DEFF Research Database (Denmark)
Pires, Sara Monteiro; Hald, Tine
2010-01-01
Salmonella is a major cause of human gastroenteritis worldwide. To prioritize interventions and assess the effectiveness of efforts to reduce illness, it is important to attribute salmonellosis to the responsible sources. Studies have suggested that some Salmonella subtypes have a higher health...... impact than others. Likewise, some food sources appear to have a higher impact than others. Knowledge of variability in the impact of subtypes and sources may provide valuable added information for research, risk management, and public health strategies. We developed a Bayesian model that attributes...... illness to specific sources and allows for a better estimation of the differences in the ability of Salmonella subtypes and food types to result in reported salmonellosis. The model accommodates data for multiple years and is based on the Danish Salmonella surveillance. The number of sporadic cases caused...
Salmonella source attribution based on microbial subtyping
DEFF Research Database (Denmark)
Barco, Lisa; Barrucci, Federica; Olsen, John Elmerdahl
2013-01-01
Source attribution of cases of food-borne disease represents a valuable tool for identifying and prioritizing effective food-safety interventions. Microbial subtyping is one of the most common methods to infer potential sources of human food-borne infections. So far, Salmonella microbial subtyping...... source attribution through microbial subtyping approach. It summarizes the available microbial subtyping attribution models and discusses the use of conventional phenotypic typing methods, as well as of the most commonly applied molecular typing methods in the European Union (EU) laboratories...
New paradigms for Salmonella source attribution based on microbial subtyping.
Mughini-Gras, Lapo; Franz, Eelco; van Pelt, Wilfrid
Microbial subtyping is the most common approach for Salmonella source attribution. Typically, attributions are computed using frequency-matching models like the Dutch and Danish models based on phenotyping data (serotyping, phage-typing, and antimicrobial resistance profiling). Herewith, we
New paradigms for Salmonella source attribution based on microbial subtyping.
Mughini-Gras, Lapo; Franz, Eelco; van Pelt, Wilfrid
2018-05-01
Microbial subtyping is the most common approach for Salmonella source attribution. Typically, attributions are computed using frequency-matching models like the Dutch and Danish models based on phenotyping data (serotyping, phage-typing, and antimicrobial resistance profiling). Herewith, we critically review three major paradigms facing Salmonella source attribution today: (i) the use of genotyping data, particularly Multi-Locus Variable Number of Tandem Repeats Analysis (MLVA), which is replacing traditional Salmonella phenotyping beyond serotyping; (ii) the integration of case-control data into source attribution to improve risk factor identification/characterization; (iii) the investigation of non-food sources, as attributions tend to focus on foods of animal origin only. Population genetics models or simplified MLVA schemes may provide feasible options for source attribution, although there is a strong need to explore novel modelling options as we move towards whole-genome sequencing as the standard. Classical case-control studies are enhanced by incorporating source attribution results, as individuals acquiring salmonellosis from different sources have different associated risk factors. Thus, the more such analyses are performed the better Salmonella epidemiology will be understood. Reparametrizing current models allows for inclusion of sources like reptiles, the study of which improves our understanding of Salmonella epidemiology beyond food to tackle the pathogen in a more holistic way. Copyright © 2017 Elsevier Ltd. All rights reserved.
Bayesian predictive risk modeling of microbial criteria for Campylobacter in broilers
DEFF Research Database (Denmark)
Nauta, Maarten; Ranta, J.; Mikkelä, A.
Microbial Criteria define the acceptability of food products, based on the presence or detected number of microorganisms in samples. The criteria are applied at the level of defined food lots. Generally, these are interpreted as statistical batches representing the production [1]. The batches...... be assessed by computing posterior distribution of the parameters - a Bayesian evidence synthesis. The outcome of a defined Microbial Criterion (MC) for a batch provides additional evidence concerning the batch. Posterior predictive consumer risk (probability of illness) was computed for such batch...
Lawson, Daniel J; Holtrop, Grietje; Flint, Harry
2011-07-01
Process models specified by non-linear dynamic differential equations contain many parameters, which often must be inferred from a limited amount of data. We discuss a hierarchical Bayesian approach combining data from multiple related experiments in a meaningful way, which permits more powerful inference than treating each experiment as independent. The approach is illustrated with a simulation study and example data from experiments replicating the aspects of the human gut microbial ecosystem. A predictive model is obtained that contains prediction uncertainty caused by uncertainty in the parameters, and we extend the model to capture situations of interest that cannot easily be studied experimentally. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
DEFF Research Database (Denmark)
Lin, Lin; Chan, Cliburn; Hadrup, Sine R
2013-01-01
subtype identification in this novel, general model framework, and provide a detailed example using simulated data. We then describe application to a data set from an experimental study of antigen-specific T-cell subtyping using combinatorially encoded assays in human blood samples. Summary comments...... profiling in many biological areas, traditional flow cytometry measures relative levels of abundance of marker proteins using fluorescently labeled tags that identify specific markers by a single-color. One specific and important recent development in this area is the use of combinatorial marker assays...
Salmonella Source Attribution in Japan by a Microbiological Subtyping Approach
DEFF Research Database (Denmark)
Toyofuku, Hajime; Pires, Sara Monteiro; Hald, Tine
2011-01-01
In order to estimate the number of human Salmonella infections attributable to each of major animal-food source, and help identifying the best Salmonella intervention strategies, a microbial subtyping approach for source attribution was applied. We adapted a Bayesian model that attributes illnesses......-food sources, subtype-related factors, and source-related factors. National-surveillance serotyping data from 1998 to 2007 were applied to the model. Results suggested that the relative contribution of the sources to salmonellosis varied during the 10 year period, and that eggs are the most important source...... to specific sources and allows for the estimation of the differences in the ability of Salmonella subtypes and food types to result in reported salmonellosis. The number of human cases caused by different Salmonella subtypes is estimated as a function of the prevalence of these subtypes in the animal...
Schmidt, P J; Pintar, K D M; Fazil, A M; Flemming, C A; Lanthier, M; Laprade, N; Sunohara, M D; Simhon, A; Thomas, J L; Topp, E; Wilkes, G; Lapen, D R
2013-06-15
Human campylobacteriosis is the leading bacterial gastrointestinal illness in Canada; environmental transmission has been implicated in addition to transmission via consumption of contaminated food. Information about Campylobacter spp. occurrence at the watershed scale will enhance our understanding of the associated public health risks and the efficacy of source water protection strategies. The overriding purpose of this study is to provide a quantitative framework to assess and compare the relative public health significance of watershed microbial water quality associated with agricultural BMPs. A microbial monitoring program was expanded from fecal indicator analyses and Campylobacter spp. presence/absence tests to the development of a novel, 11-tube most probable number (MPN) method that targeted Campylobacter jejuni, Campylobacter coli, and Campylobacter lari. These three types of data were used to make inferences about theoretical risks in a watershed in which controlled tile drainage is widely practiced, an adjacent watershed with conventional (uncontrolled) tile drainage, and reference sites elsewhere in the same river basin. E. coli concentrations (MPN and plate count) in the controlled tile drainage watershed were statistically higher (2008-11), relative to the uncontrolled tile drainage watershed, but yearly variation was high as well. Escherichia coli loading for years 2008-11 combined were statistically higher in the controlled watershed, relative to the uncontrolled tile drainage watershed, but Campylobacter spp. loads for 2010-11 were generally higher for the uncontrolled tile drainage watershed (but not statistically significant). Using MPN data and a Bayesian modelling approach, higher mean Campylobacter spp. concentrations were found in the controlled tile drainage watershed relative to the uncontrolled tile drainage watershed (2010, 2011). A second-order quantitative microbial risk assessment (QMRA) was used, in a relative way, to identify
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
Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6
Directory of Open Access Journals (Sweden)
Dan B. Jensen
2013-09-01
Full Text Available Background: Prediction of the optimal habitat conditions for a given bacterium, based on genome sequence alone would be of value for scientific as well as industrial purposes. One example of such a habitat adaptation is the requirement for oxygen. In spite of good genome data availability, there have been only a few prediction attempts of bacterial oxygen requirements, using genome sequences. Here, we describe a method for distinguishing aerobic, anaerobic and facultative anaerobic bacteria, based on genome sequence-derived input, using naive Bayesian inference. In contrast, other studies found in literature only demonstrate the ability to distinguish two classes at a time. Results: The results shown in the present study are as good as or better than comparable methods previously described in the scientific literature, with an arguably simpler method, when results are directly compared. This method further compares the performance of a single-step naive Bayesian prediction of the three included classifications, compared to a simple Bayesian network with two steps. A two-step network, distinguishing first respiring from non-respiring organisms, followed by the distinction of aerobe and facultative anaerobe organisms within the respiring group, is found to perform best. Conclusions: A simple naive Bayesian network based on the presence or absence of specific protein domains within a genome is an effective and easy way to predict bacterial habitat preferences, such as oxygen requirement.
ZHOU, Lin
1996-01-01
In this paper I consider social choices under uncertainty. I prove that any social choice rule that satisfies independence of irrelevant alternatives, translation invariance, and weak anonymity is consistent with ex post Bayesian utilitarianism
Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel
2013-01-01
Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean
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...
Introduction to Bayesian statistics
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...
Bayesian artificial intelligence
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
Bayesian artificial intelligence
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.
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...
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
Morphologic Subtypes of Hepatocellular Carcinoma.
Torbenson, Michael S
2017-06-01
Hepatocellular carcinomas can be further divided into distinct subtypes that provide important clinical information and biological insights. These subtypes are distinct from growth patterns and are on based on morphologic and molecular findings. There are 12 reasonably well-defined subtypes as well as 6 provisional subtypes, together making up 35% of all hepatocellular carcinomas. These subtypes are discussed, with an emphasis on their definitions and the key morphologic findings. Copyright © 2017 Elsevier Inc. All rights reserved.
HIV-1 subtype A gag variability and epitope evolution.
Abidi, Syed Hani; Kalish, Marcia L; Abbas, Farhat; Rowland-Jones, Sarah; Ali, Syed
2014-01-01
The aim of this study was to examine the course of time-dependent evolution of HIV-1 subtype A on a global level, especially with respect to the dynamics of immunogenic HIV gag epitopes. We used a total of 1,893 HIV-1 subtype A gag sequences representing a timeline from 1985 through 2010, and 19 different countries in Africa, Europe and Asia. The phylogenetic relationship of subtype A gag and its epidemic dynamics was analysed through a Maximum Likelihood tree and Bayesian Skyline plot, genomic variability was measured in terms of G → A substitutions and Shannon entropy, and the time-dependent evolution of HIV subtype A gag epitopes was examined. Finally, to confirm observations on globally reported HIV subtype A sequences, we analysed the gag epitope data from our Kenyan, Pakistani, and Afghan cohorts, where both cohort-specific gene epitope variability and HLA restriction profiles of gag epitopes were examined. The most recent common ancestor of the HIV subtype A epidemic was estimated to be 1956 ± 1. A period of exponential growth began about 1980 and lasted for approximately 7 years, stabilized for 15 years, declined for 2-3 years, then stabilized again from about 2004. During the course of evolution, a gradual increase in genomic variability was observed that peaked in 2005-2010. We observed that the number of point mutations and novel epitopes in gag also peaked concurrently during 2005-2010. It appears that as the HIV subtype A epidemic spread globally, changing population immunogenetic pressures may have played a role in steering immune-evolution of this subtype in new directions. This trend is apparent in the genomic variability and epitope diversity of HIV-1 subtype A gag sequences.
HIV-1 subtype A gag variability and epitope evolution.
Directory of Open Access Journals (Sweden)
Syed Hani Abidi
Full Text Available OBJECTIVE: The aim of this study was to examine the course of time-dependent evolution of HIV-1 subtype A on a global level, especially with respect to the dynamics of immunogenic HIV gag epitopes. METHODS: We used a total of 1,893 HIV-1 subtype A gag sequences representing a timeline from 1985 through 2010, and 19 different countries in Africa, Europe and Asia. The phylogenetic relationship of subtype A gag and its epidemic dynamics was analysed through a Maximum Likelihood tree and Bayesian Skyline plot, genomic variability was measured in terms of G → A substitutions and Shannon entropy, and the time-dependent evolution of HIV subtype A gag epitopes was examined. Finally, to confirm observations on globally reported HIV subtype A sequences, we analysed the gag epitope data from our Kenyan, Pakistani, and Afghan cohorts, where both cohort-specific gene epitope variability and HLA restriction profiles of gag epitopes were examined. RESULTS: The most recent common ancestor of the HIV subtype A epidemic was estimated to be 1956 ± 1. A period of exponential growth began about 1980 and lasted for approximately 7 years, stabilized for 15 years, declined for 2-3 years, then stabilized again from about 2004. During the course of evolution, a gradual increase in genomic variability was observed that peaked in 2005-2010. We observed that the number of point mutations and novel epitopes in gag also peaked concurrently during 2005-2010. CONCLUSION: It appears that as the HIV subtype A epidemic spread globally, changing population immunogenetic pressures may have played a role in steering immune-evolution of this subtype in new directions. This trend is apparent in the genomic variability and epitope diversity of HIV-1 subtype A gag sequences.
BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.
Khakabimamaghani, Sahand; Ester, Martin
2016-01-01
The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data.
Pathological Gambling Subtypes
Vachon, David D.; Bagby, R. Michael
2009-01-01
Although pathological gambling (PG) is regarded in the 4th edition of the "Diagnostic and Statistical Manual of Mental Disorders" (American Psychiatric Association, 1994) as a unitary diagnostic construct, it is likely composed of distinct subtypes. In the current report, the authors used cluster analyses of personality traits with a…
Understanding Computational Bayesian Statistics
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
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
Cardiac potassium channel subtypes
DEFF Research Database (Denmark)
Schmitt, Nicole; Grunnet, Morten; Olesen, Søren-Peter
2014-01-01
About 10 distinct potassium channels in the heart are involved in shaping the action potential. Some of the K(+) channels are primarily responsible for early repolarization, whereas others drive late repolarization and still others are open throughout the cardiac cycle. Three main K(+) channels...... drive the late repolarization of the ventricle with some redundancy, and in atria this repolarization reserve is supplemented by the fairly atrial-specific KV1.5, Kir3, KCa, and K2P channels. The role of the latter two subtypes in atria is currently being clarified, and several findings indicate...... that they could constitute targets for new pharmacological treatment of atrial fibrillation. The interplay between the different K(+) channel subtypes in both atria and ventricle is dynamic, and a significant up- and downregulation occurs in disease states such as atrial fibrillation or heart failure...
Kaul, Karen L.; Mangold, Kathy A.; Du, Hongyan; Pesavento, Kristen M.; Nawrocki, John; Nowak, Jan A.
2010-01-01
Influenza virus subtyping has emerged as a critical tool in the diagnosis of influenza. Antiviral resistance is present in the majority of seasonal H1N1 influenza A infections, with association of viral strain type and antiviral resistance. Influenza A virus subtypes can be reliably distinguished by examining conserved sequences in the matrix protein gene. We describe our experience with an assay for influenza A subtyping based on matrix gene sequences. Viral RNA was prepared from nasopharyngeal swab samples, and real-time RT-PCR detection of influenza A and B was performed using a laboratory developed analyte-specific reagent-based assay that targets a conserved region of the influenza A matrix protein gene. FluA-positive samples were analyzed using a second RT-PCR assay targeting the matrix protein gene to distinguish seasonal influenza subtypes based on differential melting of fluorescence resonance energy transfer probes. The novel H1N1 influenza strain responsible for the 2009 pandemic showed a melting profile distinct from that of seasonal H1N1 or H3N2 and compatible with the predicted melting temperature based on the published novel H1N1 matrix gene sequence. Validation by comparison with the Centers for Disease Control and Prevention real-time RT-PCR for swine influenza A (novel H1N1) test showed this assay to be both rapid and reliable (>99% sensitive and specific) in the identification of the novel H1N1 influenza A virus strain. PMID:20595627
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…
Kleibergen, F.R.; Kleijn, R.; Paap, R.
2000-01-01
We propose a novel Bayesian test under a (noninformative) Jeffreys'priorspecification. We check whether the fixed scalar value of the so-calledBayesian Score Statistic (BSS) under the null hypothesis is aplausiblerealization from its known and standardized distribution under thealternative. Unlike
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.
Subtyping adolescents with bulimia nervosa.
Chen, Eunice Y; Le Grange, Daniel
2007-12-01
Cluster analyses of eating disorder patients have yielded a "dietary-depressive" subtype, typified by greater negative affect, and a "dietary" subtype, typified by dietary restraint. This study aimed to replicate these findings in an adolescent sample with bulimia nervosa (BN) from a randomized controlled trial and to examine the validity and reliability of this methodology. In the sample of BN adolescents (N=80), cluster analysis revealed a "dietary-depressive" subtype (37.5%) and a "dietary" subtype (62.5%) using the Beck Depression Inventory, Rosenberg Self-Esteem Scale and Eating Disorder Examination Restraint subscale. The "dietary-depressive" subtype compared to the "dietary" subtype was significantly more likely to: (1) report co-occurring disorders, (2) greater eating and weight concerns, and (3) less vomiting abstinence at post-treatment (all p'sreliability of the subtyping scheme, a larger sample of adolescents with mixed eating and weight disorders in an outpatient eating disorder clinic (N=149) was subtyped, yielding similar subtypes. These results support the validity and reliability of the subtyping strategy in two adolescent samples.
Evidence of multiple introductions of HIV-1 subtype C in Angola.
Afonso, Joana Morais; Morgado, Mariza G; Bello, Gonzalo
2012-10-01
HIV-1 subtype C is the most prevalent group M clade in southern Africa and some eastern African countries. Subtype C is also the most frequent subtype in Angola (southwestern Africa), with an estimated prevalence of 10-20%. In order to better understand the origin of the HIV-1 subtype C strains circulating in Angola, 31 subtype C pol sequences of Angolan origin were compared with 1950 subtype C pol sequences sampled in other African countries. Phylogenetic analyses reveal that the Angolan subtype C sequences were distributed in 16 different lineages that were widely dispersed among other African strains. Ten subtype C Angolan lineages were composed by only one sequence, while the remaining six clades contain between two and seven sequences. Bayesian phylogeographic analysis indicates that most Angolan clades probably originated in different southern African countries with the exception of one lineage that most likely originated in Burundi. Evolutionary analysis suggests that those Angolan subtype C clades composed by ≥ 2 sequences were introduced into the country between the late 1970s and the mid 2000s. The median estimated time frame for the origin of those Angolan lineages coincides with periods of positive migration influx in Angola that were preceded by phases of negative migratory outflow. These results demonstrate that the Angolan subtype C epidemic resulted from multiple introductions of subtype C viruses mainly imported from southern African countries over the last 30years, some of which have been locally disseminated establishing several autochthonous transmission networks. This study also suggests that population mobility between Angola and southern African countries during civil war (1974-2002) may have played a key role in the emergence of the Angolan subtype C epidemic. Copyright © 2012 Elsevier B.V. All rights reserved.
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
Bayesian data analysis for newcomers.
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.
Bayesian methods for data analysis
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
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...
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...
Fox, Gerardus J.A.; van den Berg, Stéphanie Martine; Veldkamp, Bernard P.; Irwing, P.; Booth, T.; Hughes, D.
2015-01-01
In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item
Bayesian Networks An Introduction
Koski, Timo
2009-01-01
Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include:.: An introduction to Dirichlet Distribution, Exponential Families and their applications.; A detailed description of learni
Maeda, Shin-ichi
2014-01-01
Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also encourages the optimization of the dropout.
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.
Pure type systems with subtyping
Zwanenburg, J.; Girard, J.-Y.
1999-01-01
We extend the framework of Pure Type Systems with subtyping, as found in F = ¿ . This leads to a concise description of many existing systems with subtyping, and also to some new interesting systems. We develop the meta-theory for this framework, including Subject Reduction and Minimal Typing. The
Bayesian networks with examples in R
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.
Bayesian methods in reliability
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.
CSIR Research Space (South Africa)
Rosman, Benjamin
2016-02-01
Full Text Available Keywords Policy Reuse · Reinforcement Learning · Online Learning · Online Bandits · Transfer Learning · Bayesian Optimisation · Bayesian Decision Theory. 1 Introduction As robots and software agents are becoming more ubiquitous in many applications.... The agent has access to a library of policies (pi1, pi2 and pi3), and has previously experienced a set of task instances (τ1, τ2, τ3, τ4), as well as samples of the utilities of the library policies on these instances (the black dots indicate the means...
Origin and dynamics of HIV-1 subtype C infection in India.
Directory of Open Access Journals (Sweden)
Chengli Shen
Full Text Available To investigate the geographical origin and evolution dynamics of HIV-1 subtype C infection in India.Ninety HIV-1 subtype C env gp120 subtype C sequences from India were compared with 312 env gp120 reference subtype C sequences from 27 different countries obtained from Los Alamos HIV database. All the HIV-1 subtype C env gp120 sequences from India were used for the geographical origin analysis and 61 subtype C env gp120 sequences with known sampling year (from 1991 to 2008 were employed to determine the origin of HIV infection in India.Phylogenetic analysis of HIV-1 env sequences was used to investigate the geographical origin and tMRCA of Indian HIV-1 subtype C. Evolutionary parameters including origin date and demographic growth patterns of Indian subtype C were estimated using a Bayesian coalescent-based approach under relaxed molecular clock models.The majority of the analyzed Indian and South African HIV-1 subtype C sequences formed a single monophyletic cluster. The most recent common ancestor date was calculated to be 1975.56 (95% HPD, 1968.78-1981.52. Reconstruction of the effective population size revealed three phases of epidemic growth: an initial slow growth, followed by exponential growth, and then a plateau phase approaching present time. Stabilization of the epidemic growth phase correlated with the foundation of National AIDS Control Organization in India.Indian subtype C originated from a single South African lineage in the middle of 1970s. The current study emphasizes not only the utility of HIV-1 sequence data for epidemiological studies but more notably highlights the effectiveness of community or government intervention strategies in controlling the trend of the epidemic.
Bayesian methods for hackers probabilistic programming and Bayesian inference
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...
Bayesian logistic regression analysis
Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.
2012-01-01
In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an
Korattikara, A.; Rathod, V.; Murphy, K.; Welling, M.; Cortes, C.; Lawrence, N.D.; Lee, D.D.; Sugiyama, M.; Garnett, R.
2015-01-01
We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities p(y|x, D), e.g., for applications involving bandits or active learning. One simple
Bayesian Geostatistical Design
DEFF Research Database (Denmark)
Diggle, Peter; Lophaven, Søren Nymand
2006-01-01
locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model...
Bayesian statistical inference
Directory of Open Access Journals (Sweden)
Bruno De Finetti
2017-04-01
Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.
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...
Bayesian Independent Component Analysis
DEFF Research Database (Denmark)
Winther, Ole; Petersen, Kaare Brandt
2007-01-01
In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...
Bayesian Exponential Smoothing.
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.
Bayesian optimization for materials science
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...
The Origin and Evolutionary History of HIV-1 Subtype C in Senegal
Jung, Matthieu; Leye, Nafissatou; Vidal, Nicole; Fargette, Denis; Diop, Halimatou; Toure Kane, Coumba; Gascuel, Olivier; Peeters, Martine
2012-01-01
Background The classification of HIV-1 strains in subtypes and Circulating Recombinant Forms (CRFs) has helped in tracking the course of the HIV pandemic. In Senegal, which is located at the tip of West Africa, CRF02_AG predominates in the general population and Female Sex Workers (FSWs). In contrast, 40% of Men having Sex with Men (MSM) in Senegal are infected with subtype C. In this study we analyzed the geographical origins and introduction dates of HIV-1 C in Senegal in order to better understand the evolutionary history of this subtype, which predominates today in the MSM population Methodology/Principal Findings We used a combination of phylogenetic analyses and a Bayesian coalescent-based approach, to study the phylogenetic relationships in pol of 56 subtype C isolates from Senegal with 3,025 subtype C strains that were sampled worldwide. Our analysis shows a significantly well supported cluster which contains all subtype C strains that circulate among MSM in Senegal. The MSM cluster and other strains from Senegal are widely dispersed among the different subclusters of African HIV-1 C strains, suggesting multiple introductions of subtype C in Senegal from many different southern and east African countries. More detailed analyses show that HIV-1 C strains from MSM are more closely related to those from southern Africa. The estimated date of the MRCA of subtype C in the MSM population in Senegal is estimated to be in the early 80's. Conclusions/Significance Our evolutionary reconstructions suggest that multiple subtype C viruses with a common ancestor originating in the early 1970s entered Senegal. There was only one efficient spread in the MSM population, which most likely resulted from a single introduction, underlining the importance of high-risk behavior in spread of viruses. PMID:22470456
Probability and Bayesian statistics
1987-01-01
This book contains selected and refereed contributions to the "Inter national Symposium on Probability and Bayesian Statistics" which was orga nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics an...
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel N
2012-01-01
Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....
Approximate Bayesian recursive estimation
Czech Academy of Sciences Publication Activity Database
Kárný, Miroslav
2014-01-01
Roč. 285, č. 1 (2014), s. 100-111 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Approximate parameter estimation * Bayesian recursive estimation * Kullback–Leibler divergence * Forgetting Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.038, year: 2014 http://library.utia.cas.cz/separaty/2014/AS/karny-0425539.pdf
DEFF Research Database (Denmark)
Antoniou, Constantinos; Harrison, Glenn W.; Lau, Morten I.
2015-01-01
A large literature suggests that many individuals do not apply Bayes’ Rule when making decisions that depend on them correctly pooling prior information and sample data. We replicate and extend a classic experimental study of Bayesian updating from psychology, employing the methods of experimenta...... economics, with careful controls for the confounding effects of risk aversion. Our results show that risk aversion significantly alters inferences on deviations from Bayes’ Rule....
Energy Technology Data Exchange (ETDEWEB)
Andrews, Stephen A. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Sigeti, David E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-11-15
These are a set of slides about Bayesian hypothesis testing, where many hypotheses are tested. The conclusions are the following: The value of the Bayes factor obtained when using the median of the posterior marginal is almost the minimum value of the Bayes factor. The value of τ^{2} which minimizes the Bayes factor is a reasonable choice for this parameter. This allows a likelihood ratio to be computed with is the least favorable to H_{0}.
Introduction to Bayesian statistics
Koch, Karl-Rudolf
2007-01-01
This book presents Bayes' theorem, the estimation of unknown parameters, the determination of confidence regions and the derivation of tests of hypotheses for the unknown parameters. It does so in a simple manner that is easy to comprehend. The book compares traditional and Bayesian methods with the rules of probability presented in a logical way allowing an intuitive understanding of random variables and their probability distributions to be formed.
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.
Bayesian ARTMAP for regression.
Sasu, L M; Andonie, R
2013-10-01
Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.
Bayesian theory and applications
Dellaportas, Petros; Polson, Nicholas G; Stephens, David A
2013-01-01
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...
Bayesian analysis in plant pathology.
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.
Motor subtype changes in early Parkinson's disease.
Eisinger, Robert S; Hess, Christopher W; Martinez-Ramirez, Daniel; Almeida, Leonardo; Foote, Kelly D; Okun, Michael S; Gunduz, Aysegul
2017-10-01
Distinct motor subtypes of Parkinson's disease (PD) have been described through both clinical observation and through data-driven approaches. However, the extent to which motor subtypes change during disease progression remains unknown. Our objective was to determine motor subtypes of PD using an unsupervised clustering methodology and evaluate subtype changes with disease duration. The Parkinson's Progression Markers Initiative database of 423 newly diagnosed PD patients was utilized to retrospectively identify unique motor subtypes through a data-driven, hierarchical correlational clustering approach. For each patient, we assigned a subtype to each motor assessment at each follow-up visit (time points) and by using published criteria. We examined changes in PD subtype with disease duration using both qualitative and quantitative methods. Five distinct motor subtypes were identified based on the motor assessment items and these included: Tremor Dominant (TD), Axial Dominant, Appendicular Dominant, Rigidity Dominant, and Postural and Instability Gait Disorder Dominant. About half of the patients had consistent subtypes at all time points. Most patients met criteria for TD subtype soon after diagnosis. For patients with inconsistent subtypes, there was an overall trend to shift away from a TD phenotype with disease duration, as shown by chi-squared test, p motor subtypes in PD can shift with increasing disease duration. Shifting subtypes is a factor that should be accounted for in clinical practice or in clinical trials. Copyright © 2017 Elsevier Ltd. All rights reserved.
Emergence of canine parvovirus subtype 2b (CPV-2b) infections in Australian dogs.
Clark, Nicholas J; Seddon, Jennifer M; Kyaw-Tanner, Myat; Al-Alawneh, John; Harper, Gavin; McDonagh, Phillip; Meers, Joanne
2018-03-01
Tracing the temporal dynamics of pathogens is crucial for developing strategies to detect and limit disease emergence. Canine parvovirus (CPV-2) is an enteric virus causing morbidity and mortality in dogs around the globe. Previous work in Australia reported that the majority of cases were associated with the CPV-2a subtype, an unexpected finding since CPV-2a was rapidly replaced by another subtype (CPV-2b) in many countries. Using a nine-year dataset of CPV-2 infections from 396 dogs sampled across Australia, we assessed the population dynamics and molecular epidemiology of circulating CPV-2 subtypes. Bayesian phylogenetic Skygrid models and logistic regressions were used to trace the temporal dynamics of CPV-2 infections in dogs sampled from 2007 to 2016. Phylogenetic models indicated that CPV-2a likely emerged in Australia between 1973 and 1988, while CPV-2b likely emerged between 1985 and 1998. Sequences from both subtypes were found in dogs across continental Australia and Tasmania, with no apparent effect of climate variability on subtype occurrence. Both variant subtypes exhibited a classical disease emergence pattern of relatively high rates of evolution during early emergence followed by subsequent decreases in evolutionary rates over time. However, the CPV-2b subtype maintained higher mutation rates than CPV-2a and continued to expand, resulting in an increase in the probability that dogs will carry this subtype over time. Ongoing monitoring programs that provide molecular epidemiology surveillance will be necessary to detect emergence of new variants and make informed recommendations to develop reliable detection and vaccine methods. Copyright © 2017 Elsevier B.V. All rights reserved.
Bayesian nonparametric data analysis
Müller, Peter; Jara, Alejandro; Hanson, Tim
2015-01-01
This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in on-line software pages.
Congdon, Peter
2014-01-01
This book provides an accessible approach to Bayesian computing and data analysis, with an emphasis on the interpretation of real data sets. Following in the tradition of the successful first edition, this book aims to make a wide range of statistical modeling applications accessible using tested code that can be readily adapted to the reader's own applications. The second edition has been thoroughly reworked and updated to take account of advances in the field. A new set of worked examples is included. The novel aspect of the first edition was the coverage of statistical modeling using WinBU
Classification of alpha 1-adrenoceptor subtypes
Michel, M. C.; Kenny, B.; Schwinn, D. A.
1995-01-01
Two alpha 1-adrenoceptor subtypes (alpha 1A and alpha 1B) have been detected in various tissues by pharmacological techniques, and three distinct cDNAs encoding alpha 1-adrenoceptor subtypes have been cloned. The profile of an increasing number of subtype-selective compounds at cloned and endogenous
Subtypes of nonmedical prescription drug misuse
McCabe, Sean Esteban; Boyd, Carol J.; Teter, Christian J.
2010-01-01
This study used three characteristics (i.e., motive, route of administration, and co-ingestion with alcohol) of nonmedical prescription drug misuse across four separate classes (i.e., pain, sedative/anxiety, sleeping and stimulant medications) to examine subtypes and drug related problems. A Web survey was self-administered by a randomly selected sample of 3,639 undergraduate students attending a large Midwestern 4-year U.S. university. Self-treatment subtypes were characterized by motives consistent with the prescription drug's pharmaceutical main indication, oral only routes of administration, and no co-ingestion with alcohol. Recreational subtypes were characterized by recreational motives, oral or non-oral routes, and co-ingestion. Mixed subtypes consisted of other combinations of motives, routes, and co-ingestion. Among those who reported nonmedical prescription drug misuse, approximately 13% were classified into the recreational subtype, while 39% were in the self-treatment subtype, and 48% were in the mixed subtype. There were significant differences in the subtypes in terms of gender, race and prescription drug class. Approximately 50% of those in subtypes other than self-treatment screened positive for drug abuse. The odds of substance use and abuse were generally lower among self-treatment subtypes than other subtypes. The findings indicate subtypes should be considered when examining nonmedical prescription drug misuse, especially for pain medication. PMID:19278795
Inference in hybrid Bayesian networks
DEFF Research Database (Denmark)
Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2009-01-01
Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....
Searching Algorithm Using Bayesian Updates
Caudle, Kyle
2010-01-01
In late October 1967, the USS Scorpion was lost at sea, somewhere between the Azores and Norfolk Virginia. Dr. Craven of the U.S. Navy's Special Projects Division is credited with using Bayesian Search Theory to locate the submarine. Bayesian Search Theory is a straightforward and interesting application of Bayes' theorem which involves searching…
Bayesian Data Analysis (lecture 2)
CERN. Geneva
2018-01-01
framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.
Bayesian Data Analysis (lecture 1)
CERN. Geneva
2018-01-01
framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.
The Bayesian Covariance Lasso.
Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G
2013-04-01
Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size ( n ) is less than the dimension ( d ), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data.
Bayesian dynamic mediation analysis.
Huang, Jing; Yuan, Ying
2017-12-01
Most existing methods for mediation analysis assume that mediation is a stationary, time-invariant process, which overlooks the inherently dynamic nature of many human psychological processes and behavioral activities. In this article, we consider mediation as a dynamic process that continuously changes over time. We propose Bayesian multilevel time-varying coefficient models to describe and estimate such dynamic mediation effects. By taking the nonparametric penalized spline approach, the proposed method is flexible and able to accommodate any shape of the relationship between time and mediation effects. Simulation studies show that the proposed method works well and faithfully reflects the true nature of the mediation process. By modeling mediation effect nonparametrically as a continuous function of time, our method provides a valuable tool to help researchers obtain a more complete understanding of the dynamic nature of the mediation process underlying psychological and behavioral phenomena. We also briefly discuss an alternative approach of using dynamic autoregressive mediation model to estimate the dynamic mediation effect. The computer code is provided to implement the proposed Bayesian dynamic mediation analysis. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Approximate Bayesian computation.
Directory of Open Access Journals (Sweden)
Mikael Sunnåker
Full Text Available Approximate Bayesian computation (ABC constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology.
Bayesian inference with ecological applications
Link, William A
2009-01-01
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...
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.
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
Diabetes and Breast Cancer Subtypes.
Directory of Open Access Journals (Sweden)
Heleen K Bronsveld
Full Text Available Women with diabetes have a worse survival after breast cancer diagnosis compared to women without diabetes. This may be due to a different etiological profile, leading to the development of more aggressive breast cancer subtypes. Our aim was to investigate whether insulin and non-insulin treated women with diabetes develop specific clinicopathological breast cancer subtypes compared to women without diabetes.This cross-sectional study included randomly selected patients with invasive breast cancer diagnosed in 2000-2010. Stratified by age at breast cancer diagnosis (≤50 and >50 years, women with diabetes were 2:1 frequency-matched on year of birth and age at breast cancer diagnosis (both in 10-year categories to women without diabetes, to select ~300 patients with tumor tissue available. Tumor MicroArrays were stained by immunohistochemistry for estrogen and progesterone receptor (ER, PR, HER2, Ki67, CK5/6, CK14, and p63. A pathologist scored all stains and revised morphology and grade. Associations between diabetes/insulin treatment and clinicopathological subtypes were analyzed using multivariable logistic regression. Morphology and grade were not significantly different between women with diabetes (n = 211 and women without diabetes (n = 101, irrespective of menopausal status. Premenopausal women with diabetes tended to have more often PR-negative (OR = 2.44(95%CI:1.07-5.55, HER2-negative (OR = 2.84(95%CI:1.11-7.22, and basal-like (OR = 3.14(95%CI:1.03-9.60 tumors than the women without diabetes, with non-significantly increased frequencies of ER-negative (OR = 2.48(95%CI:0.95-6.45 and triple negative (OR = 2.60(95%CI:0.88-7.67 tumors. After adjustment for age and BMI, the associations remained similar in size but less significant. We observed no evidence for associations of clinicopathological subtypes with diabetes in postmenopausal women, or with insulin treatment in general.We found no compelling evidence that women with diabetes
Diabetes and Breast Cancer Subtypes.
Bronsveld, Heleen K; Jensen, Vibeke; Vahl, Pernille; De Bruin, Marie L; Cornelissen, Sten; Sanders, Joyce; Auvinen, Anssi; Haukka, Jari; Andersen, Morten; Vestergaard, Peter; Schmidt, Marjanka K
2017-01-01
Women with diabetes have a worse survival after breast cancer diagnosis compared to women without diabetes. This may be due to a different etiological profile, leading to the development of more aggressive breast cancer subtypes. Our aim was to investigate whether insulin and non-insulin treated women with diabetes develop specific clinicopathological breast cancer subtypes compared to women without diabetes. This cross-sectional study included randomly selected patients with invasive breast cancer diagnosed in 2000-2010. Stratified by age at breast cancer diagnosis (≤50 and >50 years), women with diabetes were 2:1 frequency-matched on year of birth and age at breast cancer diagnosis (both in 10-year categories) to women without diabetes, to select ~300 patients with tumor tissue available. Tumor MicroArrays were stained by immunohistochemistry for estrogen and progesterone receptor (ER, PR), HER2, Ki67, CK5/6, CK14, and p63. A pathologist scored all stains and revised morphology and grade. Associations between diabetes/insulin treatment and clinicopathological subtypes were analyzed using multivariable logistic regression. Morphology and grade were not significantly different between women with diabetes (n = 211) and women without diabetes (n = 101), irrespective of menopausal status. Premenopausal women with diabetes tended to have more often PR-negative (OR = 2.44(95%CI:1.07-5.55)), HER2-negative (OR = 2.84(95%CI:1.11-7.22)), and basal-like (OR = 3.14(95%CI:1.03-9.60) tumors than the women without diabetes, with non-significantly increased frequencies of ER-negative (OR = 2.48(95%CI:0.95-6.45)) and triple negative (OR = 2.60(95%CI:0.88-7.67) tumors. After adjustment for age and BMI, the associations remained similar in size but less significant. We observed no evidence for associations of clinicopathological subtypes with diabetes in postmenopausal women, or with insulin treatment in general. We found no compelling evidence that women with diabetes, treated
International Nuclear Information System (INIS)
Rajabalinejad, M.
2010-01-01
To reduce cost of Monte Carlo (MC) simulations for time-consuming processes, Bayesian Monte Carlo (BMC) is introduced in this paper. The BMC method reduces number of realizations in MC according to the desired accuracy level. BMC also provides a possibility of considering more priors. In other words, different priors can be integrated into one model by using BMC to further reduce cost of simulations. This study suggests speeding up the simulation process by considering the logical dependence of neighboring points as prior information. This information is used in the BMC method to produce a predictive tool through the simulation process. The general methodology and algorithm of BMC method are presented in this paper. The BMC method is applied to the simplified break water model as well as the finite element model of 17th Street Canal in New Orleans, and the results are compared with the MC and Dynamic Bounds methods.
Bayesian nonparametric hierarchical modeling.
Dunson, David B
2009-04-01
In biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects. Parametric assumptions on latent variable distributions can be challenging to check and are typically unwarranted, given available prior knowledge. This article reviews some recent developments in Bayesian nonparametric methods motivated by complex, multivariate and functional data collected in biomedical studies. The author provides a brief review of flexible parametric approaches relying on finite mixtures and latent class modeling. Dirichlet process mixture models are motivated by the need to generalize these approaches to avoid assuming a fixed finite number of classes. Focusing on an epidemiology application, the author illustrates the practical utility and potential of nonparametric Bayes methods.
Book review: Bayesian analysis for population ecology
Link, William A.
2011-01-01
Brian Dennis described the field of ecology as “fertile, uncolonized ground for Bayesian ideas.” He continued: “The Bayesian propagule has arrived at the shore. Ecologists need to think long and hard about the consequences of a Bayesian ecology. The Bayesian outlook is a successful competitor, but is it a weed? I think so.” (Dennis 2004)
Motoric subtypes of delirium in geriatric patients
Directory of Open Access Journals (Sweden)
Sandeep Grover
2014-01-01
Results: On amended DMSS, hyperactive subtype (N = 45; 45.9% was the most common motoric subtype of delirium, followed by hypoactive subtype (N = 23; 23.5%, and mixed subtype (N = 21; 21.4%. On DRS-R-98, all patients fulfilled the criteria of ′acute (temporal onset of symptoms′, ′presence of an underlying physical disorder′ and ′difficulty in attention′. In the total sample, >90% of the patients had disturbances in sleep-wake cycle, orientation and fluctuation of symptoms. The least common symptoms were delusions, visuospatial disturbances and motor retardation. When compared to hypoactive group, significantly higher proportion of patients with hyperactive subtype had delusions, perceptual disturbances, and motor agitation. Whereas, compared to hyperactive subtype, significantly higher proportion of patients with hypoactive subtype had thought process abnormality and motor retardation. When the hyperactive and mixed motoric subtype groups were compared, patients with mixed subtype group had significantly higher prevalence of thought process abnormality and motor retardation. Comparison of hypoactive and mixed subtype revealed significant differences in the frequency of perceptual disturbances, delusions and motor agitation and all these symptoms being found more commonly in patients with the mixed subtype. Severity of symptoms were found to be significantly different across the various motoric subtypes for some of the non-cognitive symptoms, but significant differences were not seen for the cognitive symptoms as assessed on DRS-R-98. Conclusion: In elderly patients, motor subtypes of delirium differ from each other on non-cognitive symptom profile in terms of frequency and severity.
Current trends in Bayesian methodology with applications
Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia
2015-01-01
Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on
A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.
Mo, Qianxing; Shen, Ronglai; Guo, Cui; Vannucci, Marina; Chan, Keith S; Hilsenbeck, Susan G
2018-01-01
Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
Bayesian image restoration, using configurations
Thorarinsdottir, Thordis
2006-01-01
In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the re...
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...
Dennis, Ann M; Hué, Stephane; Learner, Emily; Sebastian, Joseph; Miller, William C; Eron, Joseph J
2017-01-01
HIV-1 diversity is increasing in North American and European cohorts which may have public health implications. However, little is known about non-B subtype diversity in the southern United States, despite the region being the epicenter of the nation's epidemic. We characterized HIV-1 diversity and transmission clusters to identify the extent to which non-B strains are transmitted locally. We conducted cross-sectional analyses of HIV-1 partial pol sequences collected from 1997 to 2014 from adults accessing routine clinical care in North Carolina (NC). Subtypes were evaluated using COMET and phylogenetic analysis. Putative transmission clusters were identified using maximum-likelihood trees. Clusters involving non-B strains were confirmed and their dates of origin were estimated using Bayesian phylogenetics. Data were combined with demographic information collected at the time of sample collection and country of origin for a subset of patients. Among 24,972 sequences from 15,246 persons, the non-B subtype prevalence increased from 0% to 3.46% over the study period. Of 325 persons with non-B subtypes, diversity was high with over 15 pure subtypes and recombinants; subtype C (28.9%) and CRF02_AG (24.0%) were most common. While identification of transmission clusters was lower for persons with non-B versus B subtypes, several local transmission clusters (≥3 persons) involving non-B subtypes were identified and all were presumably due to heterosexual transmission. Prevalence of non-B subtype diversity remains low in NC but a statistically significant rise was identified over time which likely reflects multiple importation. However, the combined phylogenetic clustering analysis reveals evidence for local onward transmission. Detection of these non-B clusters suggests heterosexual transmission and may guide diagnostic and prevention interventions.
Phylodynamics of HIV-1 subtype B among the men-having-sex-with-men (MSM population in Hong Kong.
Directory of Open Access Journals (Sweden)
Jonathan Hon-Kwan Chen
Full Text Available The men-having-sex-with-men (MSM population has become one of the major risk groups for HIV-1 infection in the Asia Pacific countries. Hong Kong is located in the centre of Asia and the transmission history of HIV-1 subtype B transmission among MSM remained unclear. The aim of this study was to investigate the transmission dynamics of HIV-1 subtype B virus in the Hong Kong MSM population. Samples of 125 HIV-1 subtype B infected MSM patients were recruited in this study. Through this study, the subtype B epidemic in the Hong Kong MSM population was identified spreading mainly among local Chinese who caught infection locally. On the other hand, HIV-1 subtype B infected Caucasian MSM caught infection mainly outside Hong Kong. The Bayesian phylogenetic analysis also indicated that 3 separate subtype B epidemics with divergence dates in the 1990s had occurred. The first and latest epidemics were comparatively small-scaled; spreading among the local Chinese MSM while sauna-visiting was found to be the major sex partner sourcing reservoir for the first subtype B epidemic. However, the second epidemic was spread in a large-scale among local Chinese MSM with a number of them having sourced their sex partners through the internet. The epidemic virus was estimated to have a divergence date in 1987 and the infected population in Hong Kong had a logistic growth throughout the past 20 years. Our study elucidated the evolutionary and demographic history of HIV-1 subtype B virus in Hong Kong MSM population. The understanding of transmission and growth model of the subtype B epidemic provides more information on the HIV-1 transmission among MSM population in other Asia Pacific high-income countries.
Hypertension Subtypes among Hypertensive Patients in Ibadan
Abiodun M. Adeoye; Adewole Adebiyi; Bamidele O. Tayo; Babatunde L. Salako; Adesola Ogunniyi; Richard S. Cooper
2014-01-01
Background. Certain hypertension subtypes have been shown to increase the risk for cardiovascular morbidity and mortality and may be related to specific underlying genetic determinants. Inappropriate characterization of subtypes of hypertension makes efforts at elucidating the genetic contributions to the etiology of hypertension largely vapid. We report the hypertension subtypes among patients with hypertension from South-Western Nigeria. Methods. A total of 1858 subjects comprising 76% fema...
International Nuclear Information System (INIS)
Le Yu; Chen, Wilfred; Mulchandani, Ashok
2006-01-01
A microbial biosensor is an analytical device that couples microorganisms with a transducer to enable rapid, accurate and sensitive detection of target analytes in fields as diverse as medicine, environmental monitoring, defense, food processing and safety. The earlier microbial biosensors used the respiratory and metabolic functions of the microorganisms to detect a substance that is either a substrate or an inhibitor of these processes. Recently, genetically engineered microorganisms based on fusing of the lux, gfp or lacZ gene reporters to an inducible gene promoter have been widely applied to assay toxicity and bioavailability. This paper reviews the recent trends in the development and application of microbial biosensors. Current advances and prospective future direction in developing microbial biosensor have also been discussed
Directory of Open Access Journals (Sweden)
Miłosz Parczewski
Full Text Available BACKGROUND: HIV-1 subtype D infections, which are associated with a faster rate of progression and lymphocyte CD4 decline, cognitive deficit and higher mortality, have rarely been found in native Europeans. In Northwestern Poland, however, infections with this subtype had been identified. This study aimed to analyze the sequence and clinical data for patients with subtype D using molecular phylogeography and identify transmission clusters and ancestry, as well as drug resistance, baseline HIV tropism and antiretroviral treatment efficacy. METHODS: Phylogenetic analyses of local HIV-1 subtype D sequences were performed, with time to the most recent common ancestor inferred using bayesian modeling. Sequence and drug resistance data were linked with the clinical and epidemiological information. RESULTS: Subtype D was found in 24 non-immigrant Caucasian, heterosexually infected patients (75% of females, median age at diagnosis of 49.5 years; IQR: 29-56 years. Partial pol sequences clustered monophyletically with the clades of Ugandan origin and no evidence of transmission from other European countries was found. Time to the most common recent ancestor was 1989.24 (95% HPD: 1968.83-1994.46. Baseline drug resistance to nucleoside reverse transcriptase inhibitors was observed in 54.5% of cases (mutations: M41L, K103N, T215S/D with evidence of clustering, no baseline integrase or protease resistance and infrequent non-R5 tropism (13.6%. Virologic failure was observed in 60% of cases and was associated with poor adherence (p<0.001 and subsequent development of drug resistance (p = 0.008, OR: 20 (95%CI: 1.7-290. CONCLUSIONS: Local subtype D represented an independently transmitted network with probably single index case, high frequency of primary drug resistance and evidence of transmission clusters.
Bayesian seismic AVO inversion
Energy Technology Data Exchange (ETDEWEB)
Buland, Arild
2002-07-01
A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P-wave velocity, S-wave velocity and density. Distributions for other elastic parameters can also be assessed, for example acoustic impedance, shear impedance and P-wave to S-wave velocity ratio. The inversion algorithm is based on the convolutional model and a linearized weak contrast approximation of the Zoeppritz equation. The solution is represented by a Gaussian posterior distribution with explicit expressions for the posterior expectation and covariance, hence exact prediction intervals for the inverted parameters can be computed under the specified model. The explicit analytical form of the posterior distribution provides a computationally fast inversion method. Tests on synthetic data show that all inverted parameters were almost perfectly retrieved when the noise approached zero. With realistic noise levels, acoustic impedance was the best determined parameter, while the inversion provided practically no information about the density. The inversion algorithm has also been tested on a real 3-D dataset from the Sleipner Field. The results show good agreement with well logs but the uncertainty is high. The stochastic model includes uncertainties of both the elastic parameters, the wavelet and the seismic and well log data. The posterior distribution is explored by Markov chain Monte Carlo simulation using the Gibbs sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The uncertainty of the estimated wavelet is low. In the Heidrun examples the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results. We have developed a 3-D linearized AVO inversion method with spatially coupled model parameters where the objective is to obtain posterior distributions for P-wave velocity, S
Phylodynamics of HIV-1 subtype F1 in Angola, Brazil and Romania.
Bello, Gonzalo; Afonso, Joana Morais; Morgado, Mariza G
2012-07-01
The HIV-1 subtype F1 is exceptionally prevalent in Angola, Brazil and Romania. The epidemiological context in which the spread of HIV occurred was highly variable from one country to another, mainly due to the existence of a long-term civil war in Angola and the contamination of a large number of children in Romania. Here we apply phylogenetic and Bayesian coalescent-based methods to reconstruct the phylodynamic patterns of HIV-1 subtype F1 in such different epidemiological settings. The phylogenetic analyses of HIV-1 subtype F1 pol sequences sampled worldwide confirmed that most sequences from Angola, Brazil and Romania segregated in country-specific monophyletic groups, while most subtype F1 sequences from Romanian children branched as a monophyletic sub-cluster (Romania-CH) nested within sequences from adults. The inferred time of the most recent common ancestor of the different subtype F1 clades were as follow: Angola=1983 (1978-1989), Brazil=1977 (1972-1981), Romania adults=1980 (1973-1987), and Romania-CH=1985 (1978-1989). All subtype F1 clades showed a demographic history best explained by a model of logistic population growth. Although the expansion phase of subtype F1 epidemic in Angola (mid 1980s to early 2000s) overlaps with the civil war period (1975-2002), the mean estimated growth rate of the Angolan F1 clade (0.49 year(-1)) was not exceptionally high, but quite similar to that estimated for the Brazilian (0.69 year(-1)) and Romanian adult (0.36 year(-1)) subtype F1 clades. The Romania-CH subtype F1 lineage, by contrast, displayed a short and explosive dissemination phase, with a median growth rate (2.47 year(-1)) much higher than that estimated for adult populations. This result supports the idea that the AIDS epidemic that affected the Romanian children was mainly caused by the spread of the HIV through highly efficient parenteral transmission networks, unlike adult populations where HIV is predominantly transmitted through sexual route. Copyright
Transsexual subtypes : Clinical and theoretical significance
Smith, YLS; van Goozen, SHM; Kuiper, AJ; Cohen-Kettenis, PT
2005-01-01
The present study was designed to investigate whether transsexuals can be validly subdivided into subtypes on the basis of sexual orientation, and whether differences between subtypes of transsexuals are similar for male-to-female (ME) and female-to-male transsexuals (FMs). Within a large
Bayesian networks improve causal environmental ...
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
Bayesian Latent Class Analysis Tutorial.
Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca
2018-01-01
This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.
Kernel Bayesian ART and ARTMAP.
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.
Interactive Instruction in Bayesian Inference
DEFF Research Database (Denmark)
Khan, Azam; Breslav, Simon; Hornbæk, Kasper
2018-01-01
An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....
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.
Bayesian analysis of CCDM models
Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.
2017-09-01
Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.
Bayesian analysis of CCDM models
Energy Technology Data Exchange (ETDEWEB)
Jesus, J.F. [Universidade Estadual Paulista (Unesp), Câmpus Experimental de Itapeva, Rua Geraldo Alckmin 519, Vila N. Sra. de Fátima, Itapeva, SP, 18409-010 Brazil (Brazil); Valentim, R. [Departamento de Física, Instituto de Ciências Ambientais, Químicas e Farmacêuticas—ICAQF, Universidade Federal de São Paulo (UNIFESP), Unidade José Alencar, Rua São Nicolau No. 210, Diadema, SP, 09913-030 Brazil (Brazil); Andrade-Oliveira, F., E-mail: jfjesus@itapeva.unesp.br, E-mail: valentim.rodolfo@unifesp.br, E-mail: felipe.oliveira@port.ac.uk [Institute of Cosmology and Gravitation—University of Portsmouth, Burnaby Road, Portsmouth, PO1 3FX United Kingdom (United Kingdom)
2017-09-01
Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3α H {sub 0} model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.
Learning Bayesian networks for discrete data
Liang, Faming; Zhang, Jian
2009-01-01
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
Bayesian Network Induction via Local Neighborhoods
National Research Council Canada - National Science Library
Margaritis, Dimitris
1999-01-01
.... We present an efficient algorithm for learning Bayesian networks from data. Our approach constructs Bayesian networks by first identifying each node's Markov blankets, then connecting nodes in a consistent way...
Can a significance test be genuinely Bayesian?
Pereira, Carlos A. de B.; Stern, Julio Michael; Wechsler, Sergio
2008-01-01
The Full Bayesian Significance Test, FBST, is extensively reviewed. Its test statistic, a genuine Bayesian measure of evidence, is discussed in detail. Its behavior in some problems of statistical inference like testing for independence in contingency tables is discussed.
Bayesian modeling using WinBUGS
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 ...
Inference in hybrid Bayesian networks
International Nuclear Information System (INIS)
Langseth, Helge; Nielsen, Thomas D.; Rumi, Rafael; Salmeron, Antonio
2009-01-01
Since the 1980s, Bayesian networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability techniques (like fault trees and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (the so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.
3D Bayesian contextual classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
2000-01-01
We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....
Bayesian methods for proteomic biomarker development
Directory of Open Access Journals (Sweden)
Belinda Hernández
2015-12-01
In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.
Bayesian networks and food security - An introduction
Stein, A.
2004-01-01
This paper gives an introduction to Bayesian networks. Networks are defined and put into a Bayesian context. Directed acyclical graphs play a crucial role here. Two simple examples from food security are addressed. Possible uses of Bayesian networks for implementation and further use in decision
Plug & Play object oriented Bayesian networks
DEFF Research Database (Denmark)
Bangsø, Olav; Flores, J.; Jensen, Finn Verner
2003-01-01
been shown to be quite suitable for dynamic domains as well. However, processing object oriented Bayesian networks in practice does not take advantage of their modular structure. Normally the object oriented Bayesian network is transformed into a Bayesian network and, inference is performed...... dynamic domains. The communication needed between instances is achieved by means of a fill-in propagation scheme....
A Bayesian framework for risk perception
van Erp, H.R.N.
2017-01-01
We present here a Bayesian framework of risk perception. This framework encompasses plausibility judgments, decision making, and question asking. Plausibility judgments are modeled by way of Bayesian probability theory, decision making is modeled by way of a Bayesian decision theory, and relevancy
Molecular subtypes of Alzheimer's disease.
Di Fede, Giuseppe; Catania, Marcella; Maderna, Emanuela; Ghidoni, Roberta; Benussi, Luisa; Tonoli, Elisa; Giaccone, Giorgio; Moda, Fabio; Paterlini, Anna; Campagnani, Ilaria; Sorrentino, Stefano; Colombo, Laura; Kubis, Adriana; Bistaffa, Edoardo; Ghetti, Bernardino; Tagliavini, Fabrizio
2018-02-19
Protein misfolding and aggregation is a central feature of several neurodegenerative disorders including Alzheimer's disease (AD), in which assemblies of amyloid β (Aβ) peptides accumulate in the brain in the form of parenchymal and/or vascular amyloid. A widely accepted concept is that AD is characterized by distinct clinical and neuropathological phenotypes. Recent studies revealed that Aβ assemblies might have structural differences among AD brains and that such pleomorphic assemblies can correlate with distinct disease phenotypes. We found that in both sporadic and inherited forms of AD, amyloid aggregates differ in the biochemical composition of Aβ species. These differences affect the physicochemical properties of Aβ assemblies including aggregation kinetics, resistance to degradation by proteases and seeding ability. Aβ-amyloidosis can be induced and propagated in animal models by inoculation of brain extracts containing aggregated Aβ. We found that brain homogenates from AD patients with different molecular profiles of Aβ are able to induce distinct patterns of Aβ-amyloidosis when injected into mice. Overall these data suggest that the assembly of mixtures of Aβ peptides into different Aβ seeds leads to the formation of distinct subtypes of amyloid having distinctive physicochemical and biological properties which result in the generation of distinct AD molecular subgroups.
Verified Subtyping with Traits and Mixins
Directory of Open Access Journals (Sweden)
Asankhaya Sharma
2014-07-01
Full Text Available Traits allow decomposing programs into smaller parts and mixins are a form of composition that resemble multiple inheritance. Unfortunately, in the presence of traits, programming languages like Scala give up on subtyping relation between objects. In this paper, we present a method to check subtyping between objects based on entailment in separation logic. We implement our method as a domain specific language in Scala and apply it on the Scala standard library. We have verified that 67% of mixins used in the Scala standard library do indeed conform to subtyping between the traits that are used to build them.
Bayesian NL interpretation and learning
Zeevat, H.
2011-01-01
Everyday natural language communication is normally successful, even though contemporary computational linguistics has shown that NL is characterised by very high degree of ambiguity and the results of stochastic methods are not good enough to explain the high success rate. Bayesian natural language
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed...
Bayesian image restoration, using configurations
DEFF Research Database (Denmark)
Thorarinsdottir, Thordis Linda
2006-01-01
configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for the salt and pepper noise. The inference in the model is discussed...
Differentiated Bayesian Conjoint Choice Designs
Z. Sándor (Zsolt); M. Wedel (Michel)
2003-01-01
textabstractPrevious conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about
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...
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...
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...
Bayesian estimates of linkage disequilibrium
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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.
3-D contextual Bayesian classifiers
DEFF Research Database (Denmark)
Larsen, Rasmus
In this paper we will consider extensions of a series of Bayesian 2-D contextual classification pocedures proposed by Owen (1984) Hjort & Mohn (1984) and Welch & Salter (1971) and Haslett (1985) to 3 spatial dimensions. It is evident that compared to classical pixelwise classification further...
Bayesian Alternation During Tactile Augmentation
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Caspar Mathias Goeke
2016-10-01
Full Text Available A large number of studies suggest that the integration of multisensory signals by humans is well described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition, rotation only (native condition, and both augmented and native information (bimodal condition. Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants’ responses with a probit model and calculated the just notable difference (JND. Then we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χred2 = 1.67 than the Bayesian integration model (χred2= 4.34. Slightly higher accuracy showed a non-Bayesian winner takes all model (χred2= 1.64, which either used only native or only augmented values per subject for prediction. However the performance of the Bayesian alternation model could be substantially improved (χred2= 1.09 utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in
Topics in Bayesian statistics and maximum entropy
International Nuclear Information System (INIS)
Mutihac, R.; Cicuttin, A.; Cerdeira, A.; Stanciulescu, C.
1998-12-01
Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)
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.
DEFF Research Database (Denmark)
Halim, Adnan; Anonsen, Jan Haug
2017-01-01
Mass spectrometry-based "-omics" technologies are important tools for global and detailed mapping of post-translational modifications. Protein glycosylation is an abundant and important post translational modification widespread throughout all domains of life. Characterization of glycoproteins...... and research in this area is rapidly accelerating. Here, we review recent developments in glycoproteomic technologies with a special focus on microbial protein glycosylation....
Spatiotemporal dynamics of the HIV-1 subtype G epidemic in West and Central Africa.
Delatorre, Edson; Mir, Daiana; Bello, Gonzalo
2014-01-01
The human immunodeficiency virus type 1 (HIV-1) subtype G is the second most prevalent HIV-1 clade in West Africa, accounting for nearly 30% of infections in the region. There is no information about the spatiotemporal dynamics of dissemination of this HIV-1 clade in Africa. To this end, we analyzed a total of 305 HIV-1 subtype G pol sequences isolated from 11 different countries from West and Central Africa over a period of 20 years (1992 to 2011). Evolutionary, phylogeographic and demographic parameters were jointly estimated from sequence data using a Bayesian coalescent-based method. Our analyses indicate that subtype G most probably emerged in Central Africa in 1968 (1956-1976). From Central Africa, the virus was disseminated to West and West Central Africa at multiple times from the middle 1970s onwards. Two subtype G strains probably introduced into Nigeria and Togo between the middle and the late 1970s were disseminated locally and to neighboring countries, leading to the origin of two major western African clades (G WA-I and G WA-II). Subtype G clades circulating in western and central African regions displayed an initial phase of exponential growth followed by a decline in growth rate since the early/middle 1990 s; but the mean epidemic growth rate of G WA-I (0.75 year-1) and G WA-II (0.95 year-1) clades was about two times higher than that estimated for central African lineages (0.47 year-1). Notably, the overall evolutionary and demographic history of G WA-I and G WA-II clades was very similar to that estimated for the CRF06_cpx clade circulating in the same region. These results support the notion that the spatiotemporal dissemination dynamics of major HIV-1 clades circulating in western Africa have probably been shaped by the same ecological factors.
Hypertension Subtypes among Hypertensive Patients in Ibadan
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Abiodun M. Adeoye
2014-01-01
Full Text Available Background. Certain hypertension subtypes have been shown to increase the risk for cardiovascular morbidity and mortality and may be related to specific underlying genetic determinants. Inappropriate characterization of subtypes of hypertension makes efforts at elucidating the genetic contributions to the etiology of hypertension largely vapid. We report the hypertension subtypes among patients with hypertension from South-Western Nigeria. Methods. A total of 1858 subjects comprising 76% female, hypertensive, aged 18 and above were recruited into the study from two centers in Ibadan, Nigeria. Hypertension was identified using JNCVII definition and was further grouped into four subtypes: controlled hypertension (CH, isolated systolic hypertension (ISH, isolated diastolic hypertension (IDH, and systolic-diastolic hypertension (SDH. Results. Systolic-diastolic hypertension was the most prevalent. Whereas SDH (77.6% versus 73.5% and IDH (4.9% versus 4.7% were more prevalent among females, ISH (10.1% versus 6.2% was higher among males (P=0.048. Female subjects were more obese (P<0.0001 and SDH was prevalent among the obese group. Conclusion. Gender and obesity significantly influenced the distribution of the hypertension subtypes. Characterization of hypertension by subtypes in genetic association studies could lead to identification of previously unknown genetic variants involved in the etiology of hypertension. Large-scale studies among various ethnic groups may be needed to confirm these observations.
Interferon α subtypes in HIV infection.
Sutter, Kathrin; Dickow, Julia; Dittmer, Ulf
2018-02-13
Type I interferons (IFN), which are immediately induced after most virus infections, are central for direct antiviral immunity and link innate and adaptive immune responses. However, several viruses have evolved strategies to evade the IFN response by preventing IFN induction or blocking IFN signaling pathways. Thus, therapeutic application of exogenous type I IFN or agonists inducing type I IFN responses are a considerable option for future immunotherapies against chronic viral infections. An important part of the type I IFN family are 12 IFNα subtypes, which all bind the same receptor, but significantly differ in their biological activities. Up to date only one IFNα subtype (IFNα2) is being used in clinical treatment against chronic virus infections, however its therapeutic success rate is rather limited, especially during Human Immunodeficiency Virus (HIV) infection. Recent studies addressed the important question if other IFNα subtypes would be more potent against retroviral infections in in vitro and in vivo experiments. Indeed, very potent IFNα subtypes were defined and their antiviral and immunomodulatory properties were characterized. In this review we summarize the recent findings on the role of individual IFNα subtypes during HIV and Simian Immunodeficiency Virus infection. This includes their induction during HIV/SIV infection, their antiretroviral activity and the regulation of immune response against HIV by different IFNα subtypes. The findings might facilitate novel strategies for HIV cure or functional cure studies. Copyright © 2018 Elsevier Ltd. All rights reserved.
Bhosale, Prakash; Bernstein, Paul S
2005-09-01
Xanthophylls are oxygenated carotenoids abundant in the human food supply. Lutein, zeaxanthin, and cryptoxanthin are major xanthophyll carotenoids in human plasma. The consumption of these xanthophylls is directly associated with reduction in the risk of cancers, cardiovascular disease, age-related macular degeneration, and cataract formation. Canthaxanthin and astaxanthin also have considerable importance in aquaculture for salmonid and crustacean pigmentation, and are of commercial interest for the pharmaceutical and food industries. Chemical synthesis is a major source for the heavy demand of xanthophylls in the consumer market; however, microbial producers also have potential as commercial sources. In this review, we discuss the biosynthesis, commercial utility, and major microbial sources of xanthophylls. We also present a critical review of current research and technologies involved in promoting microbes as potential commercial sources for mass production.
Bayesian estimation methods in metrology
International Nuclear Information System (INIS)
Cox, M.G.; Forbes, A.B.; Harris, P.M.
2004-01-01
In metrology -- the science of measurement -- a measurement result must be accompanied by a statement of its associated uncertainty. The degree of validity of a measurement result is determined by the validity of the uncertainty statement. In recognition of the importance of uncertainty evaluation, the International Standardization Organization in 1995 published the Guide to the Expression of Uncertainty in Measurement and the Guide has been widely adopted. The validity of uncertainty statements is tested in interlaboratory comparisons in which an artefact is measured by a number of laboratories and their measurement results compared. Since the introduction of the Mutual Recognition Arrangement, key comparisons are being undertaken to determine the degree of equivalence of laboratories for particular measurement tasks. In this paper, we discuss the possible development of the Guide to reflect Bayesian approaches and the evaluation of key comparison data using Bayesian estimation methods
Deep Learning and Bayesian Methods
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Prosper Harrison B.
2017-01-01
Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.
Bayesian inference on proportional elections.
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Gabriel Hideki Vatanabe Brunello
Full Text Available Polls for majoritarian voting systems usually show estimates of the percentage of votes for each candidate. However, proportional vote systems do not necessarily guarantee the candidate with the most percentage of votes will be elected. Thus, traditional methods used in majoritarian elections cannot be applied on proportional elections. In this context, the purpose of this paper was to perform a Bayesian inference on proportional elections considering the Brazilian system of seats distribution. More specifically, a methodology to answer the probability that a given party will have representation on the chamber of deputies was developed. Inferences were made on a Bayesian scenario using the Monte Carlo simulation technique, and the developed methodology was applied on data from the Brazilian elections for Members of the Legislative Assembly and Federal Chamber of Deputies in 2010. A performance rate was also presented to evaluate the efficiency of the methodology. Calculations and simulations were carried out using the free R statistical software.
BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS
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Thordis Linda Thorarinsdottir
2011-05-01
Full Text Available In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.
Space Shuttle RTOS Bayesian Network
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
Multiview Bayesian Correlated Component Analysis
DEFF Research Database (Denmark)
Kamronn, Simon Due; Poulsen, Andreas Trier; Hansen, Lars Kai
2015-01-01
are identical. Here we propose a hierarchical probabilistic model that can infer the level of universality in such multiview data, from completely unrelated representations, corresponding to canonical correlation analysis, to identical representations as in correlated component analysis. This new model, which...... we denote Bayesian correlated component analysis, evaluates favorably against three relevant algorithms in simulated data. A well-established benchmark EEG data set is used to further validate the new model and infer the variability of spatial representations across multiple subjects....
12th Brazilian Meeting on Bayesian Statistics
Louzada, Francisco; Rifo, Laura; Stern, Julio; Lauretto, Marcelo
2015-01-01
Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesia...
A Bayesian model for binary Markov chains
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Belkheir Essebbar
2004-02-01
Full Text Available This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.
3rd Bayesian Young Statisticians Meeting
Lanzarone, Ettore; Villalobos, Isadora; Mattei, Alessandra
2017-01-01
This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).
Precise subtyping for synchronous multiparty sessions
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Mariangiola Dezani-Ciancaglini
2016-02-01
Full Text Available The notion of subtyping has gained an important role both in theoretical and applicative domains: in lambda and concurrent calculi as well as in programming languages. The soundness and the completeness, together referred to as the preciseness of subtyping, can be considered from two different points of view: operational and denotational. The former preciseness has been recently developed with respect to type safety, i.e. the safe replacement of a term of a smaller type when a term of a bigger type is expected. The latter preciseness is based on the denotation of a type which is a mathematical object that describes the meaning of the type in accordance with the denotations of other expressions from the language. The result of this paper is the operational and denotational preciseness of the subtyping for a synchronous multiparty session calculus. The novelty of this paper is the introduction of characteristic global types to prove the operational completeness.
International Nuclear Information System (INIS)
Sharpe, V.J.
1985-10-01
The long term safety and integrity of radioactive waste disposal sites proposed for use by Ontario Hydro may be affected by the release of radioactive gases. Microbes mediate the primary pathways of waste degradation and hence an assessment of their potential to produce gaseous end products from the breakdown of low level waste was performed. Due to a number of unknown variables, assumptions were made regarding environmental and waste conditions that controlled microbial activity; however, it was concluded that 14 C and 3 H would be produced, albeit over a long time scale of about 1500 years for 14 C in the worst case situation
Bayesian Methods and Universal Darwinism
Campbell, John
2009-12-01
Bayesian methods since the time of Laplace have been understood by their practitioners as closely aligned to the scientific method. Indeed a recent Champion of Bayesian methods, E. T. Jaynes, titled his textbook on the subject Probability Theory: the Logic of Science. Many philosophers of science including Karl Popper and Donald Campbell have interpreted the evolution of Science as a Darwinian process consisting of a `copy with selective retention' algorithm abstracted from Darwin's theory of Natural Selection. Arguments are presented for an isomorphism between Bayesian Methods and Darwinian processes. Universal Darwinism, as the term has been developed by Richard Dawkins, Daniel Dennett and Susan Blackmore, is the collection of scientific theories which explain the creation and evolution of their subject matter as due to the Operation of Darwinian processes. These subject matters span the fields of atomic physics, chemistry, biology and the social sciences. The principle of Maximum Entropy states that Systems will evolve to states of highest entropy subject to the constraints of scientific law. This principle may be inverted to provide illumination as to the nature of scientific law. Our best cosmological theories suggest the universe contained much less complexity during the period shortly after the Big Bang than it does at present. The scientific subject matter of atomic physics, chemistry, biology and the social sciences has been created since that time. An explanation is proposed for the existence of this subject matter as due to the evolution of constraints in the form of adaptations imposed on Maximum Entropy. It is argued these adaptations were discovered and instantiated through the Operations of a succession of Darwinian processes.
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.
Bayesian flood forecasting methods: A review
Han, Shasha; Coulibaly, Paulin
2017-08-01
Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been
Patiño-Galindo, Juan Ángel; Torres-Puente, Manoli; Bracho, María Alma; Alastrué, Ignacio; Juan, Amparo; Navarro, David; Galindo, María José; Gimeno, Concepción; Ortega, Enrique; González-Candelas, Fernando
2017-01-01
We describe and characterize an exceptionally large HIV-1 subtype B transmission cluster occurring in the Comunidad Valenciana (CV, Spain). A total of 1806 HIV-1 protease-reverse transcriptase (PR/RT) sequences from different patients were obtained in the CV between 2004 and 2014. After subtyping and generating a phylogenetic tree with additional HIV-1 subtype B sequences, a very large transmission cluster which included almost exclusively sequences from the CV was detected (n = 143 patients). This cluster was then validated and characterized with further maximum-likelihood phylogenetic analyses and Bayesian coalescent reconstructions. With these analyses, the CV cluster was delimited to 113 patients, predominately men who have sex with men (MSM). Although it was significantly located in the city of Valencia (n = 105), phylogenetic analyses suggested this cluster derives from a larger HIV lineage affecting other Spanish localities (n = 194). Coalescent analyses estimated its expansion in Valencia to have started between 1998 and 2004. From 2004 to 2009, members of this cluster represented only 1.46% of the HIV-1 subtype B samples studied in Valencia (n = 5/143), whereas from 2010 onwards its prevalence raised to 12.64% (n = 100/791). In conclusion, we have detected a very large transmission cluster in the CV where it has experienced a very fast growth in the recent years in the city of Valencia, thus contributing significantly to the HIV epidemic in this locality. Its transmission efficiency evidences shortcomings in HIV control measures in Spain and particularly in Valencia.
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Bayesian inference for Hawkes processes
DEFF Research Database (Denmark)
Rasmussen, Jakob Gulddahl
2013-01-01
The Hawkes process is a practically and theoretically important class of point processes, but parameter-estimation for such a process can pose various problems. In this paper we explore and compare two approaches to Bayesian inference. The first approach is based on the so-called conditional...... intensity function, while the second approach is based on an underlying clustering and branching structure in the Hawkes process. For practical use, MCMC (Markov chain Monte Carlo) methods are employed. The two approaches are compared numerically using three examples of the Hawkes process....
Numeracy, frequency, and Bayesian reasoning
Directory of Open Access Journals (Sweden)
Gretchen B. Chapman
2009-02-01
Full Text Available Previous research has demonstrated that Bayesian reasoning performance is improved if uncertainty information is presented as natural frequencies rather than single-event probabilities. A questionnaire study of 342 college students replicated this effect but also found that the performance-boosting benefits of the natural frequency presentation occurred primarily for participants who scored high in numeracy. This finding suggests that even comprehension and manipulation of natural frequencies requires a certain threshold of numeracy abilities, and that the beneficial effects of natural frequency presentation may not be as general as previously believed.
A Bayesian method for detecting pairwise associations in compositional data.
Directory of Open Access Journals (Sweden)
Emma Schwager
2017-11-01
Full Text Available Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.
Genetic contributions to subtypes of aggression
Ligthart, R.S.L.; Bartels, M.; Hoekstra, R.A.; Hudziak, J.; Boomsma, D.I.
2005-01-01
Boys and girls may display different styles of aggression. The aim of this study was to identify subtypes of aggression within the Child Behavior Checklist (CBCL) aggression scale, and determine their characteristics for both sexes. Maternal CBCL ratings of 7449 7-year-old twin pairs were analyzed
Obesity and risk of ovarian cancer subtypes
DEFF Research Database (Denmark)
Olsen, Catherine M; Nagle, Christina M; Whiteman, David C
2013-01-01
Whilst previous studies have reported that higher BMI increases a woman's risk of developing ovarian cancer, associations for the different histological subtypes have not been well defined. As the prevalence of obesity has increased dramatically, and classification of ovarian histology has improv...
Parkinson's disease motor subtypes and mood.
Burn, David J; Landau, Sabine; Hindle, John V; Samuel, Michael; Wilson, Kenneth C; Hurt, Catherine S; Brown, Richard G
2012-03-01
Parkinson's disease is heterogeneous, both in terms of motor symptoms and mood. Identifying associations between phenotypic variants of motor and mood subtypes may provide clues to understand mechanisms underlying mood disorder and symptoms in Parkinson's disease. A total of 513 patients were assessed using the Hospital Anxiety and Depression Scale, and separately classified into anxious, depressed, and anxious-depressed mood classes based on latent class analysis of a semistructured interview. Motor subtypes assessed related to age-of-onset, rate of progression, presence of motor fluctuations, lateralization of motor symptoms, tremor dominance, and the presence of postural instability and gait symptoms and falls. The directions of observed associations tended to support previous findings with the exception of lateralization of symptoms, for which there were no consistent or significant results. Regression models examining a range of motor subtypes together indicated increased risk of anxiety in patients with younger age-of-onset and motor fluctuations. In contrast, depression was most strongly related to axial motor symptoms. Different risk factors were observed for depressed patients with and without anxiety, suggesting heterogeneity within Parkinson's disease depression. Such association data may suggest possible underlying common risk factors for motor subtype and mood. Combined with convergent evidence from other sources, possible mechanisms may include cholinergic system damage and white matter changes contributing to non-anxious depression in Parkinson's disease, while situational factors related to threat and unpredictability may contribute to the exacerbation and maintenance of anxiety in susceptible individuals. Copyright © 2011 Movement Disorder Society.
Subtyping can have a simple semantics
Balsters, H.; Fokkinga, M.M.
1991-01-01
Consider a first order typed language, with semantics $S$ for expressions and types. Adding subtyping means that a partial order $<$; on types is defined and that the typing rules are extended to the effect that expression $e$ has type $t$ whenever $e$ has type $s$ and $s
Subtypes of children with attention disabilities.
Brand, E.F.J.M.; Das-Smaal, E.A.; Jong, de P.F.
1996-01-01
Subtypes of children with attentional problems were investigated using cluster analysis. Subjects were 9-year-old-elementary school children (N = 443). The test battery administered to these children comprised a comprehensive set of common attention tests, covering different aspects of attentional
Bayesian analysis of magnetic island dynamics
International Nuclear Information System (INIS)
Preuss, R.; Maraschek, M.; Zohm, H.; Dose, V.
2003-01-01
We examine a first order differential equation with respect to time used to describe magnetic islands in magnetically confined plasmas. The free parameters of this equation are obtained by employing Bayesian probability theory. Additionally, a typical Bayesian change point is solved in the process of obtaining the data
Learning dynamic Bayesian networks with mixed variables
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...
Using Bayesian Networks to Improve Knowledge Assessment
Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra
2013-01-01
In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…
Using Bayesian belief networks in adaptive management.
J.B. Nyberg; B.G. Marcot; R. Sulyma
2006-01-01
Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...
Bayesian Decision Theoretical Framework for Clustering
Chen, Mo
2011-01-01
In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…
Robust Bayesian detection of unmodelled bursts
International Nuclear Information System (INIS)
Searle, Antony C; Sutton, Patrick J; Tinto, Massimo; Woan, Graham
2008-01-01
We develop a Bayesian treatment of the problem of detecting unmodelled gravitational wave bursts using the new global network of interferometric detectors. We also compare this Bayesian treatment with existing coherent methods, and demonstrate that the existing methods make implicit assumptions on the distribution of signals that make them sub-optimal for realistic signal populations
Bayesian models: A statistical primer for ecologists
Hobbs, N. Thompson; Hooten, Mevin B.
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models
Particle identification in ALICE: a Bayesian approach
Adam, J.; Adamova, D.; Aggarwal, M. M.; Rinella, G. Aglieri; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anticic, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshaeuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badala, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnafoeldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Bathen, B.; Batigne, G.; Camejo, A. Batista; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Benacek, P.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielcik, J.; Bielcikova, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Boggild, H.; Boldizsar, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossu, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Diaz, L. Calero; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castellanos, J. Castillo; Castro, A. J.; Casula, E. A. R.; Sanchez, C. Ceballos; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Barroso, V. Chibante; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Balbastre, G. Conesa; del Valle, Z. Conesa; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Morales, Y. Corrales; Cortes Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Denes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Corchero, M. A. Diaz; Dietel, T.; Dillenseger, P.; Divia, R.; Djuvsland, O.; Dobrin, A.; Gimenez, D. Domenicis; Doenigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernandez Tellez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Girard, M. Fusco; Gaardhoje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, A.; Gheata, M.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glaessel, P.; Gomez Coral, D. M.; Ramirez, A. Gomez; Gonzalez, A. S.; Gonzalez, V.; Gonzalez-Zamora, P.; Gorbunov, S.; Goerlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Haake, R.; Haaland, O.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbaer, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacazio, N.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Bustamante, R. T. Jimenez; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kaplin, V.; Kar, S.; Uysal, A. Karasu; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Khan, M. Mohisin; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, J. S.; Kim, M.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein-Boesing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kostarakis, P.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Meethaleveedu, G. Koyithatta; Kralik, I.; Kravcakova, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kucera, V.; Kuijer, P. G.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; Monzon, I. Leon; Leon Vargas, H.; Leoncino, M.; Levai, P.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; Torres, E. Lopez; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mares, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marin, A.; Markert, C.; Marquard, M.; Martin, N. A.; Blanco, J. Martin; Martinengo, P.; Martinez, M. I.; Garcia, G. Martinez; Pedreira, M. Martinez; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Perez, J. Mercado; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montano Zetina, L.; Montes, E.; De Godoy, D. A. Moreira; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Muehlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paic, G.; Pal, S. K.; Pan, J.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Da Costa, H. Pereira; Peresunko, D.; Lara, C. E. Perez; Lezama, E. Perez; Peskov, V.; Pestov, Y.; Petracek, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Raesaenen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodriguez Cahuantzi, M.; Manso, A. Rodriguez; Roed, K.; Rogochaya, E.; Rohr, D.; Roehrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Montero, A. J. Rubio; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Safarik, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Sefcik, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shahzad, M. I.; Shangaraev, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; de Souza, R. D.; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Sumbera, M.; Sumowidagdo, S.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Munoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thaeder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Palomo, L. Valencia; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vyvre, P. Vande; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Vercellin, E.; Vergara Limon, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Baillie, O. Villalobos; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Voelkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrlakova, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Watanabe, D.; Watanabe, Y.; Weiser, D. F.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yang, H.; Yano, S.; Yasin, Z.; Yokoyama, H.; Yoo, I. -K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Zavada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, C.; Zhao, C.; Zhigareva, N.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.
2016-01-01
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian
Advances in Bayesian Modeling in Educational Research
Levy, Roy
2016-01-01
In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…
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...
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...
BELM: Bayesian extreme learning machine.
Soria-Olivas, Emilio; Gómez-Sanchis, Juan; Martín, José D; Vila-Francés, Joan; Martínez, Marcelino; Magdalena, José R; Serrano, Antonio J
2011-03-01
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.
Bayesian Nonparametric Longitudinal Data Analysis.
Quintana, Fernando A; Johnson, Wesley O; Waetjen, Elaine; Gold, Ellen
2016-01-01
Practical Bayesian nonparametric methods have been developed across a wide variety of contexts. Here, we develop a novel statistical model that generalizes standard mixed models for longitudinal data that include flexible mean functions as well as combined compound symmetry (CS) and autoregressive (AR) covariance structures. AR structure is often specified through the use of a Gaussian process (GP) with covariance functions that allow longitudinal data to be more correlated if they are observed closer in time than if they are observed farther apart. We allow for AR structure by considering a broader class of models that incorporates a Dirichlet Process Mixture (DPM) over the covariance parameters of the GP. We are able to take advantage of modern Bayesian statistical methods in making full predictive inferences and about characteristics of longitudinal profiles and their differences across covariate combinations. We also take advantage of the generality of our model, which provides for estimation of a variety of covariance structures. We observe that models that fail to incorporate CS or AR structure can result in very poor estimation of a covariance or correlation matrix. In our illustration using hormone data observed on women through the menopausal transition, biology dictates the use of a generalized family of sigmoid functions as a model for time trends across subpopulation categories.
2nd Bayesian Young Statisticians Meeting
Bitto, Angela; Kastner, Gregor; Posekany, Alexandra
2015-01-01
The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session ...
Bayesian natural language semantics and pragmatics
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.
Muscarinic acetylcholine receptor subtypes: localization and structure/function
DEFF Research Database (Denmark)
Brann, M R; Ellis, J; Jørgensen, H
1993-01-01
Based on the sequence of the five cloned muscarinic receptor subtypes (m1-m5), subtype selective antibody and cDNA probes have been prepared. Use of these probes has demonstrated that each of the five subtypes has a markedly distinct distribution within the brain and among peripheral tissues...... are described, as well as the implied structures of these functional domains....
Subtypes of depression in cancer patients : An empirically driven approach
Zhu, Lei; Ranchor, Adelita V; van der Lee, Marije; Garssen, Bert; Sanderman, Robbert; Schroevers, Maya J
PURPOSE: This study aimed to (1) identify subgroups of cancer patients with distinct subtypes of depression before the start of psychological care, (2) examine whether socio-demographic and medical characteristics distinguished these subtypes, and (3) examine whether people with distinct subtypes
Subtypes of depression in cancer patients: an empirically driven approach
Zhu, Lei; Ranchor, A.V.; van der Lee, Marije; Garssen, Bert; Sanderman, Robbert; Schroevers, Maya J.
2016-01-01
Purpose This study aimed to (1) identify subgroups of cancer patients with distinct subtypes of depression before the start of psychological care, (2) examine whether socio-demographic and medical characteristics distinguished these subtypes, and (3) examine whether people with distinct subtypes
Assessing the genetic architecture of epithelial ovarian cancer histological subtypes
DEFF Research Database (Denmark)
Cuellar-Partida, Gabriel; Lu, Yi; Dixon, Suzanne C
2016-01-01
studies show that certain genetic variants confer susceptibility to all subtypes while other variants are subtype-specific. Here, we perform an extensive analysis of the genetic architecture of EOC subtypes. To this end, we used data of 10,014 invasive EOC patients and 21,233 controls from the Ovarian...
A Bayesian Reflection on Surfaces
Directory of Open Access Journals (Sweden)
David R. Wolf
1999-10-01
Full Text Available Abstract: The topic of this paper is a novel Bayesian continuous-basis field representation and inference framework. Within this paper several problems are solved: The maximally informative inference of continuous-basis fields, that is where the basis for the field is itself a continuous object and not representable in a finite manner; the tradeoff between accuracy of representation in terms of information learned, and memory or storage capacity in bits; the approximation of probability distributions so that a maximal amount of information about the object being inferred is preserved; an information theoretic justification for multigrid methodology. The maximally informative field inference framework is described in full generality and denoted the Generalized Kalman Filter. The Generalized Kalman Filter allows the update of field knowledge from previous knowledge at any scale, and new data, to new knowledge at any other scale. An application example instance, the inference of continuous surfaces from measurements (for example, camera image data, is presented.
Attention in a bayesian framework
DEFF Research Database (Denmark)
Whiteley, Louise Emma; Sahani, Maneesh
2012-01-01
, and include both selective phenomena, where attention is invoked by cues that point to particular stimuli, and integrative phenomena, where attention is invoked dynamically by endogenous processing. However, most previous Bayesian accounts of attention have focused on describing relatively simple experimental...... selective and integrative roles, and thus cannot be easily extended to complex environments. We suggest that the resource bottleneck stems from the computational intractability of exact perceptual inference in complex settings, and that attention reflects an evolved mechanism for approximate inference which...... can be shaped to refine the local accuracy of perception. We show that this approach extends the simple picture of attention as prior, so as to provide a unified and computationally driven account of both selective and integrative attentional phenomena....
On Bayesian System Reliability Analysis
Energy Technology Data Exchange (ETDEWEB)
Soerensen Ringi, M
1995-05-01
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person`s state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs.
Nonparametric Bayesian inference in biostatistics
Müller, Peter
2015-01-01
As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters c...
On Bayesian System Reliability Analysis
International Nuclear Information System (INIS)
Soerensen Ringi, M.
1995-01-01
The view taken in this thesis is that reliability, the probability that a system will perform a required function for a stated period of time, depends on a person's state of knowledge. Reliability changes as this state of knowledge changes, i.e. when new relevant information becomes available. Most existing models for system reliability prediction are developed in a classical framework of probability theory and they overlook some information that is always present. Probability is just an analytical tool to handle uncertainty, based on judgement and subjective opinions. It is argued that the Bayesian approach gives a much more comprehensive understanding of the foundations of probability than the so called frequentistic school. A new model for system reliability prediction is given in two papers. The model encloses the fact that component failures are dependent because of a shared operational environment. The suggested model also naturally permits learning from failure data of similar components in non identical environments. 85 refs
Bayesian estimation in homodyne interferometry
International Nuclear Information System (INIS)
Olivares, Stefano; Paris, Matteo G A
2009-01-01
We address phase-shift estimation by means of squeezed vacuum probe and homodyne detection. We analyse Bayesian estimator, which is known to asymptotically saturate the classical Cramer-Rao bound to the variance, and discuss convergence looking at the a posteriori distribution as the number of measurements increases. We also suggest two feasible adaptive methods, acting on the squeezing parameter and/or the homodyne local oscillator phase, which allow us to optimize homodyne detection and approach the ultimate bound to precision imposed by the quantum Cramer-Rao theorem. The performances of our two-step methods are investigated by means of Monte Carlo simulated experiments with a small number of homodyne data, thus giving a quantitative meaning to the notion of asymptotic optimality.
Bayesian Kernel Mixtures for Counts.
Canale, Antonio; Dunson, David B
2011-12-01
Although Bayesian nonparametric mixture models for continuous data are well developed, there is a limited literature on related approaches for count data. A common strategy is to use a mixture of Poissons, which unfortunately is quite restrictive in not accounting for distributions having variance less than the mean. Other approaches include mixing multinomials, which requires finite support, and using a Dirichlet process prior with a Poisson base measure, which does not allow smooth deviations from the Poisson. As a broad class of alternative models, we propose to use nonparametric mixtures of rounded continuous kernels. An efficient Gibbs sampler is developed for posterior computation, and a simulation study is performed to assess performance. Focusing on the rounded Gaussian case, we generalize the modeling framework to account for multivariate count data, joint modeling with continuous and categorical variables, and other complications. The methods are illustrated through applications to a developmental toxicity study and marketing data. This article has supplementary material online.
Bayesian networks in educational assessment
Almond, Russell G; Steinberg, Linda S; Yan, Duanli; Williamson, David M
2015-01-01
Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets’ foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volume’s grounding in Evidence-Centered Design (ECD) framework for assessment design. This “design forward” approach enables designers to take full advantage of Bayes nets’ modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as ...
Bokulich, Nicholas A; Bergsveinson, Jordyn; Ziola, Barry; Mills, David A
2015-03-10
Distinct microbial ecosystems have evolved to meet the challenges of indoor environments, shaping the microbial communities that interact most with modern human activities. Microbial transmission in food-processing facilities has an enormous impact on the qualities and healthfulness of foods, beneficially or detrimentally interacting with food products. To explore modes of microbial transmission and spoilage-gene frequency in a commercial food-production scenario, we profiled hop-resistance gene frequencies and bacterial and fungal communities in a brewery. We employed a Bayesian approach for predicting routes of contamination, revealing critical control points for microbial management. Physically mapping microbial populations over time illustrates patterns of dispersal and identifies potential contaminant reservoirs within this environment. Habitual exposure to beer is associated with increased abundance of spoilage genes, predicting greater contamination risk. Elucidating the genetic landscapes of indoor environments poses important practical implications for food-production systems and these concepts are translatable to other built environments.
Comparison of symptoms of delirium across various motoric subtypes.
Grover, Sandeep; Sharma, Akhilesh; Aggarwal, Munish; Mattoo, Surendra K; Chakrabarti, Subho; Malhotra, Savita; Avasthi, Ajit; Kulhara, Parmanand; Basu, Debasish
2014-04-01
The aim of this study was to determine the correlation between delirium motor subtypes and other symptoms of delirium. Three hundred and twenty-one (n = 321) consecutive patients referred to consultation-liaison psychiatry services were evaluated on Delirium Rating scale-Revised-98 version and amended Delirium Motor Symptom Scale. Half of the patients had hyperactive subtype (n = 161; 50.15%) delirium. One-quarter of the study sample met the criteria for mixed subtype (n = 79; 24.61%), about one-fifth of the study sample met the criteria for hypoactive delirium subtype (n = 64; 19.93%), and only very few patients (n = 17; 5.29%) did not meet the required criteria for any of these three subtypes and were categorized as 'no subtype'. When the hyperactive and hypoactive subtypes were compared, significant differences were seen in the prevalence of perceptual disturbances, delusions, lability of affect, thought process abnormality, motor agitation and motor retardation. All the symptoms were more common in the hyperactive subtype except for thought process abnormality and motor retardation. Compared to hyperactive subtype, the mixed subtype had significantly higher prevalence of thought process abnormality and motor retardation. Significant differences emerged with regard to perceptual disturbances, delusions, lability of affect and motor agitation when comparing the patients with mixed subtype with those with hypoactive subtype. All these symptoms were found to be more common in the mixed subtype. No significant differences emerged for the cognitive symptoms as assessed on Delirium Rating scale-Revised-98 across the different motoric subtypes. Different motoric subtypes of delirium differ on non-cognitive symptoms. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.
A taxometric investigation of developmental dyslexia subtypes.
O'Brien, Beth A; Wolf, Maryanne; Lovett, Maureen W
2012-02-01
Long-standing issues with the conceptualization, identification and subtyping of developmental dyslexia persist. This study takes an alternative approach to examine the heterogeneity of developmental dyslexia using taxometric classification techniques. These methods were used with a large sample of 671 children ages 6-8 who were diagnosed with severe reading disorders. Latent characteristics of the sample are assessed in regard to posited subtypes with phonological deficits and naming speed deficits, thus extending prior work by addressing whether these deficits embody separate classes of individuals. Findings support separate taxa of dyslexia with and without phonological deficits. Different latent structure for naming speed deficits was found depending on the definitional criterion used to define dyslexia. Non-phonologically based forms of dyslexia showed particular difficulty with naming speed and reading fluency. Copyright © 2012 John Wiley & Sons, Ltd.
Proteomic maps of breast cancer subtypes
DEFF Research Database (Denmark)
Tyanova, Stefka; Albrechtsen, Reidar; Kronqvist, Pauliina
2016-01-01
Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analysed 40...... oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell......-cell communication. Furthermore, we derived a signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were...
Robust bayesian analysis of an autoregressive model with ...
African Journals Online (AJOL)
In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...
McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T
2014-06-01
Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.
Bayesian models a statistical primer for ecologists
Hobbs, N Thompson
2015-01-01
Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili
Agonist discrimination between AMPA receptor subtypes
DEFF Research Database (Denmark)
Coquelle, T; Christensen, J K; Banke, T G
2000-01-01
The lack of subtype-selective compounds for AMPA receptors (AMPA-R) led us to search for compounds with such selectivity. Homoibotenic acid analogues were investigated at recombinant GluR1o, GluR2o(R), GluR3o and GluR1o + 3o receptors expressed in Sf9 insect cells and affinities determined in [3H...
Global DNA methylation of ischemic stroke subtypes.
Directory of Open Access Journals (Sweden)
Carolina Soriano-Tárraga
Full Text Available Ischemic stroke (IS, a heterogeneous multifactorial disorder, is among the leading causes of mortality and long-term disability in the western world. Epidemiological data provides evidence for a genetic component to the disease, but its epigenetic involvement is still largely unknown. Epigenetic mechanisms, such as DNA methylation, change over time and may be associated with aging processes and with modulation of the risk of various pathologies, such as cardiovascular disease and stroke. We analyzed 2 independent cohorts of IS patients. Global DNA methylation was measured by luminometric methylation assay (LUMA of DNA blood samples. Univariate and multivariate regression analyses were used to assess the methylation differences between the 3 most common IS subtypes, large-artery atherosclerosis (LAA, small-artery disease (SAD, and cardio-aortic embolism (CE. A total of 485 IS patients from 2 independent hospital cohorts (n = 281 and n = 204 were included, distributed across 3 IS subtypes: LAA (78/281, 59/204, SAD (97/281, 53/204, and CE (106/281, 89/204. In univariate analyses, no statistical differences in LUMA levels were observed between the 3 etiologies in either cohort. Multivariate analysis, adjusted by age, sex, hyperlipidemia, and smoking habit, confirmed the lack of differences in methylation levels between the analyzed IS subtypes in both cohorts. Despite differences in pathogenesis, our results showed no global methylation differences between LAA, SAD, and CE subtypes of IS. Further work is required to establish whether the epigenetic mechanism of methylation might play a role in this complex disease.
Subtyping borderline personality disorder by suicidal behavior.
Soloff, Paul H; Chiappetta, Laurel
2012-06-01
Course and outcome of Borderline Personality Disorder (BPD) are favorable for the vast majority of patients; however, up to 10% die by suicide. This discrepancy begs the question of whether there is a high lethality subtype in BPD, defined by recurrent suicidal behavior and increasing attempt lethality over time. In a prospective, longitudinal study, we sought predictors of high lethality among repeat attempters, and defined clinical subtypes by applying trajectory analysis to consecutive lethality scores. Criteria-defined subjects with BPD were assessed using standardized instruments and followed longitudinally. Suicidal behavior was assessed on the Columbia Suicide History, Lethality Rating Scale, and Suicide Intent Scale. Variables discriminating single and repeat attempters were entered into logistic regression models to define predictors of high and low lethality attempts. Trajectory analysis using three attempt and five attempt models identified discrete patterns of Lethality Rating Scale scores. A high lethality trajectory was associated with inpatient recruitment, and poor psychosocial function, a low lethality trajectory with greater Negativism, Substance Use Disorders, Histrionic and/or Narcissistic PD co-morbidity. Illness severity, older age, and poor psychosocial function are characteristics of a poor prognosis subtype related to suicidal behavior.
Transcriptome classification reveals molecular subtypes in psoriasis
Directory of Open Access Journals (Sweden)
Ainali Chrysanthi
2012-09-01
Full Text Available Abstract Background Psoriasis is an immune-mediated disease characterised by chronically elevated pro-inflammatory cytokine levels, leading to aberrant keratinocyte proliferation and differentiation. Although certain clinical phenotypes, such as plaque psoriasis, are well defined, it is currently unclear whether there are molecular subtypes that might impact on prognosis or treatment outcomes. Results We present a pipeline for patient stratification through a comprehensive analysis of gene expression in paired lesional and non-lesional psoriatic tissue samples, compared with controls, to establish differences in RNA expression patterns across all tissue types. Ensembles of decision tree predictors were employed to cluster psoriatic samples on the basis of gene expression patterns and reveal gene expression signatures that best discriminate molecular disease subtypes. This multi-stage procedure was applied to several published psoriasis studies and a comparison of gene expression patterns across datasets was performed. Conclusion Overall, classification of psoriasis gene expression patterns revealed distinct molecular sub-groups within the clinical phenotype of plaque psoriasis. Enrichment for TGFb and ErbB signaling pathways, noted in one of the two psoriasis subgroups, suggested that this group may be more amenable to therapies targeting these pathways. Our study highlights the potential biological relevance of using ensemble decision tree predictors to determine molecular disease subtypes, in what may initially appear to be a homogenous clinical group. The R code used in this paper is available upon request.
Molecular subtypes and imaging phenotypes of breast cancer
International Nuclear Information System (INIS)
Cho, Nariya
2016-01-01
During the last 15 years, traditional breast cancer classifications based on histopathology have been reorganized into the luminal A, luminal B, human epidermal growth factor receptor 2 (HER2), and basal-like subtypes based on gene expression profiling. Each molecular subtype has shown varying risk for progression, response to treatment, and survival outcomes. Research linking the imaging phenotype with the molecular subtype has revealed that non-calcified, relatively circumscribed masses with posterior acoustic enhancement are common in the basal-like subtype, spiculated masses with a poorly circumscribed margin and posterior acoustic shadowing in the luminal subtype, and pleomorphic calcifications in the HER2-enriched subtype. Understanding the clinical implications of the molecular subtypes and imaging phenotypes could help radiologists guide precision medicine, tailoring medical treatment to patients and their tumor characteristics
Molecular subtypes and imaging phenotypes of breast cancer
Directory of Open Access Journals (Sweden)
Nariya Cho
2016-10-01
Full Text Available During the last 15 years, traditional breast cancer classifications based on histopathology have been reorganized into the luminal A, luminal B, human epidermal growth factor receptor 2 (HER2, and basal-like subtypes based on gene expression profiling. Each molecular subtype has shown varying risk for progression, response to treatment, and survival outcomes. Research linking the imaging phenotype with the molecular subtype has revealed that non-calcified, relatively circumscribed masses with posterior acoustic enhancement are common in the basal-like subtype, spiculated masses with a poorly circumscribed margin and posterior acoustic shadowing in the luminal subtype, and pleomorphic calcifications in the HER2-enriched subtype. Understanding the clinical implications of the molecular subtypes and imaging phenotypes could help radiologists guide precision medicine, tailoring medical treatment to patients and their tumor characteristics.
Molecular subtypes and imaging phenotypes of breast cancer
Energy Technology Data Exchange (ETDEWEB)
Cho, Nariya [Dept. of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of)
2016-08-15
During the last 15 years, traditional breast cancer classifications based on histopathology have been reorganized into the luminal A, luminal B, human epidermal growth factor receptor 2 (HER2), and basal-like subtypes based on gene expression profiling. Each molecular subtype has shown varying risk for progression, response to treatment, and survival outcomes. Research linking the imaging phenotype with the molecular subtype has revealed that non-calcified, relatively circumscribed masses with posterior acoustic enhancement are common in the basal-like subtype, spiculated masses with a poorly circumscribed margin and posterior acoustic shadowing in the luminal subtype, and pleomorphic calcifications in the HER2-enriched subtype. Understanding the clinical implications of the molecular subtypes and imaging phenotypes could help radiologists guide precision medicine, tailoring medical treatment to patients and their tumor characteristics.
Microbial micropatches within microbial hotspots
Smith, Renee J.; Tobe, Shanan S.; Paterson, James S.; Seymour, Justin R.; Oliver, Rod L.; Mitchell, James G.
2018-01-01
The spatial distributions of organism abundance and diversity are often heterogeneous. This includes the sub-centimetre distributions of microbes, which have ‘hotspots’ of high abundance, and ‘coldspots’ of low abundance. Previously we showed that 300 μl abundance hotspots, coldspots and background regions were distinct at all taxonomic levels. Here we build on these results by showing taxonomic micropatches within these 300 μl microscale hotspots, coldspots and background regions at the 1 μl scale. This heterogeneity among 1 μl subsamples was driven by heightened abundance of specific genera. The micropatches were most pronounced within hotspots. Micropatches were dominated by Pseudomonas, Bacteroides, Parasporobacterium and Lachnospiraceae incertae sedis, with Pseudomonas and Bacteroides being responsible for a shift in the most dominant genera in individual hotspot subsamples, representing up to 80.6% and 47.3% average abundance, respectively. The presence of these micropatches implies the ability these groups have to create, establish themselves in, or exploit heterogeneous microenvironments. These genera are often particle-associated, from which we infer that these micropatches are evidence for sub-millimetre aggregates and the aquatic polymer matrix. These findings support the emerging paradigm that the microscale distributions of planktonic microbes are numerically and taxonomically heterogeneous at scales of millimetres and less. We show that microscale microbial hotspots have internal structure within which specific local nutrient exchanges and cellular interactions might occur. PMID:29787564
Bayesian adaptive methods for clinical trials
National Research Council Canada - National Science Library
Berry, Scott M
2011-01-01
.... One is that Bayesian approaches implemented with the majority of their informative content coming from the current data, and not any external prior informa- tion, typically have good frequentist properties (e.g...
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
Structure-based bayesian sparse reconstruction
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
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++
Learning Bayesian networks for discrete data
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.
An Intuitive Dashboard for Bayesian Network Inference
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++.
Bayesian optimization for computationally extensive probability distributions.
Tamura, Ryo; Hukushima, Koji
2018-01-01
An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribution. Our Bayesian optimization technique is applied to the posterior distribution in the effective physical model estimation, which is a computationally extensive probability distribution. Even when the number of sampling points on the posterior distributions is fixed to be small, the Bayesian optimization provides a better maximizer of the posterior distributions in comparison to those by the random search method, the steepest descent method, or the Monte Carlo method. Furthermore, the Bayesian optimization improves the results efficiently by combining the steepest descent method and thus it is a powerful tool to search for a better maximizer of computationally extensive probability distributions.
Correct Bayesian and frequentist intervals are similar
International Nuclear Information System (INIS)
Atwood, C.L.
1986-01-01
This paper argues that Bayesians and frequentists will normally reach numerically similar conclusions, when dealing with vague data or sparse data. It is shown that both statistical methodologies can deal reasonably with vague data. With sparse data, in many important practical cases Bayesian interval estimates and frequentist confidence intervals are approximately equal, although with discrete data the frequentist intervals are somewhat longer. This is not to say that the two methodologies are equally easy to use: The construction of a frequentist confidence interval may require new theoretical development. Bayesians methods typically require numerical integration, perhaps over many variables. Also, Bayesian can easily fall into the trap of over-optimism about their amount of prior knowledge. But in cases where both intervals are found correctly, the two intervals are usually not very different. (orig.)
Implementing the Bayesian paradigm in risk analysis
International Nuclear Information System (INIS)
Aven, T.; Kvaloey, J.T.
2002-01-01
The Bayesian paradigm comprises a unified and consistent framework for analyzing and expressing risk. Yet, we see rather few examples of applications where the full Bayesian setting has been adopted with specifications of priors of unknown parameters. In this paper, we discuss some of the practical challenges of implementing Bayesian thinking and methods in risk analysis, emphasizing the introduction of probability models and parameters and associated uncertainty assessments. We conclude that there is a need for a pragmatic view in order to 'successfully' apply the Bayesian approach, such that we can do the assignments of some of the probabilities without adopting the somewhat sophisticated procedure of specifying prior distributions of parameters. A simple risk analysis example is presented to illustrate ideas
An overview on Approximate Bayesian computation*
Directory of Open Access Journals (Sweden)
Baragatti Meïli
2014-01-01
Full Text Available Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the most satisfactory approach to intractable likelihood problems. This overview presents recent results since its introduction about ten years ago in population genetics.
Bayesian probability theory and inverse problems
International Nuclear Information System (INIS)
Kopec, S.
1994-01-01
Bayesian probability theory is applied to approximate solving of the inverse problems. In order to solve the moment problem with the noisy data, the entropic prior is used. The expressions for the solution and its error bounds are presented. When the noise level tends to zero, the Bayesian solution tends to the classic maximum entropy solution in the L 2 norm. The way of using spline prior is also shown. (author)
A Bayesian classifier for symbol recognition
Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick
2007-01-01
URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...
Bayesian Modeling of a Human MMORPG Player
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.
Variations on Bayesian Prediction and Inference
2016-05-09
inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle
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.
MCMC for parameters estimation by bayesian approach
International Nuclear Information System (INIS)
Ait Saadi, H.; Ykhlef, F.; Guessoum, A.
2011-01-01
This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.
Bayesian Networks for Modeling Dredging Decisions
2011-10-01
years, that algorithms have been developed to solve these problems efficiently. Most modern Bayesian network software uses junction tree (a.k.a. join... software was used to develop the network . This is by no means an exhaustive list of Bayesian network applications, but it is representative of recent...characteristic node (SCN), state- defining node ( SDN ), effect node (EFN), or value node. The five types of nodes can be described as follows: ERDC/EL TR-11
Fully probabilistic design of hierarchical Bayesian models
Czech Academy of Sciences Publication Activity Database
Quinn, A.; Kárný, Miroslav; Guy, Tatiana Valentine
2016-01-01
Roč. 369, č. 1 (2016), s. 532-547 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Fully probabilistic design * Ideal distribution * Minimum cross-entropy principle * Bayesian conditioning * Kullback-Leibler divergence * Bayesian nonparametric modelling Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.832, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0463052.pdf
A Bayesian Method for Weighted Sampling
Lo, Albert Y.
1993-01-01
Bayesian statistical inference for sampling from weighted distribution models is studied. Small-sample Bayesian bootstrap clone (BBC) approximations to the posterior distribution are discussed. A second-order property for the BBC in unweighted i.i.d. sampling is given. A consequence is that BBC approximations to a posterior distribution of the mean and to the sampling distribution of the sample average, can be made asymptotically accurate by a proper choice of the random variables that genera...
Philosophy and the practice of Bayesian statistics.
Gelman, Andrew; Shalizi, Cosma Rohilla
2013-02-01
A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. © 2012 The British Psychological Society.
DEFF Research Database (Denmark)
Stensvold, C. R.; Alfellani, M. A.; Nørskov-Lauritsen, S.
2009-01-01
Blastocystis isolates from 56 Danish synanthropic and zoo animals, 62 primates primarily from United Kingdom (UK) collections and 16 UK primate handlers were subtyped by PCR, sequencing and phylogenetic analysis. A new subtype (ST) from primates and artiodactyls was identified and designated...... infections from primates by their handlers had occurred in these cases. Data from published studies of non-human primates, other mammals and birds were collected and interpreted to generate a comprehensive overview on the ST distribution in such animals. On the basis of information on 438 samples...
EXONEST: The Bayesian Exoplanetary Explorer
Directory of Open Access Journals (Sweden)
Kevin H. Knuth
2017-10-01
Full Text Available The fields of astronomy and astrophysics are currently engaged in an unprecedented era of discovery as recent missions have revealed thousands of exoplanets orbiting other stars. While the Kepler Space Telescope mission has enabled most of these exoplanets to be detected by identifying transiting events, exoplanets often exhibit additional photometric effects that can be used to improve the characterization of exoplanets. The EXONEST Exoplanetary Explorer is a Bayesian exoplanet inference engine based on nested sampling and originally designed to analyze archived Kepler Space Telescope and CoRoT (Convection Rotation et Transits planétaires exoplanet mission data. We discuss the EXONEST software package and describe how it accommodates plug-and-play models of exoplanet-associated photometric effects for the purpose of exoplanet detection, characterization and scientific hypothesis testing. The current suite of models allows for both circular and eccentric orbits in conjunction with photometric effects, such as the primary transit and secondary eclipse, reflected light, thermal emissions, ellipsoidal variations, Doppler beaming and superrotation. We discuss our new efforts to expand the capabilities of the software to include more subtle photometric effects involving reflected and refracted light. We discuss the EXONEST inference engine design and introduce our plans to port the current MATLAB-based EXONEST software package over to the next generation Exoplanetary Explorer, which will be a Python-based open source project with the capability to employ third-party plug-and-play models of exoplanet-related photometric effects.
Maximum entropy and Bayesian methods
International Nuclear Information System (INIS)
Smith, C.R.; Erickson, G.J.; Neudorfer, P.O.
1992-01-01
Bayesian probability theory and Maximum Entropy methods are at the core of a new view of scientific inference. These 'new' ideas, along with the revolution in computational methods afforded by modern computers allow astronomers, electrical engineers, image processors of any type, NMR chemists and physicists, and anyone at all who has to deal with incomplete and noisy data, to take advantage of methods that, in the past, have been applied only in some areas of theoretical physics. The title workshops have been the focus of a group of researchers from many different fields, and this diversity is evident in this book. There are tutorial and theoretical papers, and applications in a very wide variety of fields. Almost any instance of dealing with incomplete and noisy data can be usefully treated by these methods, and many areas of theoretical research are being enhanced by the thoughtful application of Bayes' theorem. Contributions contained in this volume present a state-of-the-art overview that will be influential and useful for many years to come
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
Immunohistochemical visualization of mouse interneuron subtypes
DEFF Research Database (Denmark)
Jensen, Simon Mølgaard; Ulrichsen, Maj; Boggild, Simon
2014-01-01
, and calretinin are also commonly used as markers to narrow down the specific interneuron subtype. Here, we describe a journey to find the necessary immunological reagents for studying GABAergic interneurons of the mouse hippocampus. Based on web searches there are several hundreds of different antibodies...... of the hippocampus where they have previously been described. Additionally, the antibodies were also tested on sections from mouse spinal cord with similar criteria for specificity of the antibodies. Using the antibodies with a high rating on pAbmAbs, stainings with high signal-to-noise ratios and location...
African Journals Online (AJOL)
186–198. Insam H. (1990). Are the soil microbial biomass and basal respiration governed by the climatic regime? Soil. Biol. Biochem. 22: 525–532. Insam H. D. and Domsch K. H. (1989). Influence of microclimate on soil microbial biomass. Soil Biol. Biochem. 21: 211–21. Jenkinson D. S. (1988). Determination of microbial.
Molecular microbial ecology manual
Kowalchuk, G.A.; Bruijn, de F.J.; Head, I.M.; Akkermans, A.D.L.
2004-01-01
The field of microbial ecology has been revolutionized in the past two decades by the introduction of molecular methods into the toolbox of the microbial ecologist. This molecular arsenal has helped to unveil the enormity of microbial diversity across the breadth of the earth's ecosystems, and has
Microbial Rechargeable Battery
Molenaar, Sam D.; Mol, Annemerel R.; Sleutels, Tom H.J.A.; Heijne, Ter Annemiek; Buisman, Cees J.N.
2016-01-01
Bioelectrochemical systems hold potential for both conversion of electricity into chemicals through microbial electrosynthesis (MES) and the provision of electrical power by oxidation of organics using microbial fuel cells (MFCs). This study provides a proof of concept for a microbial
Directory of Open Access Journals (Sweden)
Abdullah G Al Otaibi
2012-01-01
Conclusion: Children with suspected microbial keratitis require comprehensive evaluation and management. Early recognition, identifying the predisposing factors and etiological microbial organisms, and instituting appropriate treatment measures have a crucial role in outcome. Ocular trauma was the leading cause of childhood microbial keratitis in our study.
Avian metapneumovirus subtype A in China and subtypes A and B in Nigeria.
Owoade, A A; Ducatez, M F; Hübschen, J M; Sausy, A; Chen, H; Guan, Y; Muller, C P
2008-09-01
In order to detect and characterize avian metapneumovirus, organs or swabs were collected from 697 chicken and 110 turkeys from commercial farms in Southwestern Nigeria and from 107 chickens from live bird markets in Southeastern China. In Nigeria, 15% and 6% of the chicken and turkey samples, respectively, and 39% of the chicken samples from China, were positive for aMPV genome by PCR. The sequence of a 400 nt fragment of the attachment protein gene (G gene) revealed the presence of aMPV subtype A in both Nigeria and Southeastern China. Essentially identical subtype A viruses were found in both countries and were also previously reported from Brazil and the United Kingdom, suggesting a link between these countries or a common source of this subtype. In Nigeria, subtype B was also found, which may be a reflection of chicken importations from most major poultry-producing countries in Europe and Asia. In order to justify countermeasures, further studies are warranted to better understand the metapneumoviruses and their impact on poultry production.
Liposarcoma : MR findings in the histologic subtypes
International Nuclear Information System (INIS)
Lee, Jeong Hoon; Sohn, Jeong Eun; Chung, Soo Jeong; Kim, Kie Hwan; Chin, Soo Yil
1999-01-01
To evaluate the MR imaging findings of liposarcomas of different histologic subtypes. We evaluated MR images of 21 patients (5 men and 16 women, mean age, 55 years) with liposarcoma and correlated the findings with the results of histopathology. In the study group seven liposarcomas were well-differentiated, seven were myxoid, three were mixed, two were pleomorphic, and one was round cell. On T1-and T2-weighted images, six of seven well-differentiated liposarcomas showed signal intensity equal to the fat and hypointense septa, while the other showed low signal intensity on a T1-weighted image, heterogeneous high signal intensity on a T2-weighted image, heterogeneous enhancement after the administration of contrast media and was dedifferentiate. Nine masses in seven patients with myxoid liposarcoma showed low signal intensity on T1-weighted images, six of the nine showed lace-like foci of high signal intensity. On T2-weighted images, all masses showed homogeneous high signal intensity. After administration of contrast media, five of seven masses showed heterogeneous enhancement. Two of three mixed form were well-differentiated and myxoid types, and two subtypes were separable on MR. Pleomorphic, round cell, mixed type myxoid and pleomorphic and unclassified cases showed low signal intensity on T1-weighted images, heterogeneous high signal intensity on T2-weighted and heterogeneous enhancement. Using MR imaging, well-differentiated and myxoid liposcarcomas may be differentiated from other types
Bayesian analogy with relational transformations.
Lu, Hongjing; Chen, Dawn; Holyoak, Keith J
2012-07-01
How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuous-valued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought. Copyright 2012 APA, all rights reserved.
Bayesian tomographic reconstruction of microsystems
International Nuclear Information System (INIS)
Salem, Sofia Fekih; Vabre, Alexandre; Mohammad-Djafari, Ali
2007-01-01
The microtomography by X ray transmission plays an increasingly dominating role in the study and the understanding of microsystems. Within this framework, an experimental setup of high resolution X ray microtomography was developed at CEA-List to quantify the physical parameters related to the fluids flow in microsystems. Several difficulties rise from the nature of experimental data collected on this setup: enhanced error measurements due to various physical phenomena occurring during the image formation (diffusion, beam hardening), and specificities of the setup (limited angle, partial view of the object, weak contrast).To reconstruct the object we must solve an inverse problem. This inverse problem is known to be ill-posed. It therefore needs to be regularized by introducing prior information. The main prior information we account for is that the object is composed of a finite known number of different materials distributed in compact regions. This a priori information is introduced via a Gauss-Markov field for the contrast distributions with a hidden Potts-Markov field for the class materials in the Bayesian estimation framework. The computations are done by using an appropriate Markov Chain Monte Carlo (MCMC) technique.In this paper, we present first the basic steps of the proposed algorithms. Then we focus on one of the main steps in any iterative reconstruction method which is the computation of forward and adjoint operators (projection and backprojection). A fast implementation of these two operators is crucial for the real application of the method. We give some details on the fast computation of these steps and show some preliminary results of simulations
Cigarette smoking and risk of Hodgkin lymphoma and its subtypes
DEFF Research Database (Denmark)
Kamper-Jørgensen, Mads; Rostgaard, K; Glaser, S L
2013-01-01
The etiology of Hodgkin lymphoma (HL) remains incompletely characterized. Studies of the association between smoking and HL have yielded ambiguous results, possibly due to differences between HL subtypes....
Objective Bayesianism and the Maximum Entropy Principle
Directory of Open Access Journals (Sweden)
Jon Williamson
2013-09-01
Full Text Available Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities; they should be calibrated to our evidence of physical probabilities; and they should otherwise equivocate sufficiently between the basic propositions that we can express. The three norms are sometimes explicated by appealing to the maximum entropy principle, which says that a belief function should be a probability function, from all those that are calibrated to evidence, that has maximum entropy. However, the three norms of objective Bayesianism are usually justified in different ways. In this paper, we show that the three norms can all be subsumed under a single justification in terms of minimising worst-case expected loss. This, in turn, is equivalent to maximising a generalised notion of entropy. We suggest that requiring language invariance, in addition to minimising worst-case expected loss, motivates maximisation of standard entropy as opposed to maximisation of other instances of generalised entropy. Our argument also provides a qualified justification for updating degrees of belief by Bayesian conditionalisation. However, conditional probabilities play a less central part in the objective Bayesian account than they do under the subjective view of Bayesianism, leading to a reduced role for Bayes’ Theorem.
A default Bayesian hypothesis test for mediation.
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).
Classifying emotion in Twitter using Bayesian network
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%.
Empirical Bayesian inference and model uncertainty
International Nuclear Information System (INIS)
Poern, K.
1994-01-01
This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability
Bayesian Inference Methods for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand
2013-01-01
This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...
Bayesian Methods for Radiation Detection and Dosimetry
Groer, Peter G
2002-01-01
We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed comp...
Bayesian estimation of dose rate effectiveness
International Nuclear Information System (INIS)
Arnish, J.J.; Groer, P.G.
2000-01-01
A Bayesian statistical method was used to quantify the effectiveness of high dose rate 137 Cs gamma radiation at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice. The Bayesian approach considers both the temporal and dose dependence of radiation carcinogenesis and total mortality. This paper provides the first direct estimation of dose rate effectiveness using Bayesian statistics. This statistical approach provides a quantitative description of the uncertainty of the factor characterising the dose rate in terms of a probability density function. The results show that a fixed dose from 137 Cs gamma radiation delivered at a high dose rate is more effective at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice than the same dose delivered at a low dose rate. (author)
Directory of Open Access Journals (Sweden)
Juan Ángel Patiño-Galindo
Full Text Available We describe and characterize an exceptionally large HIV-1 subtype B transmission cluster occurring in the Comunidad Valenciana (CV, Spain. A total of 1806 HIV-1 protease-reverse transcriptase (PR/RT sequences from different patients were obtained in the CV between 2004 and 2014. After subtyping and generating a phylogenetic tree with additional HIV-1 subtype B sequences, a very large transmission cluster which included almost exclusively sequences from the CV was detected (n = 143 patients. This cluster was then validated and characterized with further maximum-likelihood phylogenetic analyses and Bayesian coalescent reconstructions. With these analyses, the CV cluster was delimited to 113 patients, predominately men who have sex with men (MSM. Although it was significantly located in the city of Valencia (n = 105, phylogenetic analyses suggested this cluster derives from a larger HIV lineage affecting other Spanish localities (n = 194. Coalescent analyses estimated its expansion in Valencia to have started between 1998 and 2004. From 2004 to 2009, members of this cluster represented only 1.46% of the HIV-1 subtype B samples studied in Valencia (n = 5/143, whereas from 2010 onwards its prevalence raised to 12.64% (n = 100/791. In conclusion, we have detected a very large transmission cluster in the CV where it has experienced a very fast growth in the recent years in the city of Valencia, thus contributing significantly to the HIV epidemic in this locality. Its transmission efficiency evidences shortcomings in HIV control measures in Spain and particularly in Valencia.
de Pina-Araujo, Isabel Inês M; Delatorre, Edson; Guimarães, Monick L; Morgado, Mariza G; Bello, Gonzalo
2015-01-01
The human immunodeficiency virus type 1 (HIV-1) subtype G is the most prevalent and second most prevalent HIV-1 clade in Cape Verde and Portugal, respectively; but there is no information about the origin and spatiotemporal dispersal pattern of this HIV-1 clade circulating in those countries. To this end, we used Maximum Likelihood and Bayesian coalescent-based methods to analyze a collection of 578 HIV-1 subtype G pol sequences sampled throughout Portugal, Cape Verde and 11 other countries from West and Central Africa over a period of 22 years (1992 to 2013). Our analyses indicate that most subtype G sequences from Cape Verde (80%) and Portugal (95%) branched together in a distinct monophyletic cluster (here called G(CV-PT)). The G(CV-PT) clade probably emerged after a single migration of the virus out of Central Africa into Cape Verde between the late 1970s and the middle 1980s, followed by a rapid dissemination to Portugal a couple of years later. Reconstruction of the demographic history of the G(CV-PT) clade circulating in Cape Verde and Portugal indicates that this viral clade displayed an initial phase of exponential growth during the 1980s and 1990s, followed by a decline in growth rate since the early 2000s. Our data also indicate that during the exponential growth phase the G(CV-PT) clade recombined with a preexisting subtype B viral strain circulating in Portugal, originating the CRF14_BG clade that was later disseminated to Spain and Cape Verde. Historical and recent human population movements between Angola, Cape Verde and Portugal probably played a key role in the origin and dispersal of the G(CV-PT )and CRF14_BG clades.
Molecular Subtyping of Treponema pallidum subsp. pallidum in Lisbon, Portugal▿
Castro, R.; Prieto, E.; Águas, M. J.; Manata, M. J.; Botas, J.; Martins Pereira, F.
2009-01-01
The objectives of this study were to evaluate the reproducibility of a molecular method for the subtyping of Treponema pallidum subsp. pallidum and to discriminate strains of this microorganism from strains from patients with syphilis. We studied 212 specimens from a total of 82 patients with different stages of syphilis (14 primary, 7 secondary and 61 latent syphilis). The specimens were distributed as follows: genital ulcers (n = 9), skin and mucosal lesions (n = 7), blood (n = 82), plasma (n = 82), and ear lobe scrapings (n = 32). The samples were assayed by a PCR technique to amplify a segment of the polymerase gene I (polA). Positive samples were typed on the basis of the analysis of two variable genes, tpr and arp. Sixty-two of the 90 samples positive for polA yielded typeable Treponema pallidum DNA. All skin lesions in which T. pallidum was identified (six of six [100%]) were found to contain enough DNA for typing of the organism. It was also possible to type DNA from 7/9 (77.7%) genital ulcer samples, 13/22 (59.1%) blood samples, 20/32 (62.5%) plasma samples, and 16/21 (76.2%) ear lobe scrapings. The same subtype was identified in all samples from the same patient. Five molecular subtypes (subtypes 10a, 14a, 14c, 14f, and 14g) were identified, with the most frequently found subtype being subtype 14a and the least frequently found subtype being subtype 10a. In conclusion, the subtyping technique used in this study seems to have good reproducibility. To our knowledge, subtype 10a was identified for the first time. Further studies are needed to explain the presence of this subtype in Portugal, namely, its relationship to the Treponema pallidum strains circulating in the African countries where Portuguese is spoken. PMID:19494073
A nonparametric Bayesian approach for genetic evaluation in ...
African Journals Online (AJOL)
South African Journal of Animal Science ... the Bayesian and Classical models, a Bayesian procedure is provided which allows these random ... data from the Elsenburg Dormer sheep stud and data from a simulation experiment are utilized. >
Bayesian disease mapping: hierarchical modeling in spatial epidemiology
National Research Council Canada - National Science Library
Lawson, Andrew
2013-01-01
.... Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications...
Sparse reconstruction using distribution agnostic bayesian matching pursuit
Masood, Mudassir; Al-Naffouri, Tareq Y.
2013-01-01
A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics
The Bayesian Approach to Association
Arora, N. S.
2017-12-01
The Bayesian approach to Association focuses mainly on quantifying the physics of the domain. In the case of seismic association for instance let X be the set of all significant events (above some threshold) and their attributes, such as location, time, and magnitude, Y1 be the set of detections that are caused by significant events and their attributes such as seismic phase, arrival time, amplitude etc., Y2 be the set of detections that are not caused by significant events, and finally Y be the set of observed detections We would now define the joint distribution P(X, Y1, Y2, Y) = P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2) ; where the last term simply states that Y1 and Y2 are a partitioning of Y. Given the above joint distribution the inference problem is simply to find the X, Y1, and Y2 that maximizes posterior probability P(X, Y1, Y2| Y) which reduces to maximizing P(X) P(Y1 | X) P(Y2) I(Y = Y1 + Y2). In this expression P(X) captures our prior belief about event locations. P(Y1 | X) captures notions of travel time, residual error distributions as well as detection and mis-detection probabilities. While P(Y2) captures the false detection rate of our seismic network. The elegance of this approach is that all of the assumptions are stated clearly in the model for P(X), P(Y1|X) and P(Y2). The implementation of the inference is merely a by-product of this model. In contrast some of the other methods such as GA hide a number of assumptions in the implementation details of the inference - such as the so called "driver cells." The other important aspect of this approach is that all seismic knowledge including knowledge from other domains such as infrasound and hydroacoustic can be included in the same model. So, we don't need to separately account for misdetections or merge seismic and infrasound events as a separate step. Finally, it should be noted that the objective of automatic association is to simplify the job of humans who are publishing seismic bulletins based on this
Bayesian uncertainty analyses of probabilistic risk models
International Nuclear Information System (INIS)
Pulkkinen, U.
1989-01-01
Applications of Bayesian principles to the uncertainty analyses are discussed in the paper. A short review of the most important uncertainties and their causes is provided. An application of the principle of maximum entropy to the determination of Bayesian prior distributions is described. An approach based on so called probabilistic structures is presented in order to develop a method of quantitative evaluation of modelling uncertainties. The method is applied to a small example case. Ideas for application areas for the proposed method are discussed
Justifying Objective Bayesianism on Predicate Languages
Directory of Open Access Journals (Sweden)
Jürgen Landes
2015-04-01
Full Text Available Objective Bayesianism says that the strengths of one’s beliefs ought to be probabilities, calibrated to physical probabilities insofar as one has evidence of them, and otherwise sufficiently equivocal. These norms of belief are often explicated using the maximum entropy principle. In this paper we investigate the extent to which one can provide a unified justification of the objective Bayesian norms in the case in which the background language is a first-order predicate language, with a view to applying the resulting formalism to inductive logic. We show that the maximum entropy principle can be motivated largely in terms of minimising worst-case expected loss.
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.
Nonparametric Bayesian Modeling of Complex Networks
DEFF Research Database (Denmark)
Schmidt, Mikkel Nørgaard; Mørup, Morten
2013-01-01
an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...
Length Scales in Bayesian Automatic Adaptive Quadrature
Directory of Open Access Journals (Sweden)
Adam Gh.
2016-01-01
Full Text Available Two conceptual developments in the Bayesian automatic adaptive quadrature approach to the numerical solution of one-dimensional Riemann integrals [Gh. Adam, S. Adam, Springer LNCS 7125, 1–16 (2012] are reported. First, it is shown that the numerical quadrature which avoids the overcomputing and minimizes the hidden floating point loss of precision asks for the consideration of three classes of integration domain lengths endowed with specific quadrature sums: microscopic (trapezoidal rule, mesoscopic (Simpson rule, and macroscopic (quadrature sums of high algebraic degrees of precision. Second, sensitive diagnostic tools for the Bayesian inference on macroscopic ranges, coming from the use of Clenshaw-Curtis quadrature, are derived.
Bayesian parameter estimation in probabilistic risk assessment
International Nuclear Information System (INIS)
Siu, Nathan O.; Kelly, Dana L.
1998-01-01
Bayesian statistical methods are widely used in probabilistic risk assessment (PRA) because of their ability to provide useful estimates of model parameters when data are sparse and because the subjective probability framework, from which these methods are derived, is a natural framework to address the decision problems motivating PRA. This paper presents a tutorial on Bayesian parameter estimation especially relevant to PRA. It summarizes the philosophy behind these methods, approaches for constructing likelihood functions and prior distributions, some simple but realistic examples, and a variety of cautions and lessons regarding practical applications. References are also provided for more in-depth coverage of various topics
Bayesian estimation and tracking a practical guide
Haug, Anton J
2012-01-01
A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation
Directory of Open Access Journals (Sweden)
A. Meillier
2018-01-01
Conclusions: Achalasia subtypes had similar clinical symptoms, except for increased vomiting severity in subtype i. The maximum esophageal diameter in subtype ii was significantly greater than in subtype iii. Esophageal stasis scores were similar. Thus, high-resolution esophageal manometry remains essential in assessing achalasia subtypes.
DEFF Research Database (Denmark)
Boonstra, Philip S; Mukherjee, Bhramar; Taylor, Jeremy M G
2011-01-01
Summary Genetic anticipation, described by earlier age of onset (AOO) and more aggressive symptoms in successive generations, is a phenomenon noted in certain hereditary diseases. Its extent may vary between families and/or between mutation subtypes known to be associated with the disease phenotype....... In this article, we posit a Bayesian approach to infer genetic anticipation under flexible random effects models for censored data that capture the effect of successive generations on AOO. Primary interest lies in the random effects. Misspecifying the distribution of random effects may result in incorrect...... to cause hereditary nonpolyposis colorectal cancer, also called Lynch syndrome (LS). We find evidence for a decrease in AOO between generations in this article. Our model predicts family-level anticipation effects that are potentially useful in genetic counseling clinics for high-risk families....
Subtype selective kainic acid receptor agonists
DEFF Research Database (Denmark)
Bunch, Lennart; Krogsgaard-Larsen, Povl
2009-01-01
(S)-Glutamic acid (Glu) is the major excitatory neurotransmitter in the mammalian central nervous system, activating the plethora of glutamate receptors (GluRs). In broad lines, the GluRs are divided into two major classes: the ionotropic Glu receptors (iGluRs) and the metabotropic Glu receptors (m......GluRs). Within the iGluRs, five subtypes (KA1, KA2, iGluR5-7) show high affinity and express full agonist activity upon binding of the naturally occurring amino acid kainic acid (KA). Thus these receptors have been named the KA receptors. This review describes all-to our knowledge-published KA receptor agonists...
The validity and utility of subtyping bulimia nervosa
van Hoeken, Daphne; Veling, Wim; Sinke, Sjoukje; Mitchell, James E.; Hoek, Hans W.
Objective: To review the evidence for the validity and utility of subtyping bulimia nervosa (BN) into a purging (BN-P) and a nonpurging subtype (BN-NP), and of distinguishing BN-NP from binge eating disorder (BED), by comparing course, complications, and treatment. Method: A literature search of
The validity and utility of subtyping bulimia nervosa
van Hoeken, Daphne; Veling, Wim; Sinke, Sjoukje; Mitchell, James E.; Hoek, Hans W.
2009-01-01
Objective: To review the evidence for the validity and utility of subtyping bulimia nervosa (BN) into a purging (BN-P) and a nonpurging subtype (BN-NP), and of distinguishing BN-NP from binge eating disorder (BED), by comparing course, complications, and treatment. Method: A literature search of
ADHD subtype differences in reinforcement sensitivity and visuospatial working memory
Dovis, S.; van der Oord, S.; Wiers, R.W.; Prins, P.J.M.
2015-01-01
Both cognitive and motivational deficits are thought to give rise to the problems in the combined (ADHD-C) and inattentive subtype (ADHD-I) of attention-deficit hyperactivity disorder (ADHD). In both subtypes one of the most prominent cognitive weaknesses appears to be in visuospatial working memory
The Association between Physical Morbidity and Subtypes of Severe Depression
DEFF Research Database (Denmark)
Østergaard, Søren Dinesen; Petrides, Georgio; Dinesen, Peter Thisted
2013-01-01
Physical illness and depression are related, but the association between specific physical diseases and diagnostic subtypes of depression remains poorly understood. This study aimed to clarify the relationship between a number of physical diseases and the nonpsychotic and psychotic subtype...... of severe depression....
Stroke subtypes and factors associated with ischemic stroke in ...
African Journals Online (AJOL)
Stroke subtypes assessed four OCSP (Oxfordshire Communi-. African Health Sciences Vol 15 Issue 1, March 2015. 68. 69 ty Stroke Project Classification) subtypes classification. 13 was used with lacunar circulation infarct (LACI) and total anterior (TACI), partial anterior (PACI), posterior. (POCI) circulation infarcts as non ...
Integrative Analysis of Prognosis Data on Multiple Cancer Subtypes
Liu, Jin; Huang, Jian; Zhang, Yawei; Lan, Qing; Rothman, Nathaniel; Zheng, Tongzhang; Ma, Shuangge
2014-01-01
Summary In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is diverse. Examining the similarity and difference in the genetic basis of multiple subtypes of the same cancer can lead to a better understanding of their connections and distinctions. Classic meta-analysis methods analyze each subtype separately and then compare analysis results across subtypes. Integrative analysis methods, in contrast, analyze the raw data on multiple subtypes simultaneously and can outperform meta-analysis methods. In this study, prognosis data on multiple subtypes of the same cancer are analyzed. An AFT (accelerated failure time) model is adopted to describe survival. The genetic basis of multiple subtypes is described using the heterogeneity model, which allows a gene/SNP to be associated with prognosis of some subtypes but not others. A compound penalization method is developed to identify genes that contain important SNPs associated with prognosis. The proposed method has an intuitive formulation and is realized using an iterative algorithm. Asymptotic properties are rigorously established. Simulation shows that the proposed method has satisfactory performance and outperforms a penalization-based meta-analysis method and a regularized thresholding method. An NHL (non-Hodgkin lymphoma) prognosis study with SNP measurements is analyzed. Genes associated with the three major subtypes, namely DLBCL, FL, and CLL/SLL, are identified. The proposed method identifies genes that are different from alternatives and have important implications and satisfactory prediction performance. PMID:24766212
Ethnic variation of the histological subtypes of renal cell carcinoma ...
African Journals Online (AJOL)
E.V. Ezenwa
The content of the data obtained included the ethnicity categorized as Chinese, Malays, Indians and others (Indonesians, Vietnamese and other minor groups). Other data collected included age, gender and the histological subtype categorized as clear cell, papillary, chromophobe, collecting duct and unclassified subtypes.
Tubal ligation and risk of ovarian cancer subtypes
DEFF Research Database (Denmark)
Sieh, Weiva; Salvador, Shannon; McGuire, Valerie
2013-01-01
Tubal ligation is a protective factor for ovarian cancer, but it is unknown whether this protection extends to all invasive histological subtypes or borderline tumors. We undertook an international collaborative study to examine the association between tubal ligation and ovarian cancer subtypes....
Integrative subtype discovery in glioblastoma using iCluster.
Directory of Open Access Journals (Sweden)
Ronglai Shen
Full Text Available Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.
A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation
DEFF Research Database (Denmark)
Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri
2013-01-01
representation of the Bessel K probability density function; a highly efficient, fast iterative Bayesian inference method is then applied to the proposed model. The resulting estimator outperforms other state-of-the-art Bayesian and non-Bayesian estimators, either by yielding lower mean squared estimation error...
A Gentle Introduction to Bayesian Analysis : Applications to Developmental Research
Van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A G
2014-01-01
Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,
A gentle introduction to Bayesian analysis : Applications to developmental research
van de Schoot, R.; Kaplan, D.; Denissen, J.J.A.; Asendorpf, J.B.; Neyer, F.J.; van Aken, M.A.G.
2014-01-01
Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,
A default Bayesian hypothesis test for ANOVA designs
Wetzels, R.; Grasman, R.P.P.P.; Wagenmakers, E.J.
2012-01-01
This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA
Prior approval: the growth of Bayesian methods in psychology.
Andrews, Mark; Baguley, Thom
2013-02-01
Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.
Energy Technology Data Exchange (ETDEWEB)
Dihlmann, W. [Radiologische Praxis, Hamburg-Barmbek (Germany); Dihlmann, A. [Berufsgenossenschaftliches Unfallkrankenhaus Hamburg (Germany)
1998-02-01
Aim: Description of a subtype of arthrosis deformans of the hand which is characterised as osteoclastic arthrosis. Patients and methods: Retrospective analysis of radiographs of the hands of 150 women and 100 men with radiological findings of arthrosis deformans. Results: 5% of women and 2% of men showed at least one digital joint with subchondral osteolysis of one or both articulating bones involving at least a third of the phalanx. This subchondral osteolysis far exceeds the cysts which are situated in the epiphyseal part of the articular region. It may develop within a year. Conclusion: Osteoclastic arthrosis of the finger is a subtype of polyarthrosis of the hand. Serial observations suggest that an osteoclast stimulating substance is produced by the cysts or arises directly from the synovial fluid; this enters the subchondral part of the bone through clefts which may or may not be visible radiologically and that this produces osteoclastic activity. The most important differential diagnoses are chronic tophacious gout and a benign tumor. (orig.) [Deutsch] Ziel: Beschreibung eines Subtyps der Arthrosis deformans an der Hand, der als osteoklastische Arthrose bezeichnet wird. Patienten und Methode: Retrospektive Analyse der Handroentgenaufnahmen von 150 Frauen und 100 Maennern mit Roentgenbefunden der Arthrosis deformans. Ergebnisse: 5% der Frauen und 2% der maennlichen Patienten des durchgesehenen Krankenguts zeigten an mindestens einem Fingergelenk eine Arthrose mit subchondralen Osteolysen an einem oder beiden artikulierenden Knochen, die mindestens ein Drittel der Phalanxlaenge erfasst hatten. Diese subchondralen Osteolysen gehen ueber die Groesse und Form der arthrotischen Geroellzysten, die lediglich im knoechernen (epiphysaeren) Gelenksockel sitzen, weit hinaus. Sie koennen innerhalb eines Jahres entstehen. Schlussfolgerung: Die osteoklastische Arthrose der Finger ist ein Subtyp der Handpolyarthrose. Nach Verlaufsbeobachtungen wird vermutet, dass eine
Sensory Subtypes in Preschool Aged Children with Autism Spectrum Disorder.
Tomchek, Scott D; Little, Lauren M; Myers, John; Dunn, Winnie
2018-06-01
Given the heterogeneity of autism spectrum disorder (ASD), research has investigated how sensory features elucidate subtypes that enhance our understanding of etiology and tailored treatment approaches. Previous studies, however, have not integrated core developmental behaviors with sensory features in investigations of subtypes in ASD. Therefore, we used latent profile analysis to examine subtypes in a preschool aged sample considering sensory processing patterns in combination with social-communication skill, motor performance, and adaptive behavior. Results showed four subtypes that differed by degree and quality of sensory features, age and differential presentation of developmental skills. Findings partially align with previous literature on sensory subtypes and extends our understanding of how sensory processing aligns with other developmental domains in young children with ASD.
Bayesian Source Attribution of Salmonellosis in South Australia.
Glass, K; Fearnley, E; Hocking, H; Raupach, J; Veitch, M; Ford, L; Kirk, M D
2016-03-01
Salmonellosis is a significant cause of foodborne gastroenteritis in Australia, and rates of illness have increased over recent years. We adopt a Bayesian source attribution model to estimate the contribution of different animal reservoirs to illness due to Salmonella spp. in South Australia between 2000 and 2010, together with 95% credible intervals (CrI). We excluded known travel associated cases and those of rare subtypes (fewer than 20 human cases or fewer than 10 isolates from included sources over the 11-year period), and the remaining 76% of cases were classified as sporadic or outbreak associated. Source-related parameters were included to allow for different handling and consumption practices. We attributed 35% (95% CrI: 20-49) of sporadic cases to chicken meat and 37% (95% CrI: 23-53) of sporadic cases to eggs. Of outbreak-related cases, 33% (95% CrI: 20-62) were attributed to chicken meat and 59% (95% CrI: 29-75) to eggs. A comparison of alternative model assumptions indicated that biases due to possible clustering of samples from sources had relatively minor effects on these estimates. Analysis of source-related parameters showed higher risk of illness from contaminated eggs than from contaminated chicken meat, suggesting that consumption and handling practices potentially play a bigger role in illness due to eggs, considering low Salmonella prevalence on eggs. Our results strengthen the evidence that eggs and chicken meat are important vehicles for salmonellosis in South Australia. © 2015 Society for Risk Analysis.
Molecular Subtyping of Tumors from Patients with Familial Glioma.
Ruiz, Vanessa Y; Praska, Corinne E; Armstrong, Georgina; Kollmeyer, Thomas M; Yamada, Seiji; Decker, Paul A; Kosel, Matthew L; Eckel-Passow, Jeanette E; Consortium, The Gliogene; Lachance, Daniel H; Bainbridge, Matthew N; Melin, Beatrice S; Bondy, Melissa L; Jenkins, Robert B
2017-10-10
Single-gene mutation syndromes account for some familial glioma (FG); however, they make up only a small fraction of glioma families. Gliomas can be classified into 3 major molecular subtypes based on IDH mutation and 1p/19q co-deletion. We hypothesized that the prevalence of molecular subtypes might differ in familial versus sporadic gliomas, and that tumors in the same family should have the same molecular subtype. Participants in the FG study (Gliogene) provided samples for germline DNA analysis. Formalin-fixed, paraffin-embedded (FFPE) tumor was obtained for a subset of FG cases, and DNA was extracted. We analyzed tissue from 75 families, including 10 families containing a second affected family member. Copy number variation (CNV) data was obtained using a first-generation Affymetrix molecular inversion probe (MIP) array. Samples from 62 of 75 (83%) FG cases could be classified into the 3 subtypes. The prevalence of the molecular subtypes was: 30 (48%) IDH-wild type, 21 (34%) IDH-mutant non-codeleted, and 11 (19%) IDH-mutant and 1p/19q-codeleted. This distribution of molecular subtypes was not statistically different from that of sporadic gliomas (p=0.54). Of 10 paired FG samples, molecular subtypes were concordant for 7 (κ=0.59): 3 IDH-mutant non-codeleted, 2 IDH-wild type, and 2 IDH-mutant and 1p/19q-codeleted gliomas. Our data suggest that within individual families, patients develop gliomas of the same molecular subtype. However, we did not observe differences in the prevalence of the molecular subtypes in FG compared with sporadic gliomas. These observations provide further insight about the distribution of molecular subtypes in FG. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
Microbial electrosynthetic cells
Energy Technology Data Exchange (ETDEWEB)
May, Harold D.; Marshall, Christopher W.; Labelle, Edward V.
2018-01-30
Methods are provided for microbial electrosynthesis of H.sub.2 and organic compounds such as methane and acetate. Method of producing mature electrosynthetic microbial populations by continuous culture is also provided. Microbial populations produced in accordance with the embodiments as shown to efficiently synthesize H.sub.2, methane and acetate in the presence of CO.sub.2 and a voltage potential. The production of biodegradable and renewable plastics from electricity and carbon dioxide is also disclosed.
Bayesian Meta-Analysis of Coefficient Alpha
Brannick, Michael T.; Zhang, Nanhua
2013-01-01
The current paper describes and illustrates a Bayesian approach to the meta-analysis of coefficient alpha. Alpha is the most commonly used estimate of the reliability or consistency (freedom from measurement error) for educational and psychological measures. The conventional approach to meta-analysis uses inverse variance weights to combine…
Bayesian decision theory : A simple toy problem
van Erp, H.R.N.; Linger, R.O.; van Gelder, P.H.A.J.M.
2016-01-01
We give here a comparison of the expected outcome theory, the expected utility theory, and the Bayesian decision theory, by way of a simple numerical toy problem in which we look at the investment willingness to avert a high impact low probability event. It will be found that for this toy problem
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.
Heuristics as Bayesian inference under extreme priors.
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.
A strongly quasiconvex PAC-Bayesian bound
DEFF Research Database (Denmark)
Thiemann, Niklas; Igel, Christian; Wintenberger, Olivier
2017-01-01
We propose a new PAC-Bayesian bound and a way of constructing a hypothesis space, so that the bound is convex in the posterior distribution and also convex in a trade-off parameter between empirical performance of the posterior distribution and its complexity. The complexity is measured by the Ku...
Multisnapshot Sparse Bayesian Learning for DOA
DEFF Research Database (Denmark)
Gerstoft, Peter; Mecklenbrauker, Christoph F.; Xenaki, Angeliki
2016-01-01
The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source...
Approximate Bayesian evaluations of measurement uncertainty
Possolo, Antonio; Bodnar, Olha
2018-04-01
The Guide to the Expression of Uncertainty in Measurement (GUM) includes formulas that produce an estimate of a scalar output quantity that is a function of several input quantities, and an approximate evaluation of the associated standard uncertainty. This contribution presents approximate, Bayesian counterparts of those formulas for the case where the output quantity is a parameter of the joint probability distribution of the input quantities, also taking into account any information about the value of the output quantity available prior to measurement expressed in the form of a probability distribution on the set of possible values for the measurand. The approximate Bayesian estimates and uncertainty evaluations that we present have a long history and illustrious pedigree, and provide sufficiently accurate approximations in many applications, yet are very easy to implement in practice. Differently from exact Bayesian estimates, which involve either (analytical or numerical) integrations, or Markov Chain Monte Carlo sampling, the approximations that we describe involve only numerical optimization and simple algebra. Therefore, they make Bayesian methods widely accessible to metrologists. We illustrate the application of the proposed techniques in several instances of measurement: isotopic ratio of silver in a commercial silver nitrate; odds of cryptosporidiosis in AIDS patients; height of a manometer column; mass fraction of chromium in a reference material; and potential-difference in a Zener voltage standard.
Inverse Problems in a Bayesian Setting
Matthies, Hermann G.
2016-02-13
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
Error probabilities in default Bayesian hypothesis testing
Gu, Xin; Hoijtink, Herbert; Mulder, J,
2016-01-01
This paper investigates the classical type I and type II error probabilities of default Bayes factors for a Bayesian t test. Default Bayes factors quantify the relative evidence between the null hypothesis and the unrestricted alternative hypothesis without needing to specify prior distributions for
Bayesian Averaging is Well-Temperated
DEFF Research Database (Denmark)
Hansen, Lars Kai
2000-01-01
Bayesian predictions are stochastic just like predictions of any other inference scheme that generalize from a finite sample. While a simple variational argument shows that Bayes averaging is generalization optimal given that the prior matches the teacher parameter distribution the situation is l...
Robust bayesian inference of generalized Pareto distribution ...
African Journals Online (AJOL)
En utilisant une etude exhaustive de Monte Carlo, nous prouvons que, moyennant une fonction perte generalisee adequate, on peut construire un estimateur Bayesien robuste du modele. Key words: Bayesian estimation; Extreme value; Generalized Fisher information; Gener- alized Pareto distribution; Monte Carlo; ...
Evidence Estimation for Bayesian Partially Observed MRFs
Chen, Y.; Welling, M.
2013-01-01
Bayesian estimation in Markov random fields is very hard due to the intractability of the partition function. The introduction of hidden units makes the situation even worse due to the presence of potentially very many modes in the posterior distribution. For the first time we propose a
Inverse Problems in a Bayesian Setting
Matthies, Hermann G.; Zander, Elmar; Rosić, Bojana V.; Litvinenko, Alexander; Pajonk, Oliver
2016-01-01
In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.
A Bayesian perspective on some replacement strategies
International Nuclear Information System (INIS)
Mazzuchi, Thomas A.; Soyer, Refik
1996-01-01
In this paper we present a Bayesian decision theoretic approach for determining optimal replacement strategies. This approach enables us to formally incorporate, express, and update our uncertainty when determining optimal replacement strategies. We develop relevant expressions for both the block replacement protocol with minimal repair and the age replacement protocol and illustrate the use of our approach with real data
Comparison between Fisherian and Bayesian approach to ...
African Journals Online (AJOL)
... of its simplicity and optimality properties is normally used for two group cases. However, Bayesian approach is found to be better than Fisher's approach because of its low misclassification error rate. Keywords: variance-covariance matrices, centroids, prior probability, mahalanobis distance, probability of misclassification ...
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.
Non-Linear Approximation of Bayesian Update
Litvinenko, Alexander
2016-01-01
We develop a non-linear approximation of expensive Bayesian formula. This non-linear approximation is applied directly to Polynomial Chaos Coefficients. In this way, we avoid Monte Carlo sampling and sampling error. We can show that the famous Kalman Update formula is a particular case of this update.
Comprehension and computation in Bayesian problem solving
Directory of Open Access Journals (Sweden)
Eric D. Johnson
2015-07-01
Full Text Available Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages, both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on transparent Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e. transparent problem structures at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct versus incorrect reasoners depart, and how individual difference might influence this time point.
A Bayesian Approach to Interactive Retrieval
Tague, Jean M.
1973-01-01
A probabilistic model for interactive retrieval is presented. Bayesian statistical decision theory principles are applied: use of prior and sample information about the relationship of document descriptions to query relevance; maximization of expected value of a utility function, to the problem of optimally restructuring search strategies in an…
Encoding dependence in Bayesian causal networks
Bayesian networks (BNs) represent complex, uncertain spatio-temporal dynamics by propagation of conditional probabilities between identifiable states with a testable causal interaction model. Typically, they assume random variables are discrete in time and space with a static network structure that ...
Forecasting nuclear power supply with Bayesian autoregression
International Nuclear Information System (INIS)
Beck, R.; Solow, J.L.
1994-01-01
We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)
A Bayesian Nonparametric Approach to Factor Analysis
DEFF Research Database (Denmark)
Piatek, Rémi; Papaspiliopoulos, Omiros
2018-01-01
This paper introduces a new approach for the inference of non-Gaussian factor models based on Bayesian nonparametric methods. It relaxes the usual normality assumption on the latent factors, widely used in practice, which is too restrictive in many settings. Our approach, on the contrary, does no...
Hierarchical Bayesian Models of Subtask Learning
Anglim, Jeromy; Wynton, Sarah K. A.
2015-01-01
The current study used Bayesian hierarchical methods to challenge and extend previous work on subtask learning consistency. A general model of individual-level subtask learning was proposed focusing on power and exponential functions with constraints to test for inconsistency. To study subtask learning, we developed a novel computer-based booking…
Non-Linear Approximation of Bayesian Update
Litvinenko, Alexander
2016-06-23
We develop a non-linear approximation of expensive Bayesian formula. This non-linear approximation is applied directly to Polynomial Chaos Coefficients. In this way, we avoid Monte Carlo sampling and sampling error. We can show that the famous Kalman Update formula is a particular case of this update.
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.)
Jones, Matt; Love, Bradley C
2011-08-01
The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls
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.
Reviewing the history of HIV-1: spread of subtype B in the Americas.
Directory of Open Access Journals (Sweden)
Dennis Maletich Junqueira
Full Text Available The dispersal of HIV-1 subtype B (HIV-1B is a reflection of the movement of human populations in response to social, political, and geographical issues. The initial dissemination of HIV-1B outside Africa seems to have included the passive involvement of human populations from the Caribbean in spreading the virus to the United States. However, the exact pathways taken during the establishment of the pandemic in the Americas remain unclear. Here, we propose a geographical scenario for the dissemination of HIV-1B in the Americas, based on phylogenetic and genetic statistical analyses of 313 available sequences of the pol gene from 27 countries. Maximum likelihood and bayesian inference methods were used to explore the phylogenetic relationships between HIV-1B sequences, and molecular variance estimates were analyzed to infer the genetic structure of the viral population. We found that the initial dissemination and subsequent spread of subtype B in the Americas occurred via a single introduction event in the Caribbean around 1964 (1950-1967. Phylogenetic trees present evidence of several primary outbreaks in countries in South America, directly seeded by the Caribbean epidemic. Cuba is an exception insofar as its epidemic seems to have been introduced from South America. One clade comprising isolates from different countries emerged in the most-derived branches, reflecting the intense circulation of the virus throughout the American continents. Statistical analysis supports the genetic compartmentalization of the virus among the Americas, with a close relationship between the South American and Caribbean epidemics. These findings reflect the complex establishment of the HIV-1B pandemic and contribute to our understanding between the migration process of human populations and virus diffusion.
Microbial Forensics: A Scientific Assessment
Energy Technology Data Exchange (ETDEWEB)
Keim, Paul
2003-02-17
procedures and training to meet these initial challenges so as minimize disturbance of the evidence. While epidemiology and forensics are similar sciences with similar goals when applied to biocrimes, forensics has additional and more stringent requirements. Maintaining a chain of custody on evidentiary samples is one example of an extra requirement imposed on an investigation of a biocrime. Another issue is the intent in microbial forensics to identify a bioattack organism in greatest detail. If possible, forensic investigations will strive to identify the precise strain and substrain, rather than just to the species level, which might be sufficient in an epidemiological investigation. Although multiple groups have developed lists of bioterrorism target pathogens, these lists are too narrow. An expansion of microorganisms relevant to food and water threats should be considered. Computerized networks should be established to track infectious disease outbreaks in real time. These systems could alert public health and agricultural officials to the existence of a potential bioattack earlier than simply waiting for a report of a suspicious cluster of similar patients. Once a biocrime is suspected, a wide variety of methods are available to identify the microorganism used in the bioattack and to analyze features that might lead to the source of the event. A multi-pronged approach to such an investigation may be preferable, using many available methods-ranging from genomics to sequencing to physiology to analysis of substances in the sample. Microbial forensics will be most effective if there is sufficient basic scientific information concerning microbial genetics, evolution, physiology, and ecology. Strain subtyping analysis will be difficult to interpret if we do not understand some of the basic evolutionary mechanisms and population diversity of pathogens. Phenotypic features associated with evidentiary pathogens also may provide investigative leads, but full exploitation of
Appreciating HIV-1 diversity: subtypic differences in ENV
Energy Technology Data Exchange (ETDEWEB)
Gnanakaran, S [Los Alamos National Laboratory; Shen, Tongye [Los Alamos National Laboratory; Lynch, Rebecca M [NON LANL; Derdeyn, Cynthia A [NON LANL
2008-01-01
Human immunodeficiency virus type 1 (HIV-1) group M is responsible for the current AIDS pandemic and exhibits exceedingly high levels of viral genetic diversity around the world, necessitating categorization of viruses into distinct lineages, or subtypes. These subtypes can differ by around 35% in the envelope (Env) glycoproteins of the virus, which are displayed on the surface of the virion and are targets for both neutralizing antibody and cell-mediated immune responses. This diversity reflects the remarkable ability of the virus to adapt to selective pressures, the bulk of which is applied by the host immune response, and represents a serious obstacle for developing an effective vaccine with broad coverage. Thus, it is important to understand the underlying biological consequences of inter-subtype diversity. Recent studies have revealed that the HIV-1 subtypes exhibit phenotypic differences that result from subtle differences in Env structure, particularly within the highly immunogenic V3 domain, which participates directly in viral entry. This review will therefore explore current research that describes subtypic differences in Env at the genetic and phenotypic level, focusing in particular on V3, and highlighting recent discoveries about the unique features of subtype C Env, which is the most prevalent subtype globally.
Racial difference in histologic subtype of renal cell carcinoma
International Nuclear Information System (INIS)
Olshan, Andrew F; Kuo, Tzy-Mey; Meyer, Anne-Marie; Nielsen, Matthew E; Purdue, Mark P; Rathmell, W Kimryn
2013-01-01
In the United States, renal cell carcinoma (RCC) has rapidly increased in incidence for over two decades. The most common histologic subtypes of RCC, clear cell, papillary, and chromophobe have distinct genetic and clinical characteristics; however, epidemiologic features of these subtypes have not been well characterized, particularly regarding any associations between race, disease subtypes, and recent incidence trends. Using data from the Surveillance, Epidemiology, and End Results (SEER) Program, we examined differences in the age-adjusted incidence rates and trends of RCC subtypes, including analysis focusing on racial differences. Incidence rates increased over time (2001–2009) for all three subtypes. However, the proportion of white cases with clear cell histology was higher than among blacks (50% vs. 31%, respectively), whereas black cases were more likely than white cases to have papillary RCC (23% vs. 9%, respectively). Moreover, papillary RCC incidence increased more rapidly for blacks than whites (P < 0.01) over this period. We also observed that increased incidence of papillary histology among blacks is not limited to the smallest size strata. We observed racial differences in proportionate incidence of RCC subtypes, which appear to be increasing over time; this novel finding motivates further etiologic, clinical, molecular, and genetic studies. Using national data, we observed a higher proportion of black renal cell carcinoma (RCC) cases with papillary histology compared to Caucasian cases. We also observed time trends in black-white incidence differences in histologic RCC subtypes, with rapid increases in the disproportionate share of black cases with papillary histology
Microbial accumulation of uranium
International Nuclear Information System (INIS)
Zhang Wei; Dong Faqin; Dai Qunwei
2005-01-01
The mechanism of microbial accumulation of uranium and the effects of some factors (including pH, initial uranium concentration, pretreatment of bacteria, and so on) on microbial accumulation of uranium are discussed briefly. The research direction and application prospect are presented. (authors)
DEFF Research Database (Denmark)
2008-01-01
A novel microbial fuel cell construction for the generation of electrical energy. The microbial fuel cell comprises: (i) an anode electrode, (ii) a cathode chamber, said cathode chamber comprising an in let through which an influent enters the cathode chamber, an outlet through which an effluent...
Microbial control of pollution
Energy Technology Data Exchange (ETDEWEB)
Fry, J C; Gadd, G M; Herbert, R A; Jones, C W; Watson-Craik, I A [eds.
1992-01-01
12 papers are presented on the microbial control of pollution. Topics covered include: bioremediation of oil spills; microbial control of heavy metal pollution; pollution control using microorganisms and magnetic separation; degradation of cyanide and nitriles; nitrogen removal from water and waste; and land reclamation and restoration.
An Overview of Achalasia and Its Subtypes
Patel, Dhyanesh A.; Lappas, Brian M.
2017-01-01
Achalasia is one of the most studied esophageal motility disorders. However, the pathophysiology and reasons that patients develop achalasia are still unclear. Patients often present with dysphagia to solids and liquids, regurgitation, and varying degrees of weight loss. There is significant latency prior to diagnosis, which can have nutritional implications. The diagnosis is suspected based on clinical history and confirmed by esophageal high-resolution manometry testing. Esophagogastroduodenoscopy is necessary to rule out potential malignancy that can mimic achalasia. Recent data presented in abstract form suggest that patients with type II achalasia may be most likely, and patients with type III achalasia may be least likely, to report weight loss compared to patients with type I achalasia. Although achalasia cannot be permanently cured, palliation of symptoms is possible in over 90% of patients with the treatment modalities currently available (pneumatic dilation, Heller myotomy, or peroral endoscopic myotomy). This article reviews the clinical presentation, diagnosis, and management options in patients with achalasia, as well as potential insights into histopathologic differences and nutritional implications of the subtypes of achalasia. PMID:28867969
(Re-)programming of subtype specific cardiomyocytes.
Hausburg, Frauke; Jung, Julia Jeannine; Hoch, Matti; Wolfien, Markus; Yavari, Arash; Rimmbach, Christian; David, Robert
2017-10-01
Adult cardiomyocytes (CMs) possess a highly restricted intrinsic regenerative potential - a major barrier to the effective treatment of a range of chronic degenerative cardiac disorders characterized by cellular loss and/or irreversible dysfunction and which underlies the majority of deaths in developed countries. Both stem cell programming and direct cell reprogramming hold promise as novel, potentially curative approaches to address this therapeutic challenge. The advent of induced pluripotent stem cells (iPSCs) has introduced a second pluripotent stem cell source besides embryonic stem cells (ESCs), enabling even autologous cardiomyocyte production. In addition, the recent achievement of directly reprogramming somatic cells into cardiomyocytes is likely to become of great importance. In either case, different clinical scenarios will require the generation of highly pure, specific cardiac cellular-subtypes. In this review, we discuss these themes as related to the cardiovascular stem cell and programming field, including a focus on the emergent topic of pacemaker cell generation for the development of biological pacemakers and in vitro drug testing. Copyright © 2017 Elsevier B.V. All rights reserved.
Ischemic stroke subtype is associated with outcome in thrombolyzed patients
DEFF Research Database (Denmark)
Schmitz, Marie Louise; Simonsen, Claus Ziegler; Svendsen, M L
2017-01-01
OBJECTIVES: The impact of ischemic stroke subtype on clinical outcome in patients treated with intravenous tissue-type plasminogen activator (IV-tPA) is sparsely examined. We studied the association between stroke subtype and clinical outcome in magnetic resonance imaging (MRI)-evaluated patients...... patients were more likely to achieve early neurological improvement and favorable outcome compared with LVD stroke following MRI-based IV-tPA treatment. This finding may reflect a difference in the effect of IV-tPA among stroke subtypes....
CRISPR-cas subtype I-Fb in Acinetobacter baumannii: evolution and utilization for strain subtyping.
Karah, Nabil; Samuelsen, Ørjan; Zarrilli, Raffaele; Sahl, Jason W; Wai, Sun Nyunt; Uhlin, Bernt Eric
2015-01-01
Clustered regularly interspaced short palindromic repeats (CRISPR) are polymorphic elements found in the genome of some or all strains of particular bacterial species, providing them with a system of acquired immunity against invading bacteriophages and plasmids. Two CRISPR-Cas systems have been identified in Acinetobacter baumannii, an opportunistic pathogen with a remarkable capacity for clonal dissemination. In this study, we investigated the mode of evolution and diversity of spacers of the CRISPR-cas subtype I-Fb locus in a global collection of 76 isolates of A. baumannii obtained from 14 countries and 4 continents. The locus has basically evolved from a common ancestor following two main lineages and several pathways of vertical descent. However, this vertical passage has been interrupted by occasional events of horizontal transfer of the whole locus between distinct isolates. The isolates were assigned into 40 CRISPR-based sequence types (CST). CST1 and CST23-24 comprised 18 and 9 isolates, representing two main sub-clones of international clones CC1 and CC25, respectively. Epidemiological data showed that some of the CST1 isolates were acquired or imported from Iraq, where it has probably been endemic for more than one decade and occasionally been able to spread to USA, Canada, and Europe. CST23-24 has shown a remarkable ability to cause national outbreaks of infections in Sweden, Argentina, UAE, and USA. The three isolates of CST19 were independently imported from Thailand to Sweden and Norway, raising a concern about the prevalence of CST19 in Thailand. Our study highlights the dynamic nature of the CRISPR-cas subtype I-Fb locus in A. baumannii, and demonstrates the possibility of using a CRISPR-based approach for subtyping a significant part of the global population of A. baumannii.
CRISPR-cas Subtype I-Fb in Acinetobacter baumannii: Evolution and Utilization for Strain Subtyping
Karah, Nabil; Samuelsen, Ørjan; Zarrilli, Raffaele; Sahl, Jason W.; Wai, Sun Nyunt; Uhlin, Bernt Eric
2015-01-01
Clustered regularly interspaced short palindromic repeats (CRISPR) are polymorphic elements found in the genome of some or all strains of particular bacterial species, providing them with a system of acquired immunity against invading bacteriophages and plasmids. Two CRISPR-Cas systems have been identified in Acinetobacter baumannii, an opportunistic pathogen with a remarkable capacity for clonal dissemination. In this study, we investigated the mode of evolution and diversity of spacers of the CRISPR-cas subtype I-Fb locus in a global collection of 76 isolates of A. baumannii obtained from 14 countries and 4 continents. The locus has basically evolved from a common ancestor following two main lineages and several pathways of vertical descent. However, this vertical passage has been interrupted by occasional events of horizontal transfer of the whole locus between distinct isolates. The isolates were assigned into 40 CRISPR-based sequence types (CST). CST1 and CST23-24 comprised 18 and 9 isolates, representing two main sub-clones of international clones CC1 and CC25, respectively. Epidemiological data showed that some of the CST1 isolates were acquired or imported from Iraq, where it has probably been endemic for more than one decade and occasionally been able to spread to USA, Canada, and Europe. CST23-24 has shown a remarkable ability to cause national outbreaks of infections in Sweden, Argentina, UAE, and USA. The three isolates of CST19 were independently imported from Thailand to Sweden and Norway, raising a concern about the prevalence of CST19 in Thailand. Our study highlights the dynamic nature of the CRISPR-cas subtype I-Fb locus in A. baumannii, and demonstrates the possibility of using a CRISPR-based approach for subtyping a significant part of the global population of A. baumannii. PMID:25706932
Can natural selection encode Bayesian priors?
Ramírez, Juan Camilo; Marshall, James A R
2017-08-07
The evolutionary success of many organisms depends on their ability to make decisions based on estimates of the state of their environment (e.g., predation risk) from uncertain information. These decision problems have optimal solutions and individuals in nature are expected to evolve the behavioural mechanisms to make decisions as if using the optimal solutions. Bayesian inference is the optimal method to produce estimates from uncertain data, thus natural selection is expected to favour individuals with the behavioural mechanisms to make decisions as if they were computing Bayesian estimates in typically-experienced environments, although this does not necessarily imply that favoured decision-makers do perform Bayesian computations exactly. Each individual should evolve to behave as if updating a prior estimate of the unknown environment variable to a posterior estimate as it collects evidence. The prior estimate represents the decision-maker's default belief regarding the environment variable, i.e., the individual's default 'worldview' of the environment. This default belief has been hypothesised to be shaped by natural selection and represent the environment experienced by the individual's ancestors. We present an evolutionary model to explore how accurately Bayesian prior estimates can be encoded genetically and shaped by natural selection when decision-makers learn from uncertain information. The model simulates the evolution of a population of individuals that are required to estimate the probability of an event. Every individual has a prior estimate of this probability and collects noisy cues from the environment in order to update its prior belief to a Bayesian posterior estimate with the evidence gained. The prior is inherited and passed on to offspring. Fitness increases with the accuracy of the posterior estimates produced. Simulations show that prior estimates become accurate over evolutionary time. In addition to these 'Bayesian' individuals, we also
Bayesian network learning for natural hazard assessments
Vogel, Kristin
2016-04-01
Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables
International Nuclear Information System (INIS)
Pineda, Arturo López; Ogoe, Henry Ato; Balasubramanian, Jeya Balaji; Rangel Escareño, Claudia; Visweswaran, Shyam; Herman, James Gordon; Gopalakrishnan, Vanathi
2016-01-01
Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes. Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue. Using publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis. All Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our
Interpersonal subtypes in social phobia: diagnostic and treatment implications.
Cain, Nicole M; Pincus, Aaron L; Grosse Holtforth, Martin
2010-11-01
Interpersonal assessment may provide a clinically useful way to identify subtypes of social phobia. In this study, we examined evidence for interpersonal subtypes in a sample of 77 socially phobic outpatients. A cluster analysis based on the dimensions of dominance and love on the Inventory of Interpersonal Problems-Circumplex Scales (Alden, Wiggins, & Pincus, 1990) found 2 interpersonal subtypes of socially phobic patients. These subtypes did not differ on pretreatment global symptom severity as measured by the Brief Symptom Inventory (Derogatis, 1993) or diagnostic comorbidity but did exhibit differential responses to outpatient psychotherapy. Overall, friendly-submissive social phobia patients had significantly lower scores on measures of social anxiety and significantly higher scores on measures of well-being and satisfaction at posttreatment than cold-submissive social phobia patients. We discuss the results in terms of interpersonal theory and the clinical relevance of assessment of interpersonal functioning prior to beginning psychotherapy with socially phobic patients.
Common Molecular Subtypes Among Asian Hepatocellular Carcinoma and Cholangiocarcinoma
DEFF Research Database (Denmark)
Chaisaingmongkol, Jittiporn; Budhu, Anuradha; Dang, Hien
2017-01-01
Intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are clinically disparate primary liver cancers with etiological and biological heterogeneity. We identified common molecular subtypes linked to similar prognosis among 199 Thai ICC and HCC patients through systems integratio...
Spreading of hepatitis C virus subtypes 1a and 1b through the central region of Argentina.
Culasso, Andrés Carlos Alberto; Farías, Adrián; Di Lello, Federico Alejandro; Golemba, Marcelo Darío; Ré, Viviana; Barbini, Luciana; Campos, Rodolfo
2014-08-01
The recent history of the hepatitis C virus (HCV) subtypes 1a and 1b in the central region of Argentina is hypothesized by phylogeographic reconstruction using coalescent based Bayesian analyses. Direct partial E2 sequences from HCV 1a and 1b infected patients attending different health-care centers of the country were analyzed. The inferred date of the most recent common ancestor (tMRCA) for HCV-1a was: 1962 (between 1943 and 1977) and for HCV-1b was earlier: 1929 (between 1895 and 1953). Diverse ancestral populations were inferred from both subtypes in Córdoba and in Buenos Aires cities and after that, HCV spread within and between larger cities and to other smaller cities. The analyses suggested that HCV-1b was dispersed first and it is currently in a stationary phase whereas HCV-1a was dispersed latter and it is still in a growth phase. Finally, as it was observed in the developed countries, while the transmission of HCV-1b appears to have been somehow prevented, the HCV-1a may still represent a concern in the public health. Further work should be carried out to address their current transmission rate (and its main transmission route) in the Argentinean population. Copyright © 2014 Elsevier B.V. All rights reserved.
The NIFTY way of Bayesian signal inference
International Nuclear Information System (INIS)
Selig, Marco
2014-01-01
We introduce NIFTY, 'Numerical Information Field Theory', a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTY can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTY as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D 3 PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy
The NIFTy way of Bayesian signal inference
Selig, Marco
2014-12-01
We introduce NIFTy, "Numerical Information Field Theory", a software package for the development of Bayesian signal inference algorithms that operate independently from any underlying spatial grid and its resolution. A large number of Bayesian and Maximum Entropy methods for 1D signal reconstruction, 2D imaging, as well as 3D tomography, appear formally similar, but one often finds individualized implementations that are neither flexible nor easily transferable. Signal inference in the framework of NIFTy can be done in an abstract way, such that algorithms, prototyped in 1D, can be applied to real world problems in higher-dimensional settings. NIFTy as a versatile library is applicable and already has been applied in 1D, 2D, 3D and spherical settings. A recent application is the D3PO algorithm targeting the non-trivial task of denoising, deconvolving, and decomposing photon observations in high energy astronomy.
Bayesianism and inference to the best explanation
Directory of Open Access Journals (Sweden)
Valeriano IRANZO
2008-01-01
Full Text Available Bayesianism and Inference to the best explanation (IBE are two different models of inference. Recently there has been some debate about the possibility of “bayesianizing” IBE. Firstly I explore several alternatives to include explanatory considerations in Bayes’s Theorem. Then I distinguish two different interpretations of prior probabilities: “IBE-Bayesianism” (IBE-Bay and “frequentist-Bayesianism” (Freq-Bay. After detailing the content of the latter, I propose a rule for assessing the priors. I also argue that Freq-Bay: (i endorses a role for explanatory value in the assessment of scientific hypotheses; (ii avoids a purely subjectivist reading of prior probabilities; and (iii fits better than IBE-Bayesianism with two basic facts about science, i.e., the prominent role played by empirical testing and the existence of many scientific theories in the past that failed to fulfil their promises and were subsequently abandoned.
Modelling dependable systems using hybrid Bayesian networks
International Nuclear Information System (INIS)
Neil, Martin; Tailor, Manesh; Marquez, David; Fenton, Norman; Hearty, Peter
2008-01-01
A hybrid Bayesian network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment, the models are invariably hybrid and the need for efficient and accurate computation is paramount. We apply a new iterative algorithm that efficiently combines dynamic discretisation with robust propagation algorithms on junction tree structures to perform inference in hybrid BNs. We illustrate its use in the field of dependability with two example of reliability estimation. Firstly we estimate the reliability of a simple single system and next we implement a hierarchical Bayesian model. In the hierarchical model we compute the reliability of two unknown subsystems from data collected on historically similar subsystems and then input the result into a reliability block model to compute system level reliability. We conclude that dynamic discretisation can be used as an alternative to analytical or Monte Carlo methods with high precision and can be applied to a wide range of dependability problems
Bayesian Peak Picking for NMR Spectra
Cheng, Yichen
2014-02-01
Protein structure determination is a very important topic in structural genomics, which helps people to understand varieties of biological functions such as protein-protein interactions, protein–DNA interactions and so on. Nowadays, nuclear magnetic resonance (NMR) has often been used to determine the three-dimensional structures of protein in vivo. This study aims to automate the peak picking step, the most important and tricky step in NMR structure determination. We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem. Under the Bayesian framework, the peak picking problem is casted as a variable selection problem. The proposed method can automatically distinguish true peaks from false ones without preprocessing the data. To the best of our knowledge, this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method.
Bayesian component separation: The Planck experience
Wehus, Ingunn Kathrine; Eriksen, Hans Kristian
2018-05-01
Bayesian component separation techniques have played a central role in the data reduction process of Planck. The most important strength of this approach is its global nature, in which a parametric and physical model is fitted to the data. Such physical modeling allows the user to constrain very general data models, and jointly probe cosmological, astrophysical and instrumental parameters. This approach also supports statistically robust goodness-of-fit tests in terms of data-minus-model residual maps, which are essential for identifying residual systematic effects in the data. The main challenges are high code complexity and computational cost. Whether or not these costs are justified for a given experiment depends on its final uncertainty budget. We therefore predict that the importance of Bayesian component separation techniques is likely to increase with time for intensity mapping experiments, similar to what has happened in the CMB field, as observational techniques mature, and their overall sensitivity improves.
Bayesian Modelling of Functional Whole Brain Connectivity
DEFF Research Database (Denmark)
Røge, Rasmus
the prevalent strategy of standardizing of fMRI time series and model data using directional statistics or we model the variability in the signal across the brain and across multiple subjects. In either case, we use Bayesian nonparametric modeling to automatically learn from the fMRI data the number......This thesis deals with parcellation of whole-brain functional magnetic resonance imaging (fMRI) using Bayesian inference with mixture models tailored to the fMRI data. In the three included papers and manuscripts, we analyze two different approaches to modeling fMRI signal; either we accept...... of funcional units, i.e. parcels. We benchmark the proposed mixture models against state of the art methods of brain parcellation, both probabilistic and non-probabilistic. The time series of each voxel are most often standardized using z-scoring which projects the time series data onto a hypersphere...
Software Health Management with Bayesian Networks
Mengshoel, Ole; Schumann, JOhann
2011-01-01
Most modern aircraft as well as other complex machinery is equipped with diagnostics systems for its major subsystems. During operation, sensors provide important information about the subsystem (e.g., the engine) and that information is used to detect and diagnose faults. Most of these systems focus on the monitoring of a mechanical, hydraulic, or electromechanical subsystem of the vehicle or machinery. Only recently, health management systems that monitor software have been developed. In this paper, we will discuss our approach of using Bayesian networks for Software Health Management (SWHM). We will discuss SWHM requirements, which make advanced reasoning capabilities for the detection and diagnosis important. Then we will present our approach to using Bayesian networks for the construction of health models that dynamically monitor a software system and is capable of detecting and diagnosing faults.
Narrowband interference parameterization for sparse Bayesian recovery
Ali, Anum
2015-09-11
This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.
Machine learning a Bayesian and optimization perspective
Theodoridis, Sergios
2015-01-01
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as shor...
Bayesian calibration : past achievements and future challenges
International Nuclear Information System (INIS)
Christen, J.A.
2001-01-01
Due to variations of the radiocarbon content in the biosphere over time, radiocarbon determinations need to be calibrated to obtain calendar years. Over the past decade a series of researchers have investigated the possibility of using Bayesian statistics to calibrate radiocarbon determinations, the main feature being the inclusion of contextual information into the calibration process. This allows for a coherent calibration of groups of determinations arising from related contexts (stratigraphical layers, peat cores, cultural events, ect.). Moreover, the 'related contexts' are also dated, and not only the material radiocarbon dated itself. We review Bayesian Calibration and state some of its current challenges like: software development, prior specification, robustness, etc. (author). 14 refs., 4 figs
Disentangling Complexity in Bayesian Automatic Adaptive Quadrature
Adam, Gheorghe; Adam, Sanda
2018-02-01
The paper describes a Bayesian automatic adaptive quadrature (BAAQ) solution for numerical integration which is simultaneously robust, reliable, and efficient. Detailed discussion is provided of three main factors which contribute to the enhancement of these features: (1) refinement of the m-panel automatic adaptive scheme through the use of integration-domain-length-scale-adapted quadrature sums; (2) fast early problem complexity assessment - enables the non-transitive choice among three execution paths: (i) immediate termination (exceptional cases); (ii) pessimistic - involves time and resource consuming Bayesian inference resulting in radical reformulation of the problem to be solved; (iii) optimistic - asks exclusively for subrange subdivision by bisection; (3) use of the weaker accuracy target from the two possible ones (the input accuracy specifications and the intrinsic integrand properties respectively) - results in maximum possible solution accuracy under minimum possible computing time.
Bayesian network modelling of upper gastrointestinal bleeding
Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri
2013-09-01
Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.
Bayesian Prior Probability Distributions for Internal Dosimetry
Energy Technology Data Exchange (ETDEWEB)
Miller, G.; Inkret, W.C.; Little, T.T.; Martz, H.F.; Schillaci, M.E
2001-07-01
The problem of choosing a prior distribution for the Bayesian interpretation of measurements (specifically internal dosimetry measurements) is considered using a theoretical analysis and by examining historical tritium and plutonium urine bioassay data from Los Alamos. Two models for the prior probability distribution are proposed: (1) the log-normal distribution, when there is some additional information to determine the scale of the true result, and (2) the 'alpha' distribution (a simplified variant of the gamma distribution) when there is not. These models have been incorporated into version 3 of the Bayesian internal dosimetric code in use at Los Alamos (downloadable from our web site). Plutonium internal dosimetry at Los Alamos is now being done using prior probability distribution parameters determined self-consistently from population averages of Los Alamos data. (author)
Probabilistic forecasting and Bayesian data assimilation
Reich, Sebastian
2015-01-01
In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in ap...
Anxiety After Stroke: The Importance of Subtyping.
Chun, Ho-Yan Yvonne; Whiteley, William N; Dennis, Martin S; Mead, Gillian E; Carson, Alan J
2018-03-01
Anxiety after stroke is common and disabling. Stroke trialists have treated anxiety as a homogenous condition, and intervention studies have followed suit, neglecting the different treatment approaches for phobic and generalized anxiety. Using diagnostic psychiatric interviews, we aimed to report the frequency of phobic and generalized anxiety, phobic avoidance, predictors of anxiety, and patient outcomes at 3 months poststroke/transient ischemic attack. We followed prospectively a cohort of new diagnosis of stroke/transient ischemic attack at 3 months with a telephone semistructured psychiatric interview, Fear Questionnaire, modified Rankin Scale, EuroQol-5D5L, and Work and Social Adjustment Scale. Anxiety disorder was common (any anxiety disorder, 38 of 175 [22%]). Phobic disorder was the predominant anxiety subtype: phobic disorder only, 18 of 175 (10%); phobic and generalized anxiety disorder, 13 of 175 (7%); and generalized anxiety disorder only, 7 of 175 (4%). Participants with anxiety disorder reported higher level of phobic avoidance across all situations on the Fear Questionnaire. Younger age (per decade increase in odds ratio, 0.64; 95% confidence interval, 0.45-0.91) and having previous anxiety/depression (odds ratio, 4.38; 95% confidence interval, 1.94-9.89) were predictors for anxiety poststroke/transient ischemic attack. Participants with anxiety disorder were more dependent (modified Rankin Scale score 3-5, [anxiety] 55% versus [no anxiety] 29%; P anxiety] 19.5, 10-27 versus [no anxiety] 0, 0-5; P Anxiety after stroke/transient ischemic attack is predominantly phobic and is associated with poorer patient outcomes. Trials of anxiety intervention in stroke should consider the different treatment approaches needed for phobic and generalized anxiety. © 2018 The Authors.
Bayesian networks and boundedly rational expectations
Ran Spiegler
2014-01-01
I present a framework for analyzing decision makers with an imperfect understanding of their environment's correlation structure. The framework borrows the tool of "Bayesian networks", which is ubiquitous in statistics and artificial intelligence. In the model, a decision maker faces an objective multivariate probability distribution (his own action is one of the random variables). He is characterized by a directed acyclic graph over the set of random variables. His subjective belief filters ...
Approximation of Bayesian Inverse Problems for PDEs
Cotter, S. L.; Dashti, M.; Stuart, A. M.
2010-01-01
Inverse problems are often ill posed, with solutions that depend sensitively on data.n any numerical approach to the solution of such problems, regularization of some form is needed to counteract the resulting instability. This paper is based on an approach to regularization, employing a Bayesian formulation of the problem, which leads to a notion of well posedness for inverse problems, at the level of probability measures. The stability which results from this well posedness may be used as t...
Multilevel Monte Carlo in Approximate Bayesian Computation
Jasra, Ajay
2017-02-13
In the following article we consider approximate Bayesian computation (ABC) inference. We introduce a method for numerically approximating ABC posteriors using the multilevel Monte Carlo (MLMC). A sequential Monte Carlo version of the approach is developed and it is shown under some assumptions that for a given level of mean square error, this method for ABC has a lower cost than i.i.d. sampling from the most accurate ABC approximation. Several numerical examples are given.
Centralized Bayesian reliability modelling with sensor networks
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2013-01-01
Roč. 19, č. 5 (2013), s. 471-482 ISSN 1387-3954 R&D Projects: GA MŠk 7D12004 Grant - others:GA MŠk(CZ) SVV-265315 Keywords : Bayesian modelling * Sensor network * Reliability Subject RIV: BD - Theory of Information Impact factor: 0.984, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/dedecius-0392551.pdf
Essays on portfolio choice with Bayesian methods
Kebabci, Deniz
2007-01-01
How investors should allocate assets to their portfolios in the presence of predictable components in asset returns is a question of great importance in finance. While early studies took the return generating process as given, recent studies have addressed issues such as parameter estimation and model uncertainty. My dissertation develops Bayesian methods for portfolio choice - and industry allocation in particular - under parameter and model uncertainty. The first chapter of my dissertation,...
Bayesian estimation of Weibull distribution parameters
International Nuclear Information System (INIS)
Bacha, M.; Celeux, G.; Idee, E.; Lannoy, A.; Vasseur, D.
1994-11-01
In this paper, we expose SEM (Stochastic Expectation Maximization) and WLB-SIR (Weighted Likelihood Bootstrap - Sampling Importance Re-sampling) methods which are used to estimate Weibull distribution parameters when data are very censored. The second method is based on Bayesian inference and allow to take into account available prior informations on parameters. An application of this method, with real data provided by nuclear power plants operation feedback analysis has been realized. (authors). 8 refs., 2 figs., 2 tabs
A theory of Bayesian decision making
Karni, Edi
2009-01-01
This paper presents a complete, choice-based, axiomatic Bayesian decision theory. It introduces a new choice set consisting of information-contingent plans for choosing actions and bets and subjective expected utility model with effect-dependent utility functions and action-dependent subjective probabilities which, in conjunction with the updating of the probabilities using Bayes’ rule, gives rise to a unique prior and a set of action-dependent posterior probabilities representing the decisio...
Constrained bayesian inference of project performance models
Sunmola, Funlade
2013-01-01
Project performance models play an important role in the management of project success. When used for monitoring projects, they can offer predictive ability such as indications of possible delivery problems. Approaches for monitoring project performance relies on available project information including restrictions imposed on the project, particularly the constraints of cost, quality, scope and time. We study in this paper a Bayesian inference methodology for project performance modelling in ...
Bayesian Estimation and Inference using Stochastic Hardware
Directory of Open Access Journals (Sweden)
Chetan Singh Thakur
2016-03-01
Full Text Available In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker, demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND, we show how inference can be performed in a Directed Acyclic Graph (DAG using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.
Bayesian Estimation and Inference Using Stochastic Electronics.
Thakur, Chetan Singh; Afshar, Saeed; Wang, Runchun M; Hamilton, Tara J; Tapson, Jonathan; van Schaik, André
2016-01-01
In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.
Virtual Vector Machine for Bayesian Online Classification
Minka, Thomas P.; Xiang, Rongjing; Yuan; Qi
2012-01-01
In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's primary pursuit of prediction accuracy. To address this dilemma, we introduce a novel Bayesian online classi cation algorithm, called the Virtual Vector Machine. The virtual vector machine allows you to smoothly trade-off prediction accuracy with memory size. The virtual vector machine summarizes the information con...
Characteristic imsets for learning Bayesian network structure
Czech Academy of Sciences Publication Activity Database
Hemmecke, R.; Lindner, S.; Studený, Milan
2012-01-01
Roč. 53, č. 9 (2012), s. 1336-1349 ISSN 0888-613X R&D Projects: GA MŠk(CZ) 1M0572; GA ČR GA201/08/0539 Institutional support: RVO:67985556 Keywords : learning Bayesian network structure * essential graph * standard imset * characteristic imset * LP relaxation of a polytope Subject RIV: BA - General Mathematics Impact factor: 1.729, year: 2012 http://library.utia.cas.cz/separaty/2012/MTR/studeny-0382596.pdf
Bayesian analysis of Markov point processes
DEFF Research Database (Denmark)
Berthelsen, Kasper Klitgaard; Møller, Jesper
2006-01-01
Recently Møller, Pettitt, Berthelsen and Reeves introduced a new MCMC methodology for drawing samples from a posterior distribution when the likelihood function is only specified up to a normalising constant. We illustrate the method in the setting of Bayesian inference for Markov point processes...... a partially ordered Markov point process as the auxiliary variable. As the method requires simulation from the "unknown" likelihood, perfect simulation algorithms for spatial point processes become useful....
Decisions under uncertainty using Bayesian analysis
Directory of Open Access Journals (Sweden)
Stelian STANCU
2006-01-01
Full Text Available The present paper makes a short presentation of the Bayesian decions method, where extrainformation brings a great support to decision making process, but also attract new costs. In this situation, getting new information, generally experimentaly based, contributes to diminushing the uncertainty degree that influences decision making process. As a conclusion, in a large number of decision problems, there is the possibility that the decision makers will renew some decisions already taken because of the facilities offered by obtainig extrainformation.
Molecular subtypes of glioblastoma are relevant to lower grade glioma.
Directory of Open Access Journals (Sweden)
Xiaowei Guan
Full Text Available Gliomas are the most common primary malignant brain tumors in adults with great heterogeneity in histopathology and clinical course. The intent was to evaluate the relevance of known glioblastoma (GBM expression and methylation based subtypes to grade II and III gliomas (ie. lower grade gliomas.Gene expression array, single nucleotide polymorphism (SNP array and clinical data were obtained for 228 GBMs and 176 grade II/II gliomas (GII/III from the publically available Rembrandt dataset. Two additional datasets with IDH1 mutation status were utilized as validation datasets (one publicly available dataset and one newly generated dataset from MD Anderson. Unsupervised clustering was performed and compared to gene expression subtypes assigned using the Verhaak et al 840-gene classifier. The glioma-CpG Island Methylator Phenotype (G-CIMP was assigned using prediction models by Fine et al.Unsupervised clustering by gene expression aligned with the Verhaak 840-gene subtype group assignments. GII/IIIs were preferentially assigned to the proneural subtype with IDH1 mutation and G-CIMP. GBMs were evenly distributed among the four subtypes. Proneural, IDH1 mutant, G-CIMP GII/III s had significantly better survival than other molecular subtypes. Only 6% of GBMs were proneural and had either IDH1 mutation or G-CIMP but these tumors had significantly better survival than other GBMs. Copy number changes in chromosomes 1p and 19q were associated with GII/IIIs, while these changes in CDKN2A, PTEN and EGFR were more commonly associated with GBMs.GBM gene-expression and methylation based subtypes are relevant for GII/III s and associate with overall survival differences. A better understanding of the association between these subtypes and GII/IIIs could further knowledge regarding prognosis and mechanisms of glioma progression.
Progranulin expression in breast cancer with different intrinsic subtypes.
Li, Li Qin; Min, Li Shan; Jiang, Qun; Ping, Jin Liang; Li, Jing; Dai, Li Cheng
2012-04-15
Progranulin is a newly discovered 88-kDa glycoprotein originally purified from the highly tumorigenic mouse teratoma-derived cell line PC. We found that high progranulin expression was associated with higher breast carcinoma angiogenesis, reflected by increased vascular endothelial growth factor expression and higher microvessel density. However, no immunohistochemical evidence currently exists to correlate progranulin expression with clinicopathological features in different intrinsic subtypes of breast carcinoma biopsies. The aim of this study was to investigate the progranulin expression profiles in the intrinsic subtypes of breast carcinomas and their relevance to histopathological and clinicopathological features. Tissue blocks containing 264 cases of breast carcinomas from 2006 to 2009 were classified as different intrinsic subtypes. Tissues of four intrinsic subtypes were immunostained for progranulin, vascular endothelial growth factor and CD105. Their relevance to histopathological and clinicopathological features was also analyzed. Twenty tissue samples from breast fibroadenomas were included in this study. Progranulin expression showed no significant differences in different intrinsic subtypes, although an increasing tendency could be found in the triple-negative breast cancer (TNBC) subgroup (χ(2)=5.00, df=3, p=0.17). However, differences were significant when pathologically node metastasis-positive (pN(+)) TNBC were excluded (χ(2)=17.84, df=3, pprogranulin in pathologically node metastasis-negative (pN(-)) TNBC. It was noted that the EGFR expression level of the pN(-) TNBC subtype was significantly higher in cases with strong progranulin expression than in cases with weak progranulin expression (χ(2)=11.26, df=1, pprogranulin in pN(-) TNBC suggests that progranulin is a promising new target for pN(-) TNBC treatment. Strong expression of progranulin correlates with positive EGFR expression in the pN(-) TNBC subtype. The close relationship between
Network structure exploration via Bayesian nonparametric models
International Nuclear Information System (INIS)
Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z
2015-01-01
Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)
Bayesian analyses of seasonal runoff forecasts
Krzysztofowicz, R.; Reese, S.
1991-12-01
Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.
Bayesian Recurrent Neural Network for Language Modeling.
Chien, Jen-Tzung; Ku, Yuan-Chu
2016-02-01
A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.
Bayesian Analysis of Individual Level Personality Dynamics
Directory of Open Access Journals (Sweden)
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.
Particle identification in ALICE: a Bayesian approach
Adam, Jaroslav; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahmad, Shakeel; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alexandre, Didier; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Millan Almaraz, Jesus Roberto; Alme, Johan; Alt, Torsten; Altinpinar, Sedat; Altsybeev, Igor; Alves Garcia Prado, Caio; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Balasubramanian, Supraja; Baldisseri, Alberto; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartke, Jerzy Gustaw; Bartsch, Esther; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batista Camejo, Arianna; Batyunya, Boris; Batzing, Paul Christoph; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Belmont Iii, Ronald John; Belmont Moreno, Ernesto; Belyaev, Vladimir; Benacek, Pavel; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhat, Inayat Rasool; Bhati, Ashok Kumar; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biro, Gabor; Biswas, Rathijit; Biswas, Saikat; Bjelogrlic, Sandro; Blair, Justin Thomas; Blau, Dmitry; Blume, Christoph; Bock, Friederike; Bogdanov, Alexey; Boggild, Hans; Boldizsar, Laszlo; Bombara, Marek; Book, Julian Heinz; Borel, Herve; Borissov, Alexander; Borri, Marcello; Bossu, Francesco; Botta, Elena; Bourjau, Christian; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Broker, Theo Alexander; Browning, Tyler Allen; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Cabala, Jan; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calero Diaz, Liliet; Caliva, Alberto; Calvo Villar, Ernesto; Camerini, Paolo; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Cerkala, Jakub; Chang, Beomsu; Chapeland, Sylvain; Chartier, Marielle; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chauvin, Alex; Chelnokov, Volodymyr; Cherney, Michael Gerard; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Cho, Soyeon; Chochula, Peter; Choi, Kyungeon; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Connors, Megan Elizabeth; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Crochet, Philippe; Cruz Albino, Rigoberto; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danisch, Meike Charlotte; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Conti, Camila; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Deisting, Alexander; Deloff, Andrzej; Denes, Ervin Sandor; Deplano, Caterina; Dhankher, Preeti; Di Bari, Domenico; Di Mauro, Antonio; Di Nezza, Pasquale; Diaz Corchero, Miguel Angel; Dietel, Thomas; Dillenseger, Pascal; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Drozhzhova, Tatiana; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Endress, Eric; Engel, Heiko; Epple, Eliane; Erazmus, Barbara Ewa; Erdemir, Irem; Erhardt, Filip; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Eyyubova, Gyulnara; Fabbietti, Laura; Fabris, Daniela; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Feliciello, Alessandro; Feofilov, Grigorii; Ferencei, Jozef; Fernandez Tellez, Arturo; Gonzalez Ferreiro, Elena; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Fleck, Martin Gabriel; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fronze, Gabriele Gaetano; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Gao, Chaosong; Garabatos Cuadrado, Jose; Garcia-Solis, Edmundo Javier; Gargiulo, Corrado; Gasik, Piotr Jan; Gauger, Erin Frances; Germain, Marie; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Sanchez Gonzalez, Andres; Gonzalez, Victor; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Grachov, Oleg Anatolievich; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grynyov, Borys; Grion, Nevio; Gronefeld, Julius Maximilian; Grosse-Oetringhaus, Jan Fiete; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerzoni, Barbara; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haake, Rudiger; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Hamon, Julien Charles; Harris, John William; Harton, Austin Vincent; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Hellbar, Ernst; Helstrup, Haavard; 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Keil, Markus; Khan, Mohammed Mohisin; Khan, Palash; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Do Won; Kim, Dong Jo; Kim, Daehyeok; Kim, Hyeonjoong; Kim, Jinsook; Kim, Minwoo; Kim, Se Yong; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Kiss, Gabor; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-Boesing, Christian; Klewin, Sebastian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobdaj, Chinorat; Kofarago, Monika; Kollegger, Thorsten; Kolozhvari, Anatoly; Kondratev, Valerii; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Kopcik, Michal; Kostarakis, Panagiotis; Kour, Mandeep; Kouzinopoulos, Charalampos; Kovalenko, Oleksandr; Kovalenko, Vladimir; Kowalski, Marek; Koyithatta Meethaleveedu, Greeshma; Kralik, Ivan; Kravcakova, Adela; Krivda, Marian; Krizek, Filip; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kumar, Ajay; Kumar, Jitendra; Kumar, Lokesh; Kumar, Shyam; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Ladron De Guevara, Pedro; Lagana Fernandes, Caio; Lakomov, Igor; Langoy, Rune; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Lattuca, Alessandra; Laudi, Elisa; Lea, Ramona; Leardini, Lucia; Lee, Graham Richard; Lee, Seongjoo; Lehas, Fatiha; Lemmon, Roy Crawford; Lenti, Vito; Leogrande, Emilia; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Leoncino, Marco; Levai, Peter; Li, Shuang; Li, Xiaomei; Lien, Jorgen Andre; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Lodato, Davide Francesco; Lonne, Per-Ivar; Loginov, Vitaly; Loizides, Constantinos; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Luettig, Philipp Johannes; Lunardon, Marcello; Luparello, Grazia; Lutz, Tyler Harrison; Maevskaya, Alla; Mager, Magnus; Mahajan, Sanjay; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Liudmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martin Blanco, Javier; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-Garcia, Gines; Martinez Pedreira, Miguel; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Mastroserio, Annalisa; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel Anthony; Mazzoni, Alessandra Maria; Mcdonald, Daniel; Meddi, Franco; Melikyan, Yuri; Menchaca-Rocha, Arturo Alejandro; Meninno, Elisa; Mercado-Perez, Jorge; Meres, Michal; Miake, Yasuo; Mieskolainen, Matti Mikael; Mikhaylov, Konstantin; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Montes Prado, Esther; Moreira De Godoy, Denise Aparecida; Perez Moreno, Luis Alberto; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Munzer, Robert Helmut; Murakami, Hikari; Murray, Sean; Musa, Luciano; Musinsky, Jan; Naik, Bharati; Nair, Rahul; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Naru, Muhammad Umair; Ferreira Natal Da Luz, Pedro Hugo; Nattrass, Christine; Rosado Navarro, Sebastian; Nayak, Kishora; Nayak, Ranjit; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nellen, Lukas; Ng, Fabian; Nicassio, Maria; Niculescu, Mihai; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Ohlson, Alice Elisabeth; Okatan, Ali; Okubo, Tsubasa; Olah, Laszlo; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Oravec, Matej; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozdemir, Mahmut; Pachmayer, Yvonne Chiara; Pagano, Davide; Pagano, Paola; Paic, Guy; Pal, Susanta Kumar; Pan, Jinjin; Pandey, Ashutosh Kumar; Papikyan, Vardanush; Pappalardo, Giuseppe; Pareek, Pooja; Park, Woojin; Parmar, Sonia; Passfeld, Annika; Paticchio, Vincenzo; Patra, Rajendra Nath; Paul, Biswarup; Pei, Hua; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Peresunko, Dmitry Yurevich; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrov, Viacheslav; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Ozelin De Lima Pimentel, Lais; Pinazza, Ombretta; Pinsky, Lawrence; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Porteboeuf, Sarah Julie; Porter, R Jefferson; Pospisil, Jan; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Punin, Valery; Putschke, Jorn Henning; Qvigstad, Henrik; Rachevski, Alexandre; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Reygers, Klaus Johannes; Riabov, Viktor; Ricci, Renato Angelo; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-Lucian; Rocco, Elena; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roeed, Ketil; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Ronchetti, Federico; Ronflette, Lucile; Rosnet, Philippe; Rossi, Andrea; Roukoutakis, Filimon; Roy, Ankhi; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Saarinen, Sampo; Sadhu, Samrangy; Sadovskiy, Sergey; Safarik, Karel; Sahlmuller, Baldo; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Salzwedel, Jai Samuel Nielsen; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; 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Zaporozhets, Sergey; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhang, Yonghong; Chunhui, Zhang; Zhang, Zuman; Zhao, Chengxin; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zichichi, Antonino; Zimmermann, Alice; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zyzak, Maksym
2016-05-25
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed via measurements of specific energy loss (dE/dx) and time-of-flight. PID efficiencies and misidentification probabilities are extracted and compared with Monte Carlo simulations using high purity samples of identified particles in the decay channels ${\\rm K}_{\\rm S}^{\\rm 0}\\rightarrow \\pi^+\\pi^-$, $\\phi\\rightarrow {\\rm K}^-{\\rm K}^+$ and $\\Lambda\\rightarrow{\\rm p}\\pi^-$ in p–Pb collisions at $\\sqrt{s_{\\rm NN}}= 5.02$TeV. In order to thoroughly assess the validity of the Bayesian approach, this methodology was used to obtain corrected $p_{\\rm T}$ spectra of pions, kaons, protons, and D$^0$ mesons in pp coll...
Discriminative Bayesian Dictionary Learning for Classification.
Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal
2016-12-01
We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.
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.
Bayesian posterior distributions without Markov chains.
Cole, Stephen R; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B
2012-03-01
Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976-1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984-1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.
Bayesian methodology for reliability model acceptance
International Nuclear Information System (INIS)
Zhang Ruoxue; Mahadevan, Sankaran
2003-01-01
This paper develops a methodology to assess the reliability computation model validity using the concept of Bayesian hypothesis testing, by comparing the model prediction and experimental observation, when there is only one computational model available to evaluate system behavior. Time-independent and time-dependent problems are investigated, with consideration of both cases: with and without statistical uncertainty in the model. The case of time-independent failure probability prediction with no statistical uncertainty is a straightforward application of Bayesian hypothesis testing. However, for the life prediction (time-dependent reliability) problem, a new methodology is developed in this paper to make the same Bayesian hypothesis testing concept applicable. With the existence of statistical uncertainty in the model, in addition to the application of a predictor estimator of the Bayes factor, the uncertainty in the Bayes factor is explicitly quantified through treating it as a random variable and calculating the probability that it exceeds a specified value. The developed method provides a rational criterion to decision-makers for the acceptance or rejection of the computational model
Risk-sensitivity in Bayesian sensorimotor integration.
Directory of Open Access Journals (Sweden)
Jordi Grau-Moya
Full Text Available Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.
Bayesian outcome-based strategy classification.
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.
Bayesian Methods for Radiation Detection and Dosimetry
International Nuclear Information System (INIS)
Peter G. Groer
2002-01-01
We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed compartmental activities. From the estimated probability densities of the model parameters we were able to derive the densities for compartmental activities for a two compartment catenary model at different times. We also calculated the average activities and their standard deviation for a simple two compartment model
Epidemic dynamics of two coexisting hepatitis C virus subtypes.
Jiménez-Hernández, Nuria; Torres-Puente, Manuela; Bracho, Maria Alma; García-Robles, Inmaculada; Ortega, Enrique; del Olmo, Juan; Carnicer, Fernando; González-Candelas, Fernando; Moya, Andrés
2007-01-01
Hepatitis C virus (HCV) infection affects about 3% of the human population. Phylogenetic analyses have grouped its variants into six major genotypes, which have a star-like distribution and several minor subtypes. The most abundant genotype in Europe is the so-called genotype 1, with two prevalent subtypes, 1a and 1b. In order to explain the higher prevalence of subtype 1b over 1a, a large-scale sequence analysis (100 virus clones) has been carried out over 25 patients of both subtypes in two regions of the HCV genome: one comprising hypervariable region 1 and another including the interferon sensitivity-determining region. Neither polymorphism analysis nor molecular variance analysis (attending to intra- and intersubtype differences, age, sex and previous history of antiviral treatment) was able to show any particular difference between subtypes that might account for their different prevalence. Only the demographic history of the populations carrying both subtypes and analysis of molecular variance (AMOVA) for risk practice suggested that the route of transmission may be the most important factor to explain the observed difference.
[Human immunodeficiency virus type 1 subtypes in Djibouti].
Abar, A Elmi; Jlizi, A; Darar, H Youssouf; Ben Nasr, M; Abid, S; Kacem, M Ali Ben Hadj; Slim, A
2012-01-01
The authors had for aim to study the distribution of HIV-1 subtypes in a cohort of HIV positive patients in the hospital General Peltier of Djibouti. An epidemiological study was made on 40 HIV-1 positive patients followed up in the Infectious Diseases Department over three months. All patients sample were subtyped by genotyping. Thirty-five patients (15 men and 20 women) were found infected by an HIV-1 strain belonging to the M group. Genotyping revealed that - 66% of samples were infected with subtype C, 20% with CRF02_AG, 8.5% with B, 2.9% with CRF02_AG/C and 2.9% with K/C. In fact, Subtype C prevalence has been described in the Horn of Africa and a similar prevalence was previously reported in Djibouti. However our study describes the subtype B in Djibouti for the first time. It is the predominant subtype in the Western world. The detection of CRF02_AG strains indicates that they are still circulating in Djibouti, the only country in East Africa in which this recombinant virus was found. CRF02_AG recombinant isolates were primarily described in West and Central Africa. The presence of this viral heterogeneity, probably coming from the mixing of populations in Djibouti, which is an essential economic and geographical crossroads, incites us to vigilance in the surveillance of this infection.
Subtyping pathological gamblers based on impulsivity, depression, and anxiety.
Ledgerwood, David M; Petry, Nancy M
2010-12-01
This study examined putative subtypes of pathological gamblers (PGs) based on the Pathways model, and it also evaluated whether the subtypes would benefit differentially from treatment. Treatment-seeking PGs (N = 229) were categorized into Pathways subtypes based on scores from questionnaires assessing anxiety, depression, and impulsivity. The Addiction Severity Index-Gambling assessed severity of gambling problems at baseline, posttreatment, and 12-month follow-up. Compared with behaviorally conditioned (BC) gamblers, emotionally vulnerable (EV) gamblers had higher psychiatric and gambling severity, and were more likely to have a parent with a psychiatric history. Antisocial impulsive (AI) gamblers also had elevated gambling and psychiatric severity relative to BC gamblers. They were more likely to have antisocial personality disorder and had the highest legal and family/social severity scores. They were also most likely to have a history of substance abuse treatment, history of inpatient psychiatric treatment, and a parent with a substance use or gambling problem. AI and EV gamblers experienced greater gambling severity throughout treatment than BC gamblers, but all three subtypes demonstrated similar patterns of treatment response. Thus, the three Pathways subtypes differ on some baseline characteristics, but subtyping did not predict treatment outcomes beyond a simple association with problem gambling severity. (PsycINFO Database Record (c) 2010 APA, all rights reserved).
Heterogeneity of muscarinic receptor subtypes in cerebral blood vessels
International Nuclear Information System (INIS)
Garcia-Villalon, A.L.; Krause, D.N.; Ehlert, F.J.; Duckles, S.P.
1991-01-01
The identity and distribution of muscarinic cholinergic receptor subtypes and associated signal transduction mechanisms was characterized for the cerebral circulation using correlated functional and biochemical investigations. Subtypes were distinguished by the relative affinities of a panel of muscarinic antagonists, pirenzepine, AF-DX 116 [11-2-[[2-[diethylaminomethyl]- 1-piperidinyl]acetyl]-5,11-dihydro-6H- pyrido[2,3-b][1,4]benzodiazepine-6-one], hexahydrosiladifenidol, methoctramine, 4-diphenylacetoxy-N-methylpiperidine methobromide, dicyclomine, para-fluoro-hexahydrosiladifenidol and atropine. Muscarinic receptors characterized by inhibition of [3H]quinuclidinylbenzilate binding in membranes of bovine pial arteries were of the M2 subtype. In contrast pharmacological analysis of [3H]-quinuclidinylbenzilate binding in bovine intracerebral microvessels suggests the presence of an M4 subtype. Receptors mediating endothelium-dependent vasodilation in rabbit pial arteries were of the M3 subtype, whereas muscarinic receptors stimulating endothelium-independent phosphoinositide hydrolysis in bovine pial arteries were of the M1 subtype. These findings suggest that characteristics of muscarinic receptors in cerebral blood vessels vary depending on the type of vessel, cellular location and function mediated
Being Bayesian in a quantum world
International Nuclear Information System (INIS)
Fuchs, C.
2005-01-01
Full text: To be a Bayesian about probability theory is to accept that probabilities represent subjective degrees of belief and nothing more. This is in distinction to the idea that probabilities represent long-term frequencies or objective propensities. But, how can a subjective account of probabilities coexist with the existence of quantum mechanics? To accept quantum mechanics is to accept the calculational apparatus of quantum states and the Born rule for determining probabilities in a quantum measurement. If there ever were a place for probabilities to be objective, it ought to be here. This raises the question of whether Bayesianism and quantum mechanics are compatible at all. For the Bayesian, it only suggests that we should rethink what quantum mechanics is about. Is it 'law of nature' or really more 'law of thought'? From transistors to lasers, the evidence is in that we live in a quantum world. One could infer from this that all the elements in the quantum formalism necessarily mirror nature itself: wave functions are so successful as calculational tools precisely because they represent elements of reality. A more Bayesian-like perspective is that if wave functions generate probabilities, then they too must be Bayesian degrees of belief, with all that such a radical idea entails. In particular, quantum probabilities have no firmer hold on reality than the word 'belief' in 'degrees of belief' already indicates. From this perspective, the only sense in which the quantum formalism mirrors nature is through the constraints it places on gambling agents who would like to better navigate through world. One might think that this is thin information, but it is not insubstantial. To the extent that an agent should use quantum mechanics for his uncertainty accounting rather than some other theory tells us something about the world itself - i.e., the world independent of the agent and his particular beliefs at any moment. In this talk, I will try to shore up these
Learning Local Components to Understand Large Bayesian Networks
DEFF Research Database (Denmark)
Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge
2009-01-01
(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....
An introduction to using Bayesian linear regression with clinical data.
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.
Automated Bayesian model development for frequency detection in biological time series
Directory of Open Access Journals (Sweden)
Oldroyd Giles ED
2011-06-01
Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and
Automated Bayesian model development for frequency detection in biological time series.
Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J
2011-06-24
A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time
Evolution of microbial pathogens
National Research Council Canada - National Science Library
DiRita, Victor J; Seifert, H. Steven
2006-01-01
... A. Hogan vvi ■ CONTENTS 8. Evolution of Pathogens in Soil Rachel Muir and Man-Wah Tan / 131 9. Experimental Models of Symbiotic Host-Microbial Relationships: Understanding the Underpinnings of ...
Towards Bayesian Inference of the Fast-Ion Distribution Function
DEFF Research Database (Denmark)
Stagner, L.; Heidbrink, W.W.; Salewski, Mirko
2012-01-01
sensitivity of the measurements are incorporated into Bayesian likelihood probabilities, while prior probabilities enforce physical constraints. As an initial step, this poster uses Bayesian statistics to infer the DIII-D electron density profile from multiple diagnostic measurements. Likelihood functions....... However, when theory and experiment disagree (for one or more diagnostics), it is unclear how to proceed. Bayesian statistics provides a framework to infer the DF, quantify errors, and reconcile discrepant diagnostic measurements. Diagnostic errors and ``weight functions" that describe the phase space...
Bayesian non- and semi-parametric methods and applications
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
Sparse Event Modeling with Hierarchical Bayesian Kernel Methods
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
Learning Bayesian Networks with Incomplete Data by Augmentation
Adel, Tameem; de Campos, Cassio P.
2016-01-01
We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...
Bayesian emulation for optimization in multi-step portfolio decisions
Irie, Kaoru; West, Mike
2016-01-01
We discuss the Bayesian emulation approach to computational solution of multi-step portfolio studies in financial time series. "Bayesian emulation for decisions" involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portf...
Synthetic Electric Microbial Biosensors
2017-06-10
domains and DNA-binding domains into a single protein for deregulation of down stream genes of have been favored [10]. Initially experiments with... Germany DISTRIBUTION A. Approved for public release: distribution unlimited. Talk title: “Synthetic biology based microbial biosensors for the...toolbox” in Heidelberg, Germany Poster title: “Anaerobic whole cell microbial biosensors” Link: http://phdsymposium.embl.org/#home September, 2014
Doing bayesian data analysis a tutorial with R and BUGS
Kruschke, John K
2011-01-01
There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all
A Bayesian Justification for Random Sampling in Sample Survey
Directory of Open Access Journals (Sweden)
Glen Meeden
2012-07-01
Full Text Available In the usual Bayesian approach to survey sampling the sampling design, plays a minimal role, at best. Although a close relationship between exchangeable prior distributions and simple random sampling has been noted; how to formally integrate simple random sampling into the Bayesian paradigm is not clear. Recently it has been argued that the sampling design can be thought of as part of a Bayesian's prior distribution. We will show here that under this scenario simple random sample can be given a Bayesian justification in survey sampling.
Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation
DEFF Research Database (Denmark)
Brouwer, Thomas; Frellsen, Jes; Liò, Pietro
2017-01-01
In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri......-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real...
A Bayesian Network Schema for Lessening Database Inference
National Research Council Canada - National Science Library
Chang, LiWu; Moskowitz, Ira S
2001-01-01
.... The authors introduce a formal schema for database inference analysis, based upon a Bayesian network structure, which identifies critical parameters involved in the inference problem and represents...
Ngcapu, Sinaye; Theys, Kristof; Libin, Pieter; Marconi, Vincent C; Sunpath, Henry; Ndung'u, Thumbi; Gordon, Michelle L
2017-11-08
The South African national treatment programme includes nucleoside reverse transcriptase inhibitors (NRTIs) in both first and second line highly active antiretroviral therapy regimens. Mutations in the RNase H domain have been associated with resistance to NRTIs but primarily in HIV-1 subtype B studies. Here, we investigated the prevalence and association of RNase H mutations with NRTI resistance in sequences from HIV-1 subtype C infected individuals. RNase H sequences from 112 NRTI treated but virologically failing individuals and 28 antiretroviral therapy (ART)-naive individuals were generated and analysed. In addition, sequences from 359 subtype C ART-naive sequences were downloaded from Los Alamos database to give a total of 387 sequences from ART-naive individuals for the analysis. Fisher's exact test was used to identify mutations and Bayesian network learning was applied to identify novel NRTI resistance mutation pathways in RNase H domain. The mutations A435L, S468A, T470S, L484I, A508S, Q509L, L517I, Q524E and E529D were more prevalent in sequences from treatment-experienced compared to antiretroviral treatment naive individuals, however, only the E529D mutation remained significant after correction for multiple comparison. Our findings suggest a potential interaction between E529D and NRTI-treatment; however, site-directed mutagenesis is needed to understand the impact of this RNase H mutation.
Microbial bioinformatics 2020.
Pallen, Mark J
2016-09-01
Microbial bioinformatics in 2020 will remain a vibrant, creative discipline, adding value to the ever-growing flood of new sequence data, while embracing novel technologies and fresh approaches. Databases and search strategies will struggle to cope and manual curation will not be sustainable during the scale-up to the million-microbial-genome era. Microbial taxonomy will have to adapt to a situation in which most microorganisms are discovered and characterised through the analysis of sequences. Genome sequencing will become a routine approach in clinical and research laboratories, with fresh demands for interpretable user-friendly outputs. The "internet of things" will penetrate healthcare systems, so that even a piece of hospital plumbing might have its own IP address that can be integrated with pathogen genome sequences. Microbiome mania will continue, but the tide will turn from molecular barcoding towards metagenomics. Crowd-sourced analyses will collide with cloud computing, but eternal vigilance will be the price of preventing the misinterpretation and overselling of microbial sequence data. Output from hand-held sequencers will be analysed on mobile devices. Open-source training materials will address the need for the development of a skilled labour force. As we boldly go into the third decade of the twenty-first century, microbial sequence space will remain the final frontier! © 2016 The Author. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.
Pulmonary emphysema subtypes on computed tomography: the MESA COPD study.
Smith, Benjamin M; Austin, John H M; Newell, John D; D'Souza, Belinda M; Rozenshtein, Anna; Hoffman, Eric A; Ahmed, Firas; Barr, R Graham
2014-01-01
Pulmonary emphysema is divided into 3 major subtypes at autopsy: centrilobular, paraseptal, and panlobular emphysema. These subtypes can be defined by visual assessment on computed tomography (CT); however, clinical characteristics of emphysema subtypes on CT are not well defined. We developed a reliable approach to visual assessment of emphysema subtypes on CT and examined if emphysema subtypes have distinct characteristics. The Multi-Ethnic Study of Atherosclerosis COPD Study recruited smokers with chronic obstructive pulmonary disease (COPD) and controls ages 50-79 years with ≥ 10 pack-years. Participants underwent CT following a standardized protocol. Definitions of centrilobular, paraseptal, and panlobular emphysema were obtained by literature review. Six-minute walk distance and pulmonary function were performed following guidelines. Twenty-seven percent of 318 smokers had emphysema on CT. Interrater reliability of emphysema subtype was substantial (K: 0.70). Compared with participants without emphysema, individuals with centrilobular or panlobular emphysema had greater dyspnea, reduced walk distance, greater hyperinflation, and lower diffusing capacity. In contrast, individuals with paraseptal emphysema were similar to controls, except for male predominance. Centrilobular, but not panlobular or paraseptal, emphysema was associated with greater smoking history (+21 pack-years P emphysema, was associated with reduced body mass index (-5 kg/m(2); P = .01). Other than for dyspnea, these findings were independent of the forced expiratory volume in 1 second. Seventeen percent of smokers without COPD on spirometry had emphysema, which was independently associated with reduced walk distance. Emphysema subtypes on CT are common in smokers with and without COPD. Centrilobular and panlobular emphysema, but not paraseptal emphysema, have considerable symptomatic and physiological consequences. Copyright © 2014 Elsevier Inc. All rights reserved.
Is PIGD a legitimate motor subtype in Parkinson disease?
Kotagal, Vikas
2016-06-01
Parkinson disease is a chronic progressive syndrome with a broad array of clinical features. Different investigators have suggested the heterogeneous motor manifestations of early Parkinson disease can be conceptualized through a taxonomy of clinical subtypes including tremor-predominant and postural instability and gait difficulty-predominant subtypes. Although it is theoretically valuable to distinguish subtypes of Parkinson disease, the reality is that few patients fit these discrete categories well and many transition from exhibiting elements of one subtype to elements of another. In the time since the initial description of the postural instability and gait difficulty-predominant subtype, Parkinson disease clinical research has blossomed in many ways - including an increased emphasis on the role of medical comorbidities and extranigral pathologies in Parkinson disease as markers of prognostic significance. By conceptualizing the pathogenesis of an expansive disease process in the limited terms of categorical motor subtypes, we run the risk of overlooking or misclassifying clinically significant pathogenic risk factors that lead to the development of motor milestones such as falls and related axial motor disability. Given its critical influence on quality of life and overall prognosis, we are in need of a model of postural instability and gait difficulty-predominant features in Parkinson disease that emphasizes the overlooked pathological influence of aging and medical comorbidities on the development of axial motor burden and postural instability and gait difficulty-predominant features. This Point of View proposes thinking of postural instability and gait difficulties in Parkinson disease not as a discrete subtype, but rather as multidimensional continuum influenced by several overlapping age-related pathologies.
Pulmonary Emphysema Subtypes on Computed Tomography in Smokers
Smith, Benjamin M.; Austin, John H.M.; Newell, John D.; D’Souza, Belinda M.; Rozenshtein, Anna; Hoffman, Eric A.; Ahmed, Firas; Barr, R. Graham
2013-01-01
Background Pulmonary emphysema is divided into three major subtypes at autopsy: centrilobular, paraseptal and panlobular emphysema. These subtypes can be defined by visual assessment on computed tomography (CT); however, clinical characteristics of emphysema subtypes on CT are not well-defined. We developed a reliable approach to visual assessment of emphysema subtypes on CT and examined if emphysema subtypes have distinct characteristics. Methods The Multi-Ethnic Study of Atherosclerosis COPD Study recruited smokers with COPD and controls age 50–79 years with ≥10 pack-years. Participants underwent CT following a standardized protocol. Definitions of centrilobular, paraseptal and panlobular emphysema were obtained by literature review. Six-minute walk distance and pulmonary function were performed following guidelines. Results Twenty-seven percent of 318 smokers had emphysema on CT. Inter-rater reliability of emphysema subtype was substantial (K:0.70). Compared to participants without emphysema, individuals with centrilobular or panlobular emphysema had greater dyspnea, reduced walk distance, greater hyperinflation, and lower diffusing capacity. In contrast, individuals with PSE were similar to controls, except for male predominance. Centrilobular but not panlobular or paraseptal emphysema was associated with greater smoking history (+21 pack-years Pemphysema was associated with reduced body mass index (−5 kg/m2;P=0.01). Other than for dyspnea, these findings were independent of the forced expiratory volume in one second. Seventeen percent of smokers without COPD on spirometry had emphysema, which was independently associated with reduced walk distance. Conclusions Emphysema subtypes on CT are common in smokers with and without COPD. Centrilobular and panlobular emphysema but not paraseptal emphysema have considerable symptomatic and physiological consequences. PMID:24384106
Ultrasonographic Features of Papillary Thyroid Carcinomas According to Their Subtypes
Directory of Open Access Journals (Sweden)
Hye Jin Baek
2018-05-01
Full Text Available BackgroundThe ultrasonographic characteristics and difference for various subtypes of papillary thyroid carcinoma (PTC are still unclear. The aim of this study was to compare the ultrasonographic features of PTC according to its subtype in patients undergoing thyroid surgery.MethodsIn total, 140 patients who underwent preoperative thyroid ultrasonography (US and thyroid surgery between January 2016 and December 2016 were included. The ultrasonographic features and the Korean Thyroid Imaging Reporting and Data System (K-TIRADS category of each thyroid nodule were retrospectively evaluated by a single radiologist, and differences in ultrasonographic features according to the PTC subtype were assessed.ResultsAccording to histopathological analyses, there were 97 classic PTCs (62.2%, 34 follicular variants (21.8%, 5 tall cell variants (3.2%, 2 oncocytic variants (1.3%, 1 Warthin-like variant (0.6%, and 1 diffuse sclerosing variant (0.6%. Most PTCs were classified under K-TIRADS category 5. Among the ultrasonographic features, the nodule margin and the presence of calcification were significantly different among the PTC subtypes. A spiculated/microlobulated margin was the most common type of margin, regardless of the PTC subtype. In particular, all tall cell variants exhibited a spiculated/microlobulated margin. The classic PTC group exhibited the highest prevalence of intranodular calcification, with microcalcification being the most common. The prevalence of multiplicity and nodal metastasis was high in the tall cell variant group.ConclusionThe majority of PTCs in the present study belonged to K-TIRADS category 5, regardless of the subtype. Our findings suggest that ultrasonographic features are not useful for distinguishing PTC subtypes.
Diagnosis and subtypes of adolescent antisocial personality disorder.
Jones, Meredith; Westen, Drew
2010-04-01
The present study examined the application of the Antisocial Personality Disorder (APD) diagnosis to adolescents and investigated the possibility of subtypes of APD adolescents. As part of a broader study of adolescent personality in clinically-referred patients, experienced clinicians provided personality data on a randomly selected patient in their care using the SWAP-II-A personality pathology instrument. Three hundred thirteen adolescents met adult DSM-IV diagnostic criteria for APD. To characterize adolescents with the disorder, we aggregated the data to identify the items most descriptive and distinctive of APD adolescents relative to other teenagers in the sample (N = 950). Q-factor analysis identified five personality subtypes: psychopathic-like, socially withdrawn, impulsive-histrionic, emotionally dysregulated, and attentionally dysregulated. The five subtypes differed in predictable ways on a set of external criteria related to global adaptive functioning, childhood family environment, and family history of psychiatric illness. Both the APD diagnosis and the empirically derived APD subtypes provided incremental validity over and above the DSM-IV disruptive behavior disorders in predicting global adaptive functioning, number of arrests, early-onset severe externalizing pathology, and quality of peer relationships. Although preliminary, these results provide support for the use of both APD and personality-based subtyping systems in adolescents.
Clinically-inspired automatic classification of ovarian carcinoma subtypes
Directory of Open Access Journals (Sweden)
Aicha BenTaieb
2016-01-01
Full Text Available Context: It has been shown that ovarian carcinoma subtypes are distinct pathologic entities with differing prognostic and therapeutic implications. Histotyping by pathologists has good reproducibility, but occasional cases are challenging and require immunohistochemistry and subspecialty consultation. Motivated by the need for more accurate and reproducible diagnoses and to facilitate pathologists′ workflow, we propose an automatic framework for ovarian carcinoma classification. Materials and Methods: Our method is inspired by pathologists′ workflow. We analyse imaged tissues at two magnification levels and extract clinically-inspired color, texture, and segmentation-based shape descriptors using image-processing methods. We propose a carefully designed machine learning technique composed of four modules: A dissimilarity matrix, dimensionality reduction, feature selection and a support vector machine classifier to separate the five ovarian carcinoma subtypes using the extracted features. Results: This paper presents the details of our implementation and its validation on a clinically derived dataset of eighty high-resolution histopathology images. The proposed system achieved a multiclass classification accuracy of 95.0% when classifying unseen tissues. Assessment of the classifier′s confusion (confusion matrix between the five different ovarian carcinoma subtypes agrees with clinician′s confusion and reflects the difficulty in diagnosing endometrioid and serous carcinomas. Conclusions: Our results from this first study highlight the difficulty of ovarian carcinoma diagnosis which originate from the intrinsic class-imbalance observed among subtypes and suggest that the automatic analysis of ovarian carcinoma subtypes could be valuable to clinician′s diagnostic procedure by providing a second opinion.
Deep subsurface microbial processes
Lovley, D.R.; Chapelle, F.H.
1995-01-01
Information on the microbiology of the deep subsurface is necessary in order to understand the factors controlling the rate and extent of the microbially catalyzed redox reactions that influence the geophysical properties of these environments. Furthermore, there is an increasing threat that deep aquifers, an important drinking water resource, may be contaminated by man's activities, and there is a need to predict the extent to which microbial activity may remediate such contamination. Metabolically active microorganisms can be recovered from a diversity of deep subsurface environments. The available evidence suggests that these microorganisms are responsible for catalyzing the oxidation of organic matter coupled to a variety of electron acceptors just as microorganisms do in surface sediments, but at much slower rates. The technical difficulties in aseptically sampling deep subsurface sediments and the fact that microbial processes in laboratory incubations of deep subsurface material often do not mimic in situ processes frequently necessitate that microbial activity in the deep subsurface be inferred through nonmicrobiological analyses of ground water. These approaches include measurements of dissolved H2, which can predict the predominant microbially catalyzed redox reactions in aquifers, as well as geochemical and groundwater flow modeling, which can be used to estimate the rates of microbial processes. Microorganisms recovered from the deep subsurface have the potential to affect the fate of toxic organics and inorganic contaminants in groundwater. Microbial activity also greatly influences 1 the chemistry of many pristine groundwaters and contributes to such phenomena as porosity development in carbonate aquifers, accumulation of undesirably high concentrations of dissolved iron, and production of methane and hydrogen sulfide. Although the last decade has seen a dramatic increase in interest in deep subsurface microbiology, in comparison with the study of
Internal Dosimetry Intake Estimation using Bayesian Methods
International Nuclear Information System (INIS)
Miller, G.; Inkret, W.C.; Martz, H.F.
1999-01-01
New methods for the inverse problem of internal dosimetry are proposed based on evaluating expectations of the Bayesian posterior probability distribution of intake amounts, given bioassay measurements. These expectation integrals are normally of very high dimension and hence impractical to use. However, the expectations can be algebraically transformed into a sum of terms representing different numbers of intakes, with a Poisson distribution of the number of intakes. This sum often rapidly converges, when the average number of intakes for a population is small. A simplified algorithm using data unfolding is described (UF code). (author)
Bayesian approach to inverse statistical mechanics
Habeck, Michael
2014-05-01
Inverse statistical mechanics aims to determine particle interactions from ensemble properties. This article looks at this inverse problem from a Bayesian perspective and discusses several statistical estimators to solve it. In addition, a sequential Monte Carlo algorithm is proposed that draws the interaction parameters from their posterior probability distribution. The posterior probability involves an intractable partition function that is estimated along with the interactions. The method is illustrated for inverse problems of varying complexity, including the estimation of a temperature, the inverse Ising problem, maximum entropy fitting, and the reconstruction of molecular interaction potentials.
On local optima in learning bayesian networks
DEFF Research Database (Denmark)
Dalgaard, Jens; Kocka, Tomas; Pena, Jose
2003-01-01
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that it allows a trade-off between greediness and randomness, thus exploring different good local optima. When greediness...... is set at maximum, KES corresponds to the greedy equivalence search algorithm (GES). When greediness is kept at minimum, we prove that under mild assumptions KES asymptotically returns any inclusion optimal BN with nonzero probability. Experimental results for both synthetic and real data are reported...
A Bayesian concept learning approach to crowdsourcing
DEFF Research Database (Denmark)
Viappiani, P.; Zilles, S.; Hamilton, H.J.
2011-01-01
techniques, inference methods, and query selection strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn the concept definition and the types of the experts. We evaluate our model with simulations, showing......We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation...
Default Bayesian Estimation of the Fundamental Frequency
DEFF Research Database (Denmark)
Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Jensen, Søren Holdt
2013-01-01
Joint fundamental frequency and model order esti- mation is an important problem in several applications. In this paper, a default estimation algorithm based on a minimum of prior information is presented. The algorithm is developed in a Bayesian framework, and it can be applied to both real....... Moreover, several approximations of the posterior distributions on the fundamental frequency and the model order are derived, and one of the state-of-the-art joint fundamental frequency and model order estimators is demonstrated to be a special case of one of these approximations. The performance...
Bayesian regression of piecewise homogeneous Poisson processes
Directory of Open Access Journals (Sweden)
Diego Sevilla
2015-12-01
Full Text Available In this paper, a Bayesian method for piecewise regression is adapted to handle counting processes data distributed as Poisson. A numerical code in Mathematica is developed and tested analyzing simulated data. The resulting method is valuable for detecting breaking points in the count rate of time series for Poisson processes. Received: 2 November 2015, Accepted: 27 November 2015; Edited by: R. Dickman; Reviewed by: M. Hutter, Australian National University, Canberra, Australia.; DOI: http://dx.doi.org/10.4279/PIP.070018 Cite as: D J R Sevilla, Papers in Physics 7, 070018 (2015
Structure-based bayesian sparse reconstruction
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 regularization of diffusion tensor images
DEFF Research Database (Denmark)
Frandsen, Jesper; Hobolth, Asger; Østergaard, Leif
2007-01-01
Diffusion tensor imaging (DTI) is a powerful tool in the study of the course of nerve fibre bundles in the human brain. Using DTI, the local fibre orientation in each image voxel can be described by a diffusion tensor which is constructed from local measurements of diffusion coefficients along...... several directions. The measured diffusion coefficients and thereby the diffusion tensors are subject to noise, leading to possibly flawed representations of the three dimensional fibre bundles. In this paper we develop a Bayesian procedure for regularizing the diffusion tensor field, fully utilizing...
Bayesian estimation of core-melt probability
International Nuclear Information System (INIS)
Lewis, H.W.
1984-01-01
A very simple application of the canonical Bayesian algorithm is made to the problem of estimation of the probability of core melt in a commercial power reactor. An approximation to the results of the Rasmussen study on reactor safety is used as the prior distribution, and the observation that there has been no core melt yet is used as the single experiment. The result is a substantial decrease in the mean probability of core melt--factors of 2 to 4 for reasonable choices of parameters. The purpose is to illustrate the procedure, not to argue for the decrease
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world's leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modelling by dynamic mixture models
Bayesian Predictive Models for Rayleigh Wind Speed
DEFF Research Database (Denmark)
Shahirinia, Amir; Hajizadeh, Amin; Yu, David C
2017-01-01
predictive model of the wind speed aggregates the non-homogeneous distributions into a single continuous distribution. Therefore, the result is able to capture the variation among the probability distributions of the wind speeds at the turbines’ locations in a wind farm. More specifically, instead of using...... a wind speed distribution whose parameters are known or estimated, the parameters are considered as random whose variations are according to probability distributions. The Bayesian predictive model for a Rayleigh which only has a single model scale parameter has been proposed. Also closed-form posterior...... and predictive inferences under different reasonable choices of prior distribution in sensitivity analysis have been presented....
Bayesian stratified sampling to assess corpus utility
Energy Technology Data Exchange (ETDEWEB)
Hochberg, J.; Scovel, C.; Thomas, T.; Hall, S.
1998-12-01
This paper describes a method for asking statistical questions about a large text corpus. The authors exemplify the method by addressing the question, ``What percentage of Federal Register documents are real documents, of possible interest to a text researcher or analyst?`` They estimate an answer to this question by evaluating 200 documents selected from a corpus of 45,820 Federal Register documents. Bayesian analysis and stratified sampling are used to reduce the sampling uncertainty of the estimate from over 3,100 documents to fewer than 1,000. A possible application of the method is to establish baseline statistics used to estimate recall rates for information retrieval systems.
Personalized Audio Systems - a Bayesian Approach
DEFF Research Database (Denmark)
Nielsen, Jens Brehm; Jensen, Bjørn Sand; Hansen, Toke Jansen
2013-01-01
Modern audio systems are typically equipped with several user-adjustable parameters unfamiliar to most users listening to the system. To obtain the best possible setting, the user is forced into multi-parameter optimization with respect to the users's own objective and preference. To address this......, the present paper presents a general inter-active framework for personalization of such audio systems. The framework builds on Bayesian Gaussian process regression in which a model of the users's objective function is updated sequentially. The parameter setting to be evaluated in a given trial is selected...
Microbial conversion technologies
Energy Technology Data Exchange (ETDEWEB)
Lau, P. [National Research Council of Canada, Ottawa, ON (Canada). Bioconversion and Sustainable Development
2006-07-01
Microbes are a biomass and an valuable resource. This presentation discussed microbial conversion technologies along with background information on microbial cells, their characteristics and microbial diversity. Untapped opportunities for microbial conversion were identified. Metagenomic and genome mining approaches were also discussed, as they can provide access to uncultivated or unculturable microorganisms in communal populations and are an unlimited resource for biocatalysts, novel genes and metabolites. Genome mining was seen as an economical approach. The presentation also emphasized that the development of microbial biorefineries would require significant insights into the relevant microorganisms and that biocatalysts were the ultimate in sustainability. In addition, the presentation discussed the natural fibres initiative for biochemicals and biomaterials. Anticipated outputs were identified and work in progress of a new enzyme-retting cocktail to provide diversity and/or consistency in fibre characteristics for various applications were also presented. It was concluded that it is necessary to leverage understanding of biological processes to produce bioproducts in a clean and sustainable manner. tabs., figs.
The spectrum of aphasia subtypes and etiology in subacute stroke.
Hoffmann, Michael; Chen, Ren
2013-11-01
Aphasia is one of the most common stroke syndrome presentations, yet little is known about the spectrum of different subtypes or their stroke mechanisms. Yet, subtypes and etiology are known to influence the prognosis and recovery. Our aim is to analyze aphasia subtypes and etiology in a large subacute stroke population. Consecutive patients from a dedicated cognitive stroke registry were accrued. A validated cognitive screening examination was administered during the first month of stroke presentation, which enabled a diagnosis of 14 different aphasic subtypes. The evolution from one subtype to another in the acute and subacute period, at times, resulted in more than 1 subtype being diagnosed. Etiology of stroke was determined by the modified Trial of Org 10172 in Acute Stroke Treatment criteria that included intracerebral hemorrhage. Exclusions included dementia, chronic medical illness, substance abuse, and severe depression. Of 2389 stroke patients, after exclusions (n=593), aphasias numbered 625 (625 of 1796; 34.8%), and the subtype frequencies included Broca aphasia (n=170; 27.2%), anomic aphasia (n=165; 26.4%), global aphasia (n=119; 19.04%), and subcortical aphasia (n=57; 9.12%). Less frequent subtypes (total n=40; 6.7%) included transcortical aphasia (n=11), Wernicke aphasia (n=10), conduction aphasia (n=7), aphemia (n=3), semantic aphasia (n=3), crossed aphasia (n=3), pure word deafness (n=2), and foreign accent syndrome (n=1). Aphasia subtypes and etiologies had some significant associations (chi-square: 855.8, P valueaphasia had a significant association with small-vessel disease (SVD) (odds ratio [OR]=2.0254, 95% confidence interval [CI]: 1.3820-2.9681), and global aphasia patients mostly had cardioembolic (CE) causes (OR=2.3589, 95% CI: 1.5506-3.5885) and less likely SVD (OR=.2583, 95% CI: .1444-.4654). Other notable inferences were included. Wernicke aphasia was caused by either CE (6 of 12; 50%) or hemorrhage (4 of 12; 33.3%) in a combined 83% of
Parkinson's disease severity and motor subtype influence physical capacity components
Directory of Open Access Journals (Sweden)
Marcelo Pinto Pereira
2013-09-01
Full Text Available The severity of Parkinson's disease (PD and PD's motor subtypes influence the components of physical capacity. The aim of this study was to investigate the impact of both PD severity and motor subtype in the performance of these components. Thirty-six PD patients were assigned into four groups: Tremor (TD initial and TD mild, akinetic-rigid (AR initial, and AR mild. Patients' strength, balance, coordination, mobility and aerobic capacity were evaluated and groups were compared using a two-way ANOVA (severity and subtype as factors. AR presents a poorer performance than TD in almost all tests. Also this performance was worsened with the advance of the disease in AR, contrary to TD. We conclude that AR and TD subgroups are different about their performance on physical capacity components, moreover, this performance worsens with the advance of the disease of the AR group, but not for TD.
HLA-B27 subtypes among the Chukotka native groups
International Nuclear Information System (INIS)
Krylov, M.Y.; Alexeeva, L.I.; Erdesz, S.; Benevolenskaya, L.I.; Reveille, J.D.; Arnett, F.C.
1995-01-01
The purpose of this study was to assess the relative frequency of the known HLA-B27 subtypes in HLA-B27 positive Chukotka natives, which have higher frequencies of HLA-B27 (to 40%) and spondylarthropathies (to 2%) than the Russian Caucasian population. Using oligotyping of the polymerase-chain reaction amplified second and third exons of the HLA-B27 gene in 86 DNA samples from HLA-B27 positive individuals were successfully typed. All had HLA-B*2705, including 4 patients with Reiter's syndrome and 5 with ankylosing spondyloarthritis, except one Eskimo who had HLA-B*2702. None had HLA-B*2704, a frequent subtype in Orientals. With respect to HLA-B27 subtypes the indigenous populations from the eastern part of the Chukotka Peninsula are genetically more closely related to Caucasians than to Orientals. (author). 18 refs, 1 fig., 2 tabs
Fixation Characteristics of Severe Amblyopia Subtypes: Which One is Worse?
Koylu, Mehmet Talay; Ozge, Gokhan; Kucukevcilioglu, Murat; Mutlu, Fatih Mehmet; Ceylan, Osman Melih; Akıncıoglu, Dorukcan; Ayyıldız, Onder
2017-01-01
To determine differences in macular sensitivity and fixation patterns in different subtypes of severe amblyopia. This case-control study enrolled a total of 73 male adults, including 18 with pure strabismic severe amblyopia, 19 with pure anisometropic severe amblyopia, 18 with mixed (strabismic plus anizometropic) severe amblyopia, and 18 healthy controls. MP-1 microperimetry was used to evaluate macular sensitivity, location of fixation, and stability of fixation. Mean macular sensitivity, stability of fixation, and location of fixation were significantly worse in all amblyopia subtypes when compared with healthy controls. Intergroup comparisons between amblyopia subtypes revealed that mean macular sensitivity, stability of fixation, and location of fixation were significantly worse in pure strabismic and mixed amblyopic eyes when compared with pure anisometropic amblyopic eyes. Strabismus seems to be a worse prognostic factor in severe amblyopia than anisometropia in terms of fixation characteristics and retinal sensitivity.
HLA-B27 subtypes among the Chukotka native groups
Energy Technology Data Exchange (ETDEWEB)
Krylov, M.Y.; Alexeeva, L.I.; Erdesz, S.; Benevolenskaya, L.I. [Akademiya Meditsinskikh Nauk SSSR, Moscow (Russian Federation). Inst. Revmatizma; Reveille, J.D.; Arnett, F.C. [Texas Univ., Houston, TX (United States). Health Science Center
1995-12-31
The purpose of this study was to assess the relative frequency of the known HLA-B27 subtypes in HLA-B27 positive Chukotka natives, which have higher frequencies of HLA-B27 (to 40%) and spondylarthropathies (to 2%) than the Russian Caucasian population. Using oligotyping of the polymerase-chain reaction amplified second and third exons of the HLA-B27 gene in 86 DNA samples from HLA-B27 positive individuals were successfully typed. All had HLA-B*2705, including 4 patients with Reiter`s syndrome and 5 with ankylosing spondyloarthritis, except one Eskimo who had HLA-B*2702. None had HLA-B*2704, a frequent subtype in Orientals. With respect to HLA-B27 subtypes the indigenous populations from the eastern part of the Chukotka Peninsula are genetically more closely related to Caucasians than to Orientals. (author). 18 refs, 1 fig., 2 tabs.
Mechanistic curiosity will not kill the Bayesian cat
Borsboom, D.; Wagenmakers, E.-J.; Romeijn, J.-W.
2011-01-01
Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer
Mechanistic curiosity will not kill the Bayesian cat
Borsboom, Denny; Wagenmakers, Eric-Jan; Romeijn, Jan-Willem
Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer
A default Bayesian hypothesis test for correlations and partial correlations
Wetzels, R.; Wagenmakers, E.J.
2012-01-01
We propose a default Bayesian hypothesis test for the presence of a correlation or a partial correlation. The test is a direct application of Bayesian techniques for variable selection in regression models. The test is easy to apply and yields practical advantages that the standard frequentist tests
Applications of Bayesian decision theory to intelligent tutoring systems
Vos, Hendrik J.
1994-01-01
Some applications of Bayesian decision theory to intelligent tutoring systems are considered. How the problem of adapting the appropriate amount of instruction to the changing nature of a student's capabilities during the learning process can be situated in the general framework of Bayesian decision
Systematic search of Bayesian statistics in the field of psychotraumatology
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
Power in Bayesian Mediation Analysis for Small Sample Research
Miočević, M.; MacKinnon, David; Levy, Roy
2017-01-01
Bayesian methods have the potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This article compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product,
An introduction to Bayesian statistics in health psychology
Depaoli, Sarah; Rus, Holly; Clifton, James; van de Schoot, A.G.J.; Tiemensma, Jitske
2017-01-01
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
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...
Using Alien Coins to Test Whether Simple Inference Is Bayesian
Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D.
2016-01-01
Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…
Bayesian model ensembling using meta-trained recurrent neural networks
Ambrogioni, L.; Berezutskaya, Y.; Gü ç lü , U.; Borne, E.W.P. van den; Gü ç lü tü rk, Y.; Gerven, M.A.J. van; Maris, E.G.G.
2017-01-01
In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optimal classifier. This result is obtained by relying on the framework of e-free approximate Bayesian inference, where the Bayesian
Estimating mental states of a depressed person with bayesian networks
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
Universal Darwinism As a Process of Bayesian Inference.
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.
Applying Bayesian Statistics to Educational Evaluation. Theoretical Paper No. 62.
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…
An introduction to Bayesian statistics in health psychology.
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.
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.
Non-homogeneous dynamic Bayesian networks for continuous data
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
Bayesian Approaches to Imputation, Hypothesis Testing, and Parameter Estimation
Ross, Steven J.; Mackey, Beth
2015-01-01
This chapter introduces three applications of Bayesian inference to common and novel issues in second language research. After a review of the critiques of conventional hypothesis testing, our focus centers on ways Bayesian inference can be used for dealing with missing data, for testing theory-driven substantive hypotheses without a default null…
On Bayesian Inference under Sampling from Scale Mixtures of Normals
Fernández, C.; Steel, M.F.J.
1996-01-01
This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of Normals.Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and precision
Therapeutic response to benzodiazepine in panic disorder subtypes
Directory of Open Access Journals (Sweden)
Alexandre Martins Valença
Full Text Available CONTEXT: This study makes a comparison between two subtypes of panic disorder regarding the clinical efficacy of clonazepam, a benzodiazepine. OBJECTIVES: To evaluate the clinical efficacy of clonazepam in a fixed dosage (2 mg/day, compared to placebo, in the treatment of panic disorder patients and to verify whether there are any differences in the responses to clonazepam between panic disorder patients with the respiratory and non-respiratory subtypes. TYPE OF STUDY: Randomized study with clonazepam and placebo. SETTING: Outpatient Anxiety and Depression Unit of the Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil. PARTICIPANTS: 34 patients with a diagnosis of panic disorder with agoraphobia, between 18 and 55 years old. PROCEDURES: Administration of clonazepam or placebo for 6 weeks, in panic disorder patients, after they were classified within two subtypes of panic disorder: respiratory and non-respiratory. MAIN MEASUREMENTS: Changes in the number of panic attacks in comparison with the period before the beginning of the study; Hamilton Anxiety Scale; Global Clinical Impression Scale; and Patient's Global Impression scale. RESULTS: In the group that received clonazepam, by the end of the 6th week there was a statistically significant clinical improvement, shown by the remission of panic attacks (p < 0.001 and decrease in anxiety (p = 0.024. In the group that received clonazepam there was no significant difference between the respiratory and non-respiratory subtypes of panic disorder, regarding the therapeutic response to clonazepam. CONCLUSION: Clonazepam was equally effective in the treatment of the respiratory and non-respiratory subtypes of panic disorder, suggesting there is no difference in the therapeutic response between the two subtypes.
Chen, Juan; Xu, Juan; Li, Yongsheng; Zhang, Jinwen; Chen, Hong; Lu, Jianping; Wang, Zishan; Zhao, Xueying; Xu, Kang; Li, Yixue; Li, Xia; Zhang, Yan
2017-02-07
Although competing endogenous RNAs (ceRNAs) have been implicated in many solid tumors, their roles in breast cancer subtypes are not well understood. We therefore generated a ceRNA network for each subtype based on the significance of both, positive co-expression and the shared miRNAs, in the corresponding subtype miRNA dys-regulatory network, which was constructed based on negative regulations between differentially expressed miRNAs and targets. All four subtype ceRNA networks exhibited scale-free architecture and showed that the common ceRNAs were at the core of the networks. Furthermore, the common ceRNA hubs had greater connectivity than the subtype-specific hubs. Functional analysis of the common subtype ceRNA hubs highlighted factors involved in proliferation, MAPK signaling pathways and tube morphogenesis. Subtype-specific ceRNA hubs highlighted unique subtype-specific pathways, like the estrogen response and inflammatory pathways in the luminal subtypes or the factors involved in the coagulation process that participates in the basal-like subtype. Ultimately, we identified 29 critical subtype-specific ceRNA hubs that characterized the different breast cancer subtypes. Our study thus provides new insight into the common and specific subtype ceRNA interactions that define the different categories of breast cancer and enhances our understanding of the pathology underlying the different breast cancer subtypes, which can have prognostic and therapeutic implications in the future.
Directory of Open Access Journals (Sweden)
John A. Hudson
2013-09-01
Full Text Available Most source attribution studies for Campylobacter use subtyping data based on single isolates from foods and environmental sources in an attempt to draw epidemiological inferences. It has been suggested that subtyping only one Campylobacter isolate per chicken carcass incurs a risk of failing to recognise the presence of clinically relevant, but numerically infrequent, subtypes. To investigate this, between 21 and 25 Campylobacter jejuni isolates from each of ten retail chicken carcasses were subtyped by pulsed-field gel electrophoresis (PFGE using the two restriction enzymes SmaI and KpnI. Among the 227 isolates, thirteen subtypes were identified, the most frequently occurring subtype being isolated from three carcasses. Six carcasses carried a single subtype, three carcasses carried two subtypes each and one carcass carried three subtypes. Some subtypes carried by an individual carcass were shown to be potentially clonally related. Comparison of C. jejuni subtypes from chickens with isolate subtypes from human clinical cases (n = 1248 revealed seven of the thirteen chicken subtypes were indistinguishable from human cases. None of the numerically minor chicken subtypes were identified in the human data. Therefore, typing only one Campylobacter isolate from individual chicken carcasses may be adequate to inform Campylobacter source attribution.
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
Adaptive Naive Bayesian Anti-Spam Engine
Gajewski, W P
2006-01-01
The problem of spam has been seriously troubling the Internet community during the last few years and currently reached an alarming scale. Observations made at CERN (European Organization for Nuclear Research located in Geneva, Switzerland) show that spam mails can constitute up to 75% of daily SMTP traffic. A naïve Bayesian classifier based on a Bag of Words representation of an email is widely used to stop this unwanted flood as it combines good performance with simplicity of the training and classification processes. However, facing the constantly changing patterns of spam, it is necessary to assure online adaptability of the classifier. This work proposes combining such a classifier with another NBC (naïve Bayesian classifier) based on pairs of adjacent words. Only the latter will be retrained with examples of spam reported by users. Tests are performed on considerable sets of mails both from public spam archives and CERN mailboxes. They suggest that this architecture can increase spam recall without af...
A Bayesian approach to person perception.
Clifford, C W G; Mareschal, I; Otsuka, Y; Watson, T L
2015-11-01
Here we propose a Bayesian approach to person perception, outlining the theoretical position and a methodological framework for testing the predictions experimentally. We use the term person perception to refer not only to the perception of others' personal attributes such as age and sex but also to the perception of social signals such as direction of gaze and emotional expression. The Bayesian approach provides a formal description of the way in which our perception combines current sensory evidence with prior expectations about the structure of the environment. Such expectations can lead to unconscious biases in our perception that are particularly evident when sensory evidence is uncertain. We illustrate the ideas with reference to our recent studies on gaze perception which show that people have a bias to perceive the gaze of others as directed towards themselves. We also describe a potential application to the study of the perception of a person's sex, in which a bias towards perceiving males is typically observed. Copyright © 2015 Elsevier Inc. All rights reserved.
Bayesian structural inference for hidden processes
Strelioff, Christopher C.; Crutchfield, James P.
2014-04-01
We introduce a Bayesian approach to discovering patterns in structurally complex processes. The proposed method of Bayesian structural inference (BSI) relies on a set of candidate unifilar hidden Markov model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological ɛ-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be ɛ-machines, irrespective of estimated transition probabilities. Properties of ɛ-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of candidate model topologies, despite process internal structure being only indirectly present in data. We demonstrate BSI's effectiveness in estimating a process's randomness, as reflected by the Shannon entropy rate, and its structure, as quantified by the statistical complexity. We also compare using the posterior distribution over candidate models and the single, maximum a posteriori model for point estimation and show that the former more accurately reflects uncertainty in estimated values. We apply BSI to in-class examples of finite- and infinite-order Markov processes, as well to an out-of-class, infinite-state hidden process.
Adversarial life testing: A Bayesian negotiation model
International Nuclear Information System (INIS)
Rufo, M.J.; Martín, J.; Pérez, C.J.
2014-01-01
Life testing is a procedure intended for facilitating the process of making decisions in the context of industrial reliability. On the other hand, negotiation is a process of making joint decisions that has one of its main foundations in decision theory. A Bayesian sequential model of negotiation in the context of adversarial life testing is proposed. This model considers a general setting for which a manufacturer offers a product batch to a consumer. It is assumed that the reliability of the product is measured in terms of its lifetime. Furthermore, both the manufacturer and the consumer have to use their own information with respect to the quality of the product. Under these assumptions, two situations can be analyzed. For both of them, the main aim is to accept or reject the product batch based on the product reliability. This topic is related to a reliability demonstration problem. The procedure is applied to a class of distributions that belong to the exponential family. Thus, a unified framework addressing the main topics in the considered Bayesian model is presented. An illustrative example shows that the proposed technique can be easily applied in practice
Bayesian data analysis tools for atomic physics
Trassinelli, Martino
2017-10-01
We present an introduction to some concepts of Bayesian data analysis in the context of atomic physics. Starting from basic rules of probability, we present the Bayes' theorem and its applications. In particular we discuss about how to calculate simple and joint probability distributions and the Bayesian evidence, a model dependent quantity that allows to assign probabilities to different hypotheses from the analysis of a same data set. To give some practical examples, these methods are applied to two concrete cases. In the first example, the presence or not of a satellite line in an atomic spectrum is investigated. In the second example, we determine the most probable model among a set of possible profiles from the analysis of a statistically poor spectrum. We show also how to calculate the probability distribution of the main spectral component without having to determine uniquely the spectrum modeling. For these two studies, we implement the program Nested_fit to calculate the different probability distributions and other related quantities. Nested_fit is a Fortran90/Python code developed during the last years for analysis of atomic spectra. As indicated by the name, it is based on the nested algorithm, which is presented in details together with the program itself.
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang
2006-01-01
The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used by such ada......The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... by such adaptive applications are often partial fragments of an overall user model. The fragments have then to be collected and merged into a global user profile. In this paper we investigate and present algorithms able to cope with distributed, fragmented user models – based on Bayesian Networks – in the context...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...
Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks
Directory of Open Access Journals (Sweden)
Fayroz F. Sherif
2015-01-01
Full Text Available Single nucleotide polymorphisms (SNPs contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer’s disease (AD. Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA studies. New SNP biomarkers were observed to be significantly associated with Alzheimer’s disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively.
Probabilistic Space Weather Forecasting: a Bayesian Perspective
Camporeale, E.; Chandorkar, M.; Borovsky, J.; Care', A.
2017-12-01
Most of the Space Weather forecasts, both at operational and research level, are not probabilistic in nature. Unfortunately, a prediction that does not provide a confidence level is not very useful in a decision-making scenario. Nowadays, forecast models range from purely data-driven, machine learning algorithms, to physics-based approximation of first-principle equations (and everything that sits in between). Uncertainties pervade all such models, at every level: from the raw data to finite-precision implementation of numerical methods. The most rigorous way of quantifying the propagation of uncertainties is by embracing a Bayesian probabilistic approach. One of the simplest and most robust machine learning technique in the Bayesian framework is Gaussian Process regression and classification. Here, we present the application of Gaussian Processes to the problems of the DST geomagnetic index forecast, the solar wind type classification, and the estimation of diffusion parameters in radiation belt modeling. In each of these very diverse problems, the GP approach rigorously provide forecasts in the form of predictive distributions. In turn, these distributions can be used as input for ensemble simulations in order to quantify the amplification of uncertainties. We show that we have achieved excellent results in all of the standard metrics to evaluate our models, with very modest computational cost.
Bayesian nonparametric adaptive control using Gaussian processes.
Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A
2015-03-01
Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.
Robustifying Bayesian nonparametric mixtures for count data.
Canale, Antonio; Prünster, Igor
2017-03-01
Our motivating application stems from surveys of natural populations and is characterized by large spatial heterogeneity in the counts, which makes parametric approaches to modeling local animal abundance too restrictive. We adopt a Bayesian nonparametric approach based on mixture models and innovate with respect to popular Dirichlet process mixture of Poisson kernels by increasing the model flexibility at the level both of the kernel and the nonparametric mixing measure. This allows to derive accurate and robust estimates of the distribution of local animal abundance and of the corresponding clusters. The application and a simulation study for different scenarios yield also some general methodological implications. Adding flexibility solely at the level of the mixing measure does not improve inferences, since its impact is severely limited by the rigidity of the Poisson kernel with considerable consequences in terms of bias. However, once a kernel more flexible than the Poisson is chosen, inferences can be robustified by choosing a prior more general than the Dirichlet process. Therefore, to improve the performance of Bayesian nonparametric mixtures for count data one has to enrich the model simultaneously at both levels, the kernel and the mixing measure. © 2016, The International Biometric Society.
Bayesian automated cortical segmentation for neonatal MRI
Chou, Zane; Paquette, Natacha; Ganesh, Bhavana; Wang, Yalin; Ceschin, Rafael; Nelson, Marvin D.; Macyszyn, Luke; Gaonkar, Bilwaj; Panigrahy, Ashok; Lepore, Natasha
2017-11-01
Several attempts have been made in the past few years to develop and implement an automated segmentation of neonatal brain structural MRI. However, accurate automated MRI segmentation remains challenging in this population because of the low signal-to-noise ratio, large partial volume effects and inter-individual anatomical variability of the neonatal brain. In this paper, we propose a learning method for segmenting the whole brain cortical grey matter on neonatal T2-weighted images. We trained our algorithm using a neonatal dataset composed of 3 fullterm and 4 preterm infants scanned at term equivalent age. Our segmentation pipeline combines the FAST algorithm from the FSL library software and a Bayesian segmentation approach to create a threshold matrix that minimizes the error of mislabeling brain tissue types. Our method shows promising results with our pilot training set. In both preterm and full-term neonates, automated Bayesian segmentation generates a smoother and more consistent parcellation compared to FAST, while successfully removing the subcortical structure and cleaning the edges of the cortical grey matter. This method show promising refinement of the FAST segmentation by considerably reducing manual input and editing required from the user, and further improving reliability and processing time of neonatal MR images. Further improvement will include a larger dataset of training images acquired from different manufacturers.
Polynomial Chaos Surrogates for Bayesian Inference
Le Maitre, Olivier
2016-01-06
The Bayesian inference is a popular probabilistic method to solve inverse problems, such as the identification of field parameter in a PDE model. The inference rely on the Bayes rule to update the prior density of the sought field, from observations, and derive its posterior distribution. In most cases the posterior distribution has no explicit form and has to be sampled, for instance using a Markov-Chain Monte Carlo method. In practice the prior field parameter is decomposed and truncated (e.g. by means of Karhunen- Lo´eve decomposition) to recast the inference problem into the inference of a finite number of coordinates. Although proved effective in many situations, the Bayesian inference as sketched above faces several difficulties requiring improvements. First, sampling the posterior can be a extremely costly task as it requires multiple resolutions of the PDE model for different values of the field parameter. Second, when the observations are not very much informative, the inferred parameter field can highly depends on its prior which can be somehow arbitrary. These issues have motivated the introduction of reduced modeling or surrogates for the (approximate) determination of the parametrized PDE solution and hyperparameters in the description of the prior field. Our contribution focuses on recent developments in these two directions: the acceleration of the posterior sampling by means of Polynomial Chaos expansions and the efficient treatment of parametrized covariance functions for the prior field. We also discuss the possibility of making such approach adaptive to further improve its efficiency.
Bayesian estimation of isotopic age differences
International Nuclear Information System (INIS)
Curl, R.L.
1988-01-01
Isotopic dating is subject to uncertainties arising from counting statistics and experimental errors. These uncertainties are additive when an isotopic age difference is calculated. If large, they can lead to no significant age difference by classical statistics. In many cases, relative ages are known because of stratigraphic order or other clues. Such information can be used to establish a Bayes estimate of age difference which will include prior knowledge of age order. Age measurement errors are assumed to be log-normal and a noninformative but constrained bivariate prior for two true ages in known order is adopted. True-age ratio is distributed as a truncated log-normal variate. Its expected value gives an age-ratio estimate, and its variance provides credible intervals. Bayesian estimates of ages are different and in correct order even if measured ages are identical or reversed in order. For example, age measurements on two samples might both yield 100 ka with coefficients of variation of 0.2. Bayesian estimates are 22.7 ka for age difference with a 75% credible interval of [4.4, 43.7] ka
Bayesian hypothesis testing and the maintenance rule
International Nuclear Information System (INIS)
Kelly, D.L.
1997-01-01
The Maintenance Rule (10 CFR 50.65) went into effect in the United States in July 1996. It requires commercial nuclear utilities to monitor system performance (system reliability and maintenance unavailability) for systems that are determined by the utility to be important to plant safety. Utilities must set performance goals for such systems and monitor system performance against these goals. In addition, these performance goals are intended to be commensurate with the safety significance of the system, which can be established by a probabilistic safety assessment of the plant. The author examines the frequents approach to monitoring performance, which is being used by several utilities, and proposes an alternative Bayesian approach. The Bayesian approach makes more complete use of the information in the probabilistic safety assessment, is consistent philosophically with the subjective interpretation given to probability in most probabilistic safety assessments, overcomes several pitfalls in the frequents approach, provides results which are easily interpretable, and is straightforward to implement using the information in the probabilistic safety assessment
BAYESIAN MAGNETOHYDRODYNAMIC SEISMOLOGY OF CORONAL LOOPS
International Nuclear Information System (INIS)
Arregui, I.; Asensio Ramos, A.
2011-01-01
We perform a Bayesian parameter inference in the context of resonantly damped transverse coronal loop oscillations. The forward problem is solved in terms of parametric results for kink waves in one-dimensional flux tubes in the thin tube and thin boundary approximations. For the inverse problem, we adopt a Bayesian approach to infer the most probable values of the relevant parameters, for given observed periods and damping times, and to extract their confidence levels. The posterior probability distribution functions are obtained by means of Markov Chain Monte Carlo simulations, incorporating observed uncertainties in a consistent manner. We find well-localized solutions in the posterior probability distribution functions for two of the three parameters of interest, namely the Alfven travel time and the transverse inhomogeneity length scale. The obtained estimates for the Alfven travel time are consistent with previous inversion results, but the method enables us to additionally constrain the transverse inhomogeneity length scale and to estimate real error bars for each parameter. When observational estimates for the density contrast are used, the method enables us to fully constrain the three parameters of interest. These results can serve to improve our current estimates of unknown physical parameters in coronal loops and to test the assumed theoretical model.
Bayesian Analysis of Bubbles in Asset Prices
Directory of Open Access Journals (Sweden)
Andras Fulop
2017-10-01
Full Text Available We develop a new model where the dynamic structure of the asset price, after the fundamental value is removed, is subject to two different regimes. One regime reflects the normal period where the asset price divided by the dividend is assumed to follow a mean-reverting process around a stochastic long run mean. The second regime reflects the bubble period with explosive behavior. Stochastic switches between two regimes and non-constant probabilities of exit from the bubble regime are both allowed. A Bayesian learning approach is employed to jointly estimate the latent states and the model parameters in real time. An important feature of our Bayesian method is that we are able to deal with parameter uncertainty and at the same time, to learn about the states and the parameters sequentially, allowing for real time model analysis. This feature is particularly useful for market surveillance. Analysis using simulated data reveals that our method has good power properties for detecting bubbles. Empirical analysis using price-dividend ratios of S&P500 highlights the advantages of our method.
Value of subtyping in studies of irradiation and human leukaemia
Energy Technology Data Exchange (ETDEWEB)
Darby, S C
1985-01-01
The two largest studies of the effects of irradiation on humans in postnatal life are described. These are 1) the Ankylosing Spondylitis Study(ASS) carried out on patients given X-ray therapy as treatment for spondylitis in the UK and 2) the Life Span Study(LSS) carried out on the survivors of the atomic bombings of Hiroshima and Nagasaki. From these studies, a limited degree of subtyping of leukemias is indicated. Chronic lymphatic leukemia is much less readily induced by radiation than the other major subtypes. The inducibility of acute myeloid leukemia increases with age at exposure.
Parent stress across molecular subtypes of children with Angelman syndrome.
Miodrag, N; Peters, S
2015-09-01
Parenting stress has been consistently reported among parents of children with developmental disabilities. However, to date, no studies have investigated the impact of a molecular subtype of Angelman syndrome (AS) on parent stress, despite distinct phenotypic differences among subtypes. Data for 124 families of children with three subtypes of AS: class I and II deletions (n = 99), imprinting centre defects (IC defects; n = 11) and paternal uniparental disomy (UPD; n = 14) were drawn from the AS Rare Diseases Clinical Research Network (RDCRN) database and collected from five research sites across the Unites States. The AS study at the RDCRN gathered health information to understand how the syndrome develops and how to treat it. Parents completed questionnaires on their perceived psychological stress, the severity of children's aberrant behaviour and children's sleep patterns. Children's adaptive functioning and developmental levels were clinically evaluated. Child-related stress reached clinical levels for 40% of parents of children with deletions, 100% for IC defects and 64.3% for UPD. Sleep difficulties were similar and elevated across subtypes. There were no differences between molecular subtypes for overall child and parent-related stress. However, results showed greater isolation and lack of perceived parenting skills for parents of children with UPD compared with deletions. Better overall cognition for children with deletions was significantly related to more child-related stress while their poorer adaptive functioning was associated with more child-related stress. For all three groups, the severity of children's inappropriate behaviour was positively related to different aspects of stress. How parents react to stress depends, in part, on children's AS molecular subtype. Despite falling under the larger umbrella term of AS, it is important to acknowledge the unique aspects associated with children's molecular subtype. Identifying these factors can
The whole-genome landscape of medulloblastoma subtypes
DEFF Research Database (Denmark)
Northcott, Paul A.; Buchhalter, Ivo; Morrissy, A. Sorana
2017-01-01
actionable targets. Driver mutations were confidently assigned to most patients belonging to Group 3 and Group 4 medulloblastoma subgroups, greatly enhancing previous knowledge. New molecular subtypes were differentially enriched for specific driver events, including hotspot in-frame insertions that target...... KBTBD4 and 'enhancer hijacking' events that activate PRDM6. Thus, the application of integrative genomics to an extensive cohort of clinical samples derived from a single childhood cancer entity revealed a series of cancer genes and biologically relevant subtype diversity that represent attractive...
Bayesian signal processing classical, modern, and particle filtering methods
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...
Approximation methods for efficient learning of Bayesian networks
Riggelsen, C
2008-01-01
This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.
Using consensus bayesian network to model the reactive oxygen species regulatory pathway.
Directory of Open Access Journals (Sweden)
Liangdong Hu
Full Text Available Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.
EVA Suit Microbial Leakage Investigation
National Aeronautics and Space Administration — The objective of this project is to collect microbial samples from various EVA suits to determine how much microbial contamination is typically released during...
Jordan, Jennifer; McIntosh, Virginia V W; Carter, Janet D; Rowe, Sarah; Taylor, Kathryn; Frampton, Christopher M A; McKenzie, Janice M; Latner, Janet; Joyce, Peter R
2014-04-01
DSM-5 has dropped subtyping of bulimia nervosa (BN), opting to continue inclusion of the somewhat contentious diagnosis of BN-nonpurging subtype (BN-NP) within a broad BN category. Some contend however that BN-NP is more like binge eating disorder (BED) than BN-P. This study examines clinical characteristics, eating disorder symptomatology, and Axis I comorbidity in BN-NP, BN-P, and BED groups to establish whether BN-NP more closely resembles BN-P or BED. Women with BN-P (n = 29), BN-NP (n = 29), and BED (n = 54) were assessed at baseline in an outpatient psychotherapy trial for those with binge eating. Measures included the Structured Clinical Interviews for DSM-IV, Eating Disorder Examination, and Eating Disorder Inventory-2. The BN-NP subtype had BMIs between those with BN-P and BED. Both BN subtypes had higher Restraint and Drive for Thinness scores than BED. Body Dissatisfaction was highest in BN-NP and predicted BN-NP compared to BN-P. Higher Restraint and lower BMI predicted BN-NP relative to BED. BN-NP resembled BED with higher lifetime BMIs; and weight-loss clinic than eating disorder clinic attendances relative to the BN-P subtype. Psychiatric comorbidity was comparable except for higher lifetime cannabis use disorder in the BN-NP than BN-P subtype These results suggest that BN-NP sits between BN-P and BED however the high distress driving inappropriate compensatory behaviors in BN-P requires specialist eating disorder treatment. These results support retaining the BN-NP group within the BN category. Further research is needed to determine whether there are meaningful differences in outcome over follow-up. Copyright © 2013 Wiley Periodicals, Inc.
Persistence of low-pathogenic H5N7 and H7N1 avian influenza subtypes in filtered natural waters
DEFF Research Database (Denmark)
Nielsen, Anne Ahlmann; Jensen, Trine Hammer; Stockmarr, Anders
2013-01-01
knowledge on the influence of environmental factors on the persistence of AIV in natural habitats would be valuable for risk assessments. The presented work investigated the persistence of two low-pathogenic AIV subtypes in natural water samples. The study included two AIVs formerly isolated from wild ducks......, the persistence of infectivity was negatively affected by increased temperature, salinity as well as presence of natural microbial flora. The study provides insight on impact of essential physical, chemical and biological parameters on persistence of AIV in aquatic environments. Studies determining the importance...
Dilernia, Dario Alberto; Jones, Leandro; Rodriguez, Sabrina; Turk, Gabriela; Rubio, Andrea E.; Pampuro, Sandra; Gomez-Carrillo, Manuel; Bautista, Christian; Deluchi, Gabriel; Benetucci, Jorge; Lasala, María Beatriz; Lourtau, Leonardo; Losso, Marcelo Horacio; Perez, Héctor; Cahn, Pedro; Salomón, Horacio
2008-01-01
Background Cytotoxic T-Lymphocyte (CTL) response drives the evolution of HIV-1 at a host-level by selecting HLA-restricted escape mutations. Dissecting the dynamics of these escape mutations at a population-level would help to understand how HLA-mediated selection drives the evolution of HIV-1. Methodology/Principal Findings We undertook a study of the dynamics of HIV-1 CTL-escape mutations by analyzing through statistical approaches and phylogenetic methods the viral gene gag sequenced in plasma samples collected between the years 1987 and 2006 from 302 drug-naïve HIV-positive patients. By applying logistic regression models and after performing correction for multiple test, we identified 22 potential CTL-escape mutations (p-value<0.05; q-value<0.2); 10 of these associations were confirmed in samples biologically independent by a Bayesian Markov Chain Monte-Carlo method. Analyzing their prevalence back in time we found that escape mutations that are the consensus residue in samples collected after 2003 have actually significantly increased in time in one of either B or F subtype until becoming the most frequent residue, while dominating the other viral subtype. Their estimated prevalence in the viral subtype they did not dominate was lower than 30% for the majority of samples collected at the end of the 80's. In addition, when screening the entire viral region, we found that the 75% of positions significantly changing in time (p<0.05) were located within known CTL epitopes. Conclusions Across HIV Gag protein, the rise of polymorphisms from independent origin during the last twenty years of epidemic in our setting was related to an association with an HLA allele. The fact that these mutations accumulated in one of either B or F subtypes have also dominated the other subtype shows how this selection might be causing a convergence of viral subtypes to variants which are more likely to evade the immune response of the population where they circulate. PMID:18941505
Molecular ecology of microbial mats
Bolhuis, H.; Cretoiu, M.S.; Stal, L.J.
2014-01-01
Phototrophic microbial mats are ideal model systems for ecological and evolutionary analysis of highly diverse microbial communities. Microbial mats are small-scale, nearly closed, and self-sustaining benthic ecosystems that comprise the major element cycles, trophic levels, and food webs. The steep
Brain neurodevelopmental markers related to the deficit subtype of schizophrenia.
Takahashi, Tsutomu; Takayanagi, Yoichiro; Nishikawa, Yumiko; Nakamura, Mihoko; Komori, Yuko; Furuichi, Atsushi; Kido, Mikio; Sasabayashi, Daiki; Noguchi, Kyo; Suzuki, Michio
2017-08-30
Deficit schizophrenia is a homogeneous subtype characterized by a trait-like feature of primary and prominent negative symptoms, but the etiologic factors related to this specific subtype remain largely unknown. This magnetic resonance imaging study aimed to examine gross brain morphology that probably reflects early neurodevelopment in 38 patients with deficit schizophrenia, 37 patients with non-deficit schizophrenia, and 59 healthy controls. Potential brain neurodevelopmental markers investigated in this study were the adhesio interthalamica (AI), cavum septi pellucidi (CSP), and surface morphology (i.e., olfactory sulcus depth, sulcogyral pattern, and number of orbital sulci) of the orbitofrontal cortex (OFC). The subtype classification of schizophrenia patients was based on the score of Proxy for the Deficit Syndrome. The deficit schizophrenia group had a significantly shorter AI compared with the non-deficit group and controls. The deficit group, but not the non-deficit group, was also characterized by an altered distribution of the OFC sulcogyral pattern, as well as fewer posterior orbital sulcus compared with controls. Other neurodevelopmental markers did not differentiate the deficit and non-deficit subgroups. These results suggest that the deficit subtype of schizophrenia and its clinical manifestation may be at least partly related to prominent neurodevelopmental pathology. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.
Outcomes of treatment for achalasia depend on manometric subtype
Rohof, Wout O.; Salvador, Renato; Annese, Vito; Bruley des Varannes, Stanislas; Chaussade, Stanislas; Costantini, Mario; Elizalde, J. Ignasi; Gaudric, Marianne; Smout, Andre J.; Tack, Jan; Busch, Olivier R.; Zaninotto, Giovanni; Boeckxstaens, Guy E.
2013-01-01
Patients with achalasia are treated with either pneumatic dilation (PD) or laparoscopic Heller myotomy (LHM), which have comparable rates of success. We evaluated whether manometric subtype was associated with response to treatment in a large population of patients treated with either PD or LHM (the
Social Withdrawal Subtypes during Early Adolescence in India
Bowker, Julie C.; Raja, Radhi
2011-01-01
The overarching goal of this study was to examine the associations between three social withdrawal subtypes (shyness, unsociability, avoidance), peer isolation, peer difficulties (victimization, rejection, exclusion, low acceptance), and loneliness in India during early adolescence. Participants were 194 adolescents in Surat, India (M age=13.35…
Effects of tamsulosin metabolites at alpha-1 adrenoceptor subtypes
Taguchi, K.; Saitoh, M.; Sato, S.; Asano, M.; Michel, M. C.
1997-01-01
We have investigated the affinity and selectivity of tamsulosin and its metabolites, M1, M2, M3, M4 and AM1, at the tissue and the cloned alpha-1 adrenoceptor subtypes in the radioligand binding and the functional studies. In the radioligand binding studies, the compounds competed for [3H]prazosin
Psychosocial and Adaptive Deficits Associated with Learning Disability Subtypes
Backenson, Erica M.; Holland, Sara C.; Kubas, Hanna A.; Fitzer, Kim R.; Wilcox, Gabrielle; Carmichael, Jessica A.; Fraccaro, Rebecca L.; Smith, Amanda D.; Macoun, Sarah J.; Harrison, Gina L.; Hale, James B.
2015-01-01
Children with specific learning disabilities (SLD) have deficits in the basic psychological processes that interfere with learning and academic achievement, and for some SLD subtypes, these deficits can also lead to emotional and/or behavior problems. This study examined psychosocial functioning in 123 students, aged 6 to 11, who underwent…
The global spread of HIV-1 subtype B epidemic
Magiorkinis, Gkikas; Angelis, Konstantinos; Mamais, Ioannis; Katzourakis, Aris; Hatzakis, Angelos; Albert, Jan; Lawyer, Glenn; Hamouda, Osamah; Struck, Daniel; Vercauteren, Jurgen; Wensing, Annemarie; Alexiev, Ivailo; Åsjö, Birgitta; Balotta, Claudia; Gomes, Perpétua; Camacho, Ricardo J.; Coughlan, Suzie; Griskevicius, Algirdas; Grossman, Zehava; Horban, Anders; Kostrikis, Leondios G.; Lepej, Snjezana J.; Liitsola, Kirsi; Linka, Marek; Nielsen, Claus; Otelea, Dan; Paredes, Roger; Poljak, Mario; Puchhammer-Stöckl, Elizabeth; Schmit, Jean Claude; Sönnerborg, Anders; Staneková, Danica; Stanojevic, Maja; Stylianou, Dora C.; Boucher, Charles A B; Nikolopoulos, Georgios; Vasylyeva, Tetyana; Friedman, Samuel R.; van de Vijver, David; Angarano, Gioacchino; Chaix, Marie Laure; de Luca, Andrea; Korn, Klaus; Loveday, Clive; Soriano, Vincent; Yerly, Sabine; Zazzi, Mauricio; Vandamme, Anne Mieke; Paraskevis, Dimitrios
2016-01-01
Human immunodeficiency virus type 1 (HIV-1) was discovered in the early 1980s when the virus had already established a pandemic. For at least three decades the epidemic in the Western World has been dominated by subtype B infections, as part of a sub-epidemic that traveled from Africa through Haiti
Cognitive Profiling and Preliminary Subtyping in Chinese Developmental Dyslexia
Ho, Connie Suk-Han; Chan, David Wai-Ock; Lee, Suk-Han; Tsang, Suk-Man; Luan, Vivian Hui
2004-01-01
The present study examined the cognitive profile and subtypes of developmental dyslexia in a nonalphabetic script, Chinese. One hundred and forty-seven Chinese primary school children with developmental dyslexia were tested on a number of literacy and cognitive tasks. The results showed that rapid naming deficit and orthographic deficit were the…
Longitudinal Stability of Phonological and Surface Subtypes of Developmental Dyslexia
Peterson, Robin L.; Pennington, Bruce F.; Olson, Richard K.; Wadsworth, Sally J.
2014-01-01
Limited evidence supports the external validity of the distinction between developmental phonological and surface dyslexia. We previously identified children ages 8 to 13 meeting criteria for these subtypes (Peterson, Pennington, & Olson, 2013) and now report on their reading and related skills approximately 5 years later. Longitudinal…
Brief Report: Further Evidence of Sensory Subtypes in Autism
Lane, Alison E.; Dennis, Simon J.; Geraghty, Maureen E.
2011-01-01
Distinct sensory processing (SP) subtypes in autism have been reported previously. This study sought to replicate the previous findings in an independent sample of thirty children diagnosed with an Autism Spectrum Disorder. Model-based cluster analysis of parent-reported sensory functioning (measured using the Short Sensory Profile) confirmed the…
Sensory Processing Subtypes in Autism: Association with Adaptive Behavior
Lane, Alison E.; Young, Robyn L.; Baker, Amy E. Z.; Angley, Manya T.
2010-01-01
Children with autism are frequently observed to experience difficulties in sensory processing. This study examined specific patterns of sensory processing in 54 children with autistic disorder and their association with adaptive behavior. Model-based cluster analysis revealed three distinct sensory processing subtypes in autism. These subtypes…
Distinct Response Time Distributions in Attention Deficit Hyperactivity Disorder Subtypes
Querne, Laurent; Berquin, Patrick
2009-01-01
Objective: To address the issue of response time (RT) profiles in hyperactive-impulsive (ADHD-HI), inattentive (ADHD-IA), and combined (ADHD-C) subtypes of ADHD. We hypothesized that children with ADHD-HI should respond more rapidly than children without ADHD and children with ADHD-IA and ADHD-C should respond more slowly than children without…
Breast cancer molecular subtype classifier that incorporates MRI features.
Sutton, Elizabeth J; Dashevsky, Brittany Z; Oh, Jung Hun; Veeraraghavan, Harini; Apte, Aditya P; Thakur, Sunitha B; Morris, Elizabeth A; Deasy, Joseph O
2016-07-01
To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes. This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.4%), ERPR-/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test. Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122-129. © 2016 Wiley Periodicals, Inc.
Molecular Subtypes and Clinical Outcomes of Breast Cancer
African Journals Online (AJOL)
2010-01-04
Jan 4, 2010 ... Some early work has started in earnest at both the. AKUH(N) and Kijabe Hospitals to try and stratify our breast cancer patients to those mentioned subtypes to help in both diagnosis and treatment. The limiting fac- tors are small numbers of patients expense to undertake the tests and lack of both internal ...
[Distorted cognition of bodily sensations in subtypes of social anxiety].
Kanai, Yoshihiro; Sasaki, Shoko; Iwanaga, Makoto; Seiwa, Hidetoshi
2010-02-01
The purpose of this study was to investigate the relationship between subtypes of social anxiety and distorted cognition of bodily sensations. The package of questionnaires including the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) was administered to 582 undergraduate students. To identify subtypes of social anxiety, cluster analysis was conducted using scores of the SPS and SIAS. Five clusters were identified and labeled as follows: Generalized type characterized by intense anxiety in most social situations, Non-anxious type characterized by low anxiety levels in social situations, Averaged type whose anxiety levels are averaged, Interaction anxiety type who feels anxiety mainly in social interaction situations, and Performance anxiety type who feels anxiety mainly in performance situations. Results of an ANOVA indicated that individuals with interaction type fear the negative evaluation from others regarding their bodily sensations whereas individuals with performance type overestimate the visibility of their bodily sensations to others. Differences in salient aspects of cognitive distortion among social anxiety subtypes may show necessity to select intervention techniques in consideration of subtypes.
A study of symptom profile and clinical subtypes of delirium
Meagher, David
2012-01-01
Delineating delirium phenomenology facilitates detection, understanding neuroanatomical endophenotypes, and patient management. This compendium reflects an integrated research plan executed over a five year period, employing detailed, standardized phenomenological assessments cross-sectionally and longitudinally. Motor activity studies were controlled and included both subjective and objective measures, aimed at identifying a new approach to defining this clinical subtype as a more pure motor...
Central Timing Deficits in Subtypes of Primary Speech Disorders
Peter, Beate; Stoel-Gammon, Carol
2008-01-01
Childhood apraxia of speech (CAS) is a proposed speech disorder subtype that interferes with motor planning and/or programming, affecting prosody in many cases. Pilot data (Peter & Stoel-Gammon, 2005) were consistent with the notion that deficits in timing accuracy in speech and music-related tasks may be associated with CAS. This study…
Preoperative subtyping of meningiomas by perfusion MR imaging
Energy Technology Data Exchange (ETDEWEB)
Zhang, Hao [University Medical Center Groningen, University of Groningen (Netherlands); Shanghai Jiaotong University affiliated First People' s Hospital, Department of Radiology, Shanghai (China); Department of Radiology, University of Groningen (Netherlands); Roediger, Lars A.; Oudkerk, Matthijs [University Medical Center Groningen, University of Groningen (Netherlands); Department of Radiology, University of Groningen (Netherlands); Shen, Tianzhen [Fudan University Huashan Hospital, Department of Radiology, Shanghai (China); Miao, Jingtao [Shanghai Jiaotong University affiliated First People' s Hospital, Department of Radiology, Shanghai (China)
2008-10-15
This paper aims to evaluate the value of perfusion magnetic resonance (MR) imaging in the preoperative subtyping of meningiomas by analyzing the relative cerebral blood volume (rCBV) of three benign subtypes and anaplastic meningiomas separately. Thirty-seven meningiomas with peritumoral edema (15 meningothelial, ten fibrous, four angiomatous, and eight anaplastic) underwent perfusion MR imaging by using a gradient echo echo-planar sequence. The maximal rCBV (compared with contralateral normal white matter) in both tumoral parenchyma and peritumoral edema of each tumor was measured. The mean rCBVs of each two histological subtypes were compared using one-way analysis of variance and least significant difference tests. A p value less than 0.05 indicated a statistically significant difference. The mean rCBV of meningothelial, fibrous, angiomatous, and anaplastic meningiomas in tumoral parenchyma were 6.93{+-}3.75, 5.61{+-}4.03, 11.86{+-}1.93, and 5.89{+-}3.85, respectively, and in the peritumoral edema 0.87{+-}0.62, 1.38{+-}1.44, 0.87{+-}0.30, and 3.28{+-}1.39, respectively. The mean rCBV in tumoral parenchyma of angiomatous meningiomas and in the peritumoral edema of anaplastic meningiomas were statistically different (p<0.05) from the other types of meningiomas. Perfusion MR imaging can provide useful functional information on meningiomas and help in the preoperative diagnosis of some subtypes of meningiomas. (orig.)
Detection and subtyping avian metapneumovirus from turkeys in Iran.
Mayahi, Mansour; Momtaz, Hassan; Jafari, Ramezan Ali; Zamani, Pejman
2017-01-01
Avian metapneumovirus (aMPV) causes diseases like rhinotracheitis in turkeys, swollen head syndrome in chickens and avian rhinotracheitis in other birds. Causing respiratory problems, aMPV adversely affects production and inflicts immense economic losses and mortalities, especially in turkey flocks. In recent years, several serological and molecular studies have been conducted on this virus, especially in poultry in Asia and Iran. The purpose of the present study was detecting and subtyping aMPV by reverse transcriptase polymerase chain reaction (RT-PCR) from non-vaccinated, commercial turkey flocks in Iran for the first time. Sixty three meat-type unvaccinated turkey flocks from several provinces of Iran were sampled in major turkey abattoirs. Samples were tested by RT-PCR for detecting and subtyping aMPV. The results showed that 26 samples from three flocks (4.10%) were positive for viral RNA and all of the viruses were found to be subtype B of aMPV. As a result, vaccination especially against subtype B of aMPV should be considered in turkey flocks in Iran to control aMPV infections.
Racial Differences by Ischemic Stroke Subtype: A Comprehensive Diagnostic Approach
Directory of Open Access Journals (Sweden)
Sarah Song
2012-01-01
Full Text Available Background. Previous studies have suggested that black populations have more small-vessel and fewer cardioembolic strokes. We sought to analyze racial differences in ischemic stroke subtype employing a comprehensive diagnostic workup with magnetic resonance-imaging-(MRI- based evaluation including diffusion-weighted imaging (DWI. Methods. 350 acute ischemic stroke patients admitted to an urban hospital with standardized comprehensive diagnostic evaluations were retrospectively analyzed. Ischemic stroke subtype was determined by three Trial of Org 10172 in Acute Stroke Treatment (TOAST classification systems. Results. We found similar proportions of cardioembolic and lacunar strokes in the black and white cohort. The only subtype category with a significant difference by race was “stroke of other etiology,” more common in whites. Black stroke patients were more likely to have an incomplete evaluation, but this did not reach significance. Conclusions. We found similar proportions by race of cardioembolic and lacunar strokes when employing a full diagnostic evaluation including DWI MRI. The relatively high rate of cardioembolism may have been underappreciated in black stroke patients when employing a CT approach to stroke subtype diagnosis. Further research is required to better understand the racial differences in frequency of “stroke of other etiology” and explore disparities in the extent of diagnostic evaluations.
The global spread of HIV-1 subtype B epidemic
G. Magiorkinis (Gkikas); K. Angelis (Konstantinos); I. Mamais (Ioannis); Katzourakis, A. (Aris); A. Hatzakis (Angelos); J. Albert (Jan); Lawyer, G. (Glenn); O. Hamouda (Osamah); D. Struck (Daniel); J. Vercauteren (Jurgen); A. Wensing (Amj); I. Alexiev (Ivailo); B. Åsjö (Birgitta); C. Balotta (Claudia); Gomes, P. (Perpétua); R.J. Camacho (Ricardo Jorge); S. Coughlan (Suzie); A. Griskevicius (Algirdas); Z. Grossman (Zehava); Horban, A. (Anders); L.G. Kostrikis (Leondios); Lepej, S.J. (Snjezana J.); K. Liitsola (Kirsi); M. Linka (Marek); C. Nielsen; D. Otelea (Dan); R. Paredes (Roger); M. Poljak (Mario); E. Puchhammer-Stöckl (Elisabeth); J.C. Schmit; A. Sonnerborg (Anders); D. Stanekova (Danica); M. Stanojevic (Maja); Stylianou, D.C. (Dora C.); C.A.B. Boucher (Charles); Nikolopoulos, G. (Georgios); Vasylyeva, T. (Tetyana); Friedman, S.R. (Samuel R.); D.A.M.C. van de Vijver (David); G. Angarano (Guiseppe); M.L. Chaix (Marie Laure); A. de Luca (Andrea); K. Korn (Klaus); Loveday, C. (Clive); V. Soriano (Virtudes); S. Yerly (Sabine); M. Zazzi; A.M. Vandamme (Anne Mieke); D. Paraskevis (Dimitrios)
2016-01-01
textabstractHuman immunodeficiency virus type 1 (HIV-1) was discovered in the early 1980s when the virus had already established a pandemic. For at least three decades the epidemic in the Western World has been dominated by subtype B infections, as part of a sub-epidemic that traveled from Africa
Multiple estrogen receptor subtypes influence ingestive behavior in female rodents.
Santollo, Jessica; Daniels, Derek
2015-12-01
Postmenopausal women are at an increased risk of obesity and cardiovascular-related diseases. This is attributable, at least in part, to loss of the ovarian hormone estradiol, which inhibits food and fluid intake in humans and laboratory animal models. Although the hypophagic and anti-dipsogenic effects of estradiol have been well documented for decades, the precise mechanisms underlying these effects are not fully understood. An obvious step toward addressing this open question is identifying which estrogen receptor subtypes are involved and what intracellular processes are involved. This question, however, is complicated not only by the variety of estrogen receptor subtypes that exist, but also because many subtypes have multiple locations of action (i.e. in the nucleus or in the plasma membrane). This review will highlight our current understanding of the roles that specific estrogen receptor subtypes play in mediating estradiol's anorexigenic and anti-dipsogenic effects along with highlighting the many open questions that remain. This review will also describe recent work being performed by our laboratory aimed at answering these open questions. Copyright © 2015 Elsevier Inc. All rights reserved.
Retinal Ganglion Cell Diversity and Subtype Specification from Human Pluripotent Stem Cells
Directory of Open Access Journals (Sweden)
Kirstin B. Langer
2018-04-01
Full Text Available Summary: Retinal ganglion cells (RGCs are the projection neurons of the retina and transmit visual information to postsynaptic targets in the brain. While this function is shared among nearly all RGCs, this class of cell is remarkably diverse, comprised of multiple subtypes. Previous efforts have identified numerous RGC subtypes in animal models, but less attention has been paid to human RGCs. Thus, efforts of this study examined the diversity of RGCs differentiated from human pluripotent stem cells (hPSCs and characterized defined subtypes through the expression of subtype-specific markers. Further investigation of these subtypes was achieved using single-cell transcriptomics, confirming the combinatorial expression of molecular markers associated with these subtypes, and also provided insight into more subtype-specific markers. Thus, the results of this study describe the derivation of RGC subtypes from hPSCs and will support the future exploration of phenotypic and functional diversity within human RGCs. : In this article, Langer and colleagues present extensive characterization of RGC subtypes derived from human pluripotent stem cells, with multiple subtypes identified by subtype-specific molecular markers. Their results present a more detailed analysis of RGC diversity in human cells and yield the use of different markers to identify RGC subtypes. Keywords: iPSC, retina, retinal ganglion cell, RGC subtype, stem cell, ipRGC, alpha RGC, direction selective RGC, RNA-seq
Anaerobic microbial dehalogenation
Smidt, H.; Vos, de W.M.
2004-01-01
The natural production and anthropogenic release of halogenated hydrocarbons into the environment has been the likely driving force for the evolution of an unexpectedly high microbial capacity to dehalogenate different classes of xenobiotic haloorganics. This contribution provides an update on the
Severin, I.; Stal, L.J.; Seckbach, J.; Oren, A.
2010-01-01
Microbial mats have been the focus of scientific research for a few decades. These small-scale ecosystems are examples of versatile benthic communities of microorganisms, usually dominated by phototrophic bacteria (e.g., Krumbein et al., 1977; Jørgensen et al., 1983). They develop as vertically
Energy Technology Data Exchange (ETDEWEB)
Buckley, Merry [American Society for Microbiology (ASM), Washington, DC (United States); Wall, Judy D. [Univ. of Missouri, Columbia, MO (United States)
2006-10-01
The American Academy of Microbiology convened a colloquium March 10-12, 2006, in San Francisco, California, to discuss the production of energy fuels by microbial conversions. The status of research into various microbial energy technologies, the advantages and disadvantages of each of these approaches, research needs in the field, and education and training issues were examined, with the goal of identifying routes for producing biofuels that would both decrease the need for fossil fuels and reduce greenhouse gas emissions. Currently, the choices for providing energy are limited. Policy makers and the research community must begin to pursue a broader array of potential energy technologies. A diverse energy portfolio that includes an assortment of microbial energy choices will allow communities and consumers to select the best energy solution for their own particular needs. Funding agencies and governments alike need to prepare for future energy needs by investing both in the microbial energy technologies that work today and in the untested technologies that will serve the world’s needs tomorrow. More mature bioprocesses, such as ethanol production from starchy materials and methane from waste digestors, will find applications in the short term. However, innovative techniques for liquid fuel or biohydrogen production are among the longer term possibilities that should also be vigorously explored, starting now. Microorganisms can help meet human energy needs in any of a number of ways. In their most obvious role in energy conversion, microorganisms can generate fuels, including ethanol, hydrogen, methane, lipids, and butanol, which can be burned to produce energy. Alternatively, bacteria can be put to use in microbial fuel cells, where they carry out the direct conversion of biomass into electricity. Microorganisms may also be used some day to make oil and natural gas technologies more efficient by sequestering carbon or by assisting in the recovery of oil and
Microbial electrosynthesis of biochemicals
Bajracharya, S.
2016-01-01
Microbial electrosynthesis (MES) is an electricity-driven production of chemicals from low-value waste using microorganisms as biocatalysts. MES from CO2 comprises conversion of CO2 to multi-carbon compounds employing microbes at the cathode which use electricity as an energy source. This thesis
The subtype of VSD and the angiographic differentiation
International Nuclear Information System (INIS)
Choe, Kyu Ok; Sul, Jun Hee; Lee, Sung Kyu; Cho, Bum Koo; Hong, Sung Nok
1985-01-01
VSD is the most common congenital cardiac malformation and the natural history depends not only on the age of patients and the size of defect but the subtype of VSD as well, important factor in clinical management of those patients. In 110 patients, with surgically repaired VSD in Yonsei Medical Center in 1984, the subtype of VSDs evaluated by surgical observation were correlated with LV angiogram findings to verify the incidence of subtype in Korean and the diagnostic accuracy to predict the subtype by angiogram. 1. 110 patients included 64 boys and 46 girls, the age ranged from 3 months to 14 years (average 4.6 years old). 2. Angiographic findings were interpreted as follows; a. Perimembranous defects were profiled in LAO 60 .deg. LV angiogram and located below the aortic valve. In inlet excavation the shunted blood opacified the recess between septal leaflet of tricuspid valve and interventricular septum in early phase, in infundibular excavation opacified the recess between anterior leaflet of TV and anterior free wall of RV and in trabecular excavation the shunted blood traversed anterior portion of TV ring, opacified trabecular portion of RV cavity. b. Subarterial types were profiled in RAO 30 .deg. LV angiogram, just below aortic valve as well as pulmonic valve. Total infundibular defects were profiled in RAO 30 .deg. and LAO 60 .deg. LV angiogram subaortic in location in both views. c. In muscular VSD the profiled angle was varied according to the subtype but the defects were separated from the aortic valve as muscular septum interposed between the aortic valve and the defect. 3. The incidence of subtype of VSDs evaluated by surgical observation were as follows. Subarterial type : 32 cases (29.1%) Total infundibular defect : 5 cases (4.5%) Perimembranous type : 73 cases (66.3%) Infundibular excavation : 32 cases (29.2%) Trabecular excavation : 28 cases (25.5%) Inlet excavation : 10 cases (9.1%) Mixed : 3 cases (2.7%) Muscular type : 1 cases (0.9%) Total 63
Bayesian ensemble refinement by replica simulations and reweighting
Hummer, Gerhard; Köfinger, Jürgen
2015-12-01
We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.
Bayesian inference for psychology. Part II: Example applications with JASP.
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.
Daniel Goodman’s empirical approach to Bayesian statistics
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.
Bayesian data analysis in population ecology: motivations, methods, and benefits
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.
Numerical Methods for Bayesian Inverse Problems
Ernst, Oliver
2014-01-06
We present recent results on Bayesian inversion for a groundwater flow problem with an uncertain conductivity field. In particular, we show how direct and indirect measurements can be used to obtain a stochastic model for the unknown. The main tool here is Bayes’ theorem which merges the indirect data with the stochastic prior model for the conductivity field obtained by the direct measurements. Further, we demonstrate how the resulting posterior distribution of the quantity of interest, in this case travel times of radionuclide contaminants, can be obtained by Markov Chain Monte Carlo (MCMC) simulations. Moreover, we investigate new, promising MCMC methods which exploit geometrical features of the posterior and which are suited to infinite dimensions.
A Predictive Likelihood Approach to Bayesian Averaging
Directory of Open Access Journals (Sweden)
Tomáš Jeřábek
2015-01-01
Full Text Available Multivariate time series forecasting is applied in a wide range of economic activities related to regional competitiveness and is the basis of almost all macroeconomic analysis. In this paper we combine multivariate density forecasts of GDP growth, inflation and real interest rates from four various models, two type of Bayesian vector autoregression (BVAR models, a New Keynesian dynamic stochastic general equilibrium (DSGE model of small open economy and DSGE-VAR model. The performance of models is identified using historical dates including domestic economy and foreign economy, which is represented by countries of the Eurozone. Because forecast accuracy of observed models are different, the weighting scheme based on the predictive likelihood, the trace of past MSE matrix, model ranks are used to combine the models. The equal-weight scheme is used as a simple combination scheme. The results show that optimally combined densities are comparable to the best individual models.
Efficient Bayesian network modeling of systems
International Nuclear Information System (INIS)
Bensi, Michelle; Kiureghian, Armen Der; Straub, Daniel
2013-01-01
The Bayesian network (BN) is a convenient tool for probabilistic modeling of system performance, particularly when it is of interest to update the reliability of the system or its components in light of observed information. In this paper, BN structures for modeling the performance of systems that are defined in terms of their minimum link or cut sets are investigated. Standard BN structures that define the system node as a child of its constituent components or its minimum link/cut sets lead to converging structures, which are computationally disadvantageous and could severely hamper application of the BN to real systems. A systematic approach to defining an alternative formulation is developed that creates chain-like BN structures that are orders of magnitude more efficient, particularly in terms of computational memory demand. The formulation uses an integer optimization algorithm to identify the most efficient BN structure. Example applications demonstrate the proposed methodology and quantify the gained computational advantage