation provides improved constraints on stellar populations and on deviations from general relativity.

GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence

Science.gov (United States)

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A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rajan, C.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Reed, C. M.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Ricci, F.; Riles, K.; Rizzo, M.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, J. D.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Sakellariadou, M.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sanchez, E. J.; Sandberg, V.; Sandeen, B.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O. E. S.; Savage, R. L.; Sawadsky, A.; Schale, P.; Schilling, R.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Scott, J.; Scott, S. M.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Setyawati, Y.; Shaddock, D. A.; Shaffer, T.; Shahriar, M. S.; Shaltev, M.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sieniawska, M.; Sigg, D.; Silva, A. D.; Singer, A.; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, J. R.; Smith, N. D.; Smith, R. J. E.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stevenson, S. P.; Stone, R.; Strain, K. A.; Straniero, N.; Stratta, G.; Strauss, N. A.; Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sunil, S.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tápai, M.; Tarabrin, S. P.; Taracchini, A.; Taylor, R.; Theeg, T.; Thirugnanasambandam, M. P.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Toland, K.; Tomlinson, C.; Tonelli, M.; Tornasi, Z.; Torres, C. V.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifirò, D.; Tringali, M. C.; Trozzo, L.; Tse, M.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; Vallisneri, M.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Vetrano, F.; Viceré, A.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D. V.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, X.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Wen, L.; Weßels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; Whiting, B. F.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Woehler, J.; Worden, J.; Wright, J. L.; Wu, D. S.; Wu, G.; Yablon, J.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yu, H.; Yvert, M.; Zadrożny, A.; Zangrando, L.; Zanolin, M.; Zendri, J.-P.; Zevin, M.; Zhang, L.; Zhang, M.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, X. J.; Zucker, M. E.; Zuraw, S. E.; Zweizig, J.; Boyle, M.; Hemberger, D.; Kidder, L. E.; Lovelace, G.; Ossokine, S.; Scheel, M.; Szilagyi, B.; Teukolsky, S.; LIGO Scientific Collaboration; VIRGO Collaboration

2016-06-01

We report the observation of a gravitational-wave signal produced by the coalescence of two stellar-mass black holes. The signal, GW151226, was observed by the twin detectors of the Laser Interferometer Gravitational-Wave Observatory (LIGO) on December 26, 2015 at 03:38:53 UTC. The signal was initially identified within 70 s by an online matched-filter search targeting binary coalescences. Subsequent off-line analyses recovered GW151226 with a network signal-to-noise ratio of 13 and a significance greater than 5 σ . The signal persisted in the LIGO frequency band for approximately 1 s, increasing in frequency and amplitude over about 55 cycles from 35 to 450 Hz, and reached a peak gravitational strain of 3. 4-0.9+0.7×10-22 . The inferred source-frame initial black hole masses are 14.2-3.7+8.3 M⊙ and 7. 5-2.3+2.3 M⊙, and the final black hole mass is 20.8-1.7+6.1 M⊙. We find that at least one of the component black holes has spin greater than 0.2. This source is located at a luminosity distance of 44 0-190+180 Mpc corresponding to a redshift of 0.0 9-0.04+0.03. All uncertainties define a 90% credible interval. This second gravitational-wave observation provides improved constraints on stellar populations and on deviations from general relativity.

Bayesian Sampling using Condition Indicators

DEFF Research Database (Denmark)

Faber, Michael H.; Sørensen, John Dalsgaard

2002-01-01

of condition indicators introduced by Benjamin and Cornell (1970) a Bayesian approach to quality control is formulated. The formulation is then extended to the case where the quality control is based on sampling of indirect information about the condition of the components, i.e. condition indicators...

Improved phylogenetic analyses corroborate a plausible position of Martialis heureka in the ant tree of life.

Directory of Open Access Journals (Sweden)

Patrick Kück

Full Text Available Martialinae are pale, eyeless and probably hypogaeic predatory ants. Morphological character sets suggest a close relationship to the ant subfamily Leptanillinae. Recent analyses based on molecular sequence data suggest that Martialinae are the sister group to all extant ants. However, by comparing molecular studies and different reconstruction methods, the position of Martialinae remains ambiguous. While this sister group relationship was well supported by Bayesian partitioned analyses, Maximum Likelihood approaches could not unequivocally resolve the position of Martialinae. By re-analysing a previous published molecular data set, we show that the Maximum Likelihood approach is highly appropriate to resolve deep ant relationships, especially between Leptanillinae, Martialinae and the remaining ant subfamilies. Based on improved alignments, alignment masking, and tree reconstructions with a sufficient number of bootstrap replicates, our results strongly reject a placement of Martialinae at the first split within the ant tree of life. Instead, we suggest that Leptanillinae are a sister group to all other extant ant subfamilies, whereas Martialinae branch off as a second lineage. This assumption is backed by approximately unbiased (AU tests, additional Bayesian analyses and split networks. Our results demonstrate clear effects of improved alignment approaches, alignment masking and data partitioning. We hope that our study illustrates the importance of thorough, comprehensible phylogenetic analyses using the example of ant relationships.

Optimal Detection under the Restricted Bayesian Criterion

Directory of Open Access Journals (Sweden)

Shujun Liu

2017-07-01

Full Text Available This paper aims to find a suitable decision rule for a binary composite hypothesis-testing problem with a partial or coarse prior distribution. To alleviate the negative impact of the information uncertainty, a constraint is considered that the maximum conditional risk cannot be greater than a predefined value. Therefore, the objective of this paper becomes to find the optimal decision rule to minimize the Bayes risk under the constraint. By applying the Lagrange duality, the constrained optimization problem is transformed to an unconstrained optimization problem. In doing so, the restricted Bayesian decision rule is obtained as a classical Bayesian decision rule corresponding to a modified prior distribution. Based on this transformation, the optimal restricted Bayesian decision rule is analyzed and the corresponding algorithm is developed. Furthermore, the relation between the Bayes risk and the predefined value of the constraint is also discussed. The Bayes risk obtained via the restricted Bayesian decision rule is a strictly decreasing and convex function of the constraint on the maximum conditional risk. Finally, the numerical results including a detection example are presented and agree with the theoretical results.

A two-stage Bayesian design with sample size reestimation and subgroup analysis for phase II binary response trials.

Science.gov (United States)

Zhong, Wei; Koopmeiners, Joseph S; Carlin, Bradley P

2013-11-01

Frequentist sample size determination for binary outcome data in a two-arm clinical trial requires initial guesses of the event probabilities for the two treatments. Misspecification of these event rates may lead to a poor estimate of the necessary sample size. In contrast, the Bayesian approach that considers the treatment effect to be random variable having some distribution may offer a better, more flexible approach. The Bayesian sample size proposed by (Whitehead et al., 2008) for exploratory studies on efficacy justifies the acceptable minimum sample size by a "conclusiveness" condition. In this work, we introduce a new two-stage Bayesian design with sample size reestimation at the interim stage. Our design inherits the properties of good interpretation and easy implementation from Whitehead et al. (2008), generalizes their method to a two-sample setting, and uses a fully Bayesian predictive approach to reduce an overly large initial sample size when necessary. Moreover, our design can be extended to allow patient level covariates via logistic regression, now adjusting sample size within each subgroup based on interim analyses. We illustrate the benefits of our approach with a design in non-Hodgkin lymphoma with a simple binary covariate (patient gender), offering an initial step toward within-trial personalized medicine. Copyright © 2013 Elsevier Inc. All rights reserved.

Inferring Phylogenetic Networks Using PhyloNet.

Science.gov (United States)

Wen, Dingqiao; Yu, Yun; Zhu, Jiafan; Nakhleh, Luay

2018-07-01

PhyloNet was released in 2008 as a software package for representing and analyzing phylogenetic networks. At the time of its release, the main functionalities in PhyloNet consisted of measures for comparing network topologies and a single heuristic for reconciling gene trees with a species tree. Since then, PhyloNet has grown significantly. The software package now includes a wide array of methods for inferring phylogenetic networks from data sets of unlinked loci while accounting for both reticulation (e.g., hybridization) and incomplete lineage sorting. In particular, PhyloNet now allows for maximum parsimony, maximum likelihood, and Bayesian inference of phylogenetic networks from gene tree estimates. Furthermore, Bayesian inference directly from sequence data (sequence alignments or biallelic markers) is implemented. Maximum parsimony is based on an extension of the "minimizing deep coalescences" criterion to phylogenetic networks, whereas maximum likelihood and Bayesian inference are based on the multispecies network coalescent. All methods allow for multiple individuals per species. As computing the likelihood of a phylogenetic network is computationally hard, PhyloNet allows for evaluation and inference of networks using a pseudolikelihood measure. PhyloNet summarizes the results of the various analyzes and generates phylogenetic networks in the extended Newick format that is readily viewable by existing visualization software.

A Bayesian approach to the evaluation of risk-based microbiological criteria for Campylobacter in broiler meat

DEFF Research Database (Denmark)

Ranta, Jukka; Lindqvist, Roland; Hansson, Ingrid

2015-01-01

Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation...... of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference...

Reuse, Recycle, Reweigh: Combating Influenza through Efficient Sequential Bayesian Computation for Massive Data

OpenAIRE

Tom, Jennifer A.; Sinsheimer, Janet S.; Suchard, Marc A.

2010-01-01

Massive datasets in the gigabyte and terabyte range combined with the availability of increasingly sophisticated statistical tools yield analyses at the boundary of what is computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses, removed from the context that justified the initial data collection. In a Bayesian framework, these stratified analyses generate intermediate realizations, of...

Using literature and data to learn Bayesian networks as clinical models of ovarian tumors.

Science.gov (United States)

Antal, Peter; Fannes, Geert; Timmerman, Dirk; Moreau, Yves; De Moor, Bart

2004-03-01

Thanks to its increasing availability, electronic literature has become a potential source of information for the development of complex Bayesian networks (BN), when human expertise is missing or data is scarce or contains much noise. This opportunity raises the question of how to integrate information from free-text resources with statistical data in learning Bayesian networks. Firstly, we report on the collection of prior information resources in the ovarian cancer domain, which includes "kernel" annotations of the domain variables. We introduce methods based on the annotations and literature to derive informative pairwise dependency measures, which are derived from the statistical cooccurrence of the names of the variables, from the similarity of the "kernel" descriptions of the variables and from a combined method. We perform wide-scale evaluation of these text-based dependency scores against an expert reference and against data scores (the mutual information (MI) and a Bayesian score). Next, we transform the text-based dependency measures into informative text-based priors for Bayesian network structures. Finally, we report the benefit of such informative text-based priors on the performance of a Bayesian network for the classification of ovarian tumors from clinical data.

Applying Bayesian Statistics to Educational Evaluation. Theoretical Paper No. 62.

Science.gov (United States)

Brumet, Michael E.

Bayesian statistical inference is unfamiliar to many educational evaluators. While the classical model is useful in educational research, it is not as useful in evaluation because of the need to identify solutions to practical problems based on a wide spectrum of information. The reason Bayesian analysis is effective for decision making is that it…

Van der Waals Attraction and Coalescence of Aqueous Salt Nanodroplets

Czech Academy of Sciences Publication Activity Database

Jungwirth, Pavel; Buch, V.

2003-01-01

Roč. 68, č. 12 (2003), s. 2283-2291 ISSN 0010-0765 R&D Projects: GA MŠk LN00A032 Institutional research plan: CEZ:AV0Z4040901 Keywords : van der Waals interactions * aqueous droplets * coalescence Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 1.041, year: 2003

Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain

KAUST Repository

Beck, Joakim

2018-02-19

In calculating expected information gain in optimal Bayesian experimental design, the computation of the inner loop in the classical double-loop Monte Carlo requires a large number of samples and suffers from underflow if the number of samples is small. These drawbacks can be avoided by using an importance sampling approach. We present a computationally efficient method for optimal Bayesian experimental design that introduces importance sampling based on the Laplace method to the inner loop. We derive the optimal values for the method parameters in which the average computational cost is minimized for a specified error tolerance. We use three numerical examples to demonstrate the computational efficiency of our method compared with the classical double-loop Monte Carlo, and a single-loop Monte Carlo method that uses the Laplace approximation of the return value of the inner loop. The first demonstration example is a scalar problem that is linear in the uncertain parameter. The second example is a nonlinear scalar problem. The third example deals with the optimal sensor placement for an electrical impedance tomography experiment to recover the fiber orientation in laminate composites.

Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain

Science.gov (United States)

Beck, Joakim; Dia, Ben Mansour; Espath, Luis F. R.; Long, Quan; Tempone, Raúl

2018-06-01

In calculating expected information gain in optimal Bayesian experimental design, the computation of the inner loop in the classical double-loop Monte Carlo requires a large number of samples and suffers from underflow if the number of samples is small. These drawbacks can be avoided by using an importance sampling approach. We present a computationally efficient method for optimal Bayesian experimental design that introduces importance sampling based on the Laplace method to the inner loop. We derive the optimal values for the method parameters in which the average computational cost is minimized according to the desired error tolerance. We use three numerical examples to demonstrate the computational efficiency of our method compared with the classical double-loop Monte Carlo, and a more recent single-loop Monte Carlo method that uses the Laplace method as an approximation of the return value of the inner loop. The first example is a scalar problem that is linear in the uncertain parameter. The second example is a nonlinear scalar problem. The third example deals with the optimal sensor placement for an electrical impedance tomography experiment to recover the fiber orientation in laminate composites.

A Bayesian approach to degradation-based burn-in optimization for display products exhibiting two-phase degradation patterns

International Nuclear Information System (INIS)

Yuan, Tao; Bae, Suk Joo; Zhu, Xiaoyan

2016-01-01

Motivated by the two-phase degradation phenomena observed in light displays (e.g., plasma display panels (PDPs), organic light emitting diodes (OLEDs)), this study proposes a new degradation-based burn-in testing plan for display products exhibiting two-phase degradation patterns. The primary focus of the burn-in test in this study is to eliminate the initial rapid degradation phase, while the major purpose of traditional burn-in tests is to detect and eliminate early failures from weak units. A hierarchical Bayesian bi-exponential model is used to capture two-phase degradation patterns of the burn-in population. Mission reliability and total cost are introduced as planning criteria. The proposed burn-in approach accounts for unit-to-unit variability within the burn-in population, and uncertainty concerning the model parameters, mainly in the hierarchical Bayesian framework. Available pre-burn-in data is conveniently incorporated into the burn-in decision-making procedure. A practical example of PDP degradation data is used to illustrate the proposed methodology. The proposed method is compared to other approaches such as the maximum likelihood method or the change-point regression. - Highlights: • We propose a degradation-based burn-in test for products with two-phase degradation. • Mission reliability and total cost are used as planning criteria. • The proposed burn-in approach is built within the hierarchical Bayesian framework. • A practical example was used to illustrate the proposed methodology.

Quantum Bayesian rule for weak measurements of qubits in superconducting circuit QED

International Nuclear Information System (INIS)

Wang, Peiyue; Qin, Lupei; Li, Xin-Qi

2014-01-01

Compared with the quantum trajectory equation (QTE), the quantum Bayesian approach has the advantage of being more efficient to infer a quantum state under monitoring, based on the integrated output of measurements. For weak measurement of qubits in circuit quantum electrodynamics (cQED), properly accounting for the measurement backaction effects within the Bayesian framework is an important problem of current interest. Elegant work towards this task was carried out by Korotkov in ‘bad-cavity’ and weak-response limits (Korotkov 2011 Quantum Bayesian approach to circuit QED measurement (arXiv:1111.4016)). In the present work, based on insights from the cavity-field states (dynamics) and the help of an effective QTE, we generalize the results of Korotkov to more general system parameters. The obtained Bayesian rule is in full agreement with Korotkov's result in limiting cases and as well holds satisfactory accuracy in non-limiting cases in comparison with the QTE simulations. We expect the proposed Bayesian rule to be useful for future cQED measurement and control experiments. (paper)

Banking Crisis Early Warning Model based on a Bayesian Model Averaging Approach

Directory of Open Access Journals (Sweden)

Taha Zaghdoudi

2016-08-01

Full Text Available The succession of banking crises in which most have resulted in huge economic and financial losses, prompted several authors to study their determinants. These authors constructed early warning models to prevent their occurring. It is in this same vein as our study takes its inspiration. In particular, we have developed a warning model of banking crises based on a Bayesian approach. The results of this approach have allowed us to identify the involvement of the decline in bank profitability, deterioration of the competitiveness of the traditional intermediation, banking concentration and higher real interest rates in triggering bank crisis.

Bayesian Inference of a Multivariate Regression Model

Directory of Open Access Journals (Sweden)

Marick S. Sinay

2014-01-01

Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.

Study of statistical properties of hybrid statistic in coherent multi-detector compact binary coalescences Search

OpenAIRE

Haris, K; Pai, Archana

2015-01-01