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Chapman and Hall/CRC – 2010, 300 pages ISBN: 1439836140 Explores a wide range of Bayesian model selection criteria Covers Bayesian estimation methods and modern Bayesian computing methods, including the Laplace–Metropolis estimator and the kernel density estimation Offers practical advice on simulation-based Bayesian model evaluation methods Applies Bayesian model...
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Springer, 2017. — 242 p. BAYSM 2016, Florence, Italy, June 19-21. 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...
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Springer Nature Singapore Pte Ltd., 2017. — 552 p. — ISBN 9811041172. This book presents operational modal analysis (OMA), employing a coherent and comprehensive Bayesian framework for modal identification and covering stochastic modeling, theoretical formulations, computational algorithms, and practical applications. Mathematical similarities and philosophical differences between...
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New York: Springer, 2017. — 552 p. This book presents operational modal analysis (OMA), employing a coherent and comprehensive Bayesian framework for modal identification and covering stochastic modeling, theoretical formulations, computational algorithms, and practical applications. Mathematical similarities and philosophical differences between Bayesian and classical statistical...
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Valencia: Valencia University Press, 1980. — 647 p. At conferences devoted to the foun(lations of proDability and statistics, it is natural that attention siould focus on points of division between supporters of rival schools of tiouglt. The resulting confrontation of ideas and personalities in sucll contexts is often stimulating and useful in sharpening perceptions about one's...
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Wiley series in probability and statistics. Wiley & Sons, Ltd, 2000. - 611 pages. This volume, first published in hardback in 1994, presents an overview of the foundations and key theoretical concepts of Bayesian Statistics. The world of Bayesian Statistics has been changing shape and growing in size rapidly and unpredictably - most notably in relation to developments in...
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New York: Springer, 2016. - 246p. This book provides an introduction to elementary probability and to Bayesian statistics using de Finetti's subjectivist approach. One of the features of this approach is that it does not require the introduction of sample space – a non-intrinsic concept that makes the treatment of elementary probability unnecessarily complicate – but introduces...
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2nd ed. — Wiley, 2007. — 463 p. The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Edition focuses on Bayesian methods that can be used for inference, and it also addresses how these methods...
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Wiley, 2007. — 463 p. — 2nd ed. — ISBN: 0470141158, 9780470141151 The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Edition focuses on Bayesian methods that can be used for inference, and it...
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Wiley, 2010. — 334 p. — ISBN: 0470046090, 9780470046098. 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...
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N.-Y.: Wiley, 2009. — 336 p. 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...
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3rd Edition. — Hoboken: Wiley, 2016. — 620 p. "…this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in...
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Boca Raton: Chapman & Hall/CRC, 2009. — 326 p. — (Chapman & Hall/CRC Biostatistics Series). — ISBN 1420083414, 9781420083415. Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in...
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New York: CRC Press, 2015. - 568p. Analyze Repeated Measures Studies Using Bayesian Techniques Going beyond standard non-Bayesian books, Bayesian Methods for Repeated Measures presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian viewpoint. It describes many inferential methods for analyzing repeated measures in various scientific...
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New York: Springer, 2007. — 215 p. This book has been written for undergraduate and graduate students in various areas of mathematics and its applications. It is for students who are willing to get acquainted with Bayesian approach to computational science but not necessarily to go through the full immersion into the statistical analysis. It has also been written for...
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Third Edition. — CHAPMAN & HALL/CRC. 2008. — 552 p. Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via...
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Springer – 2010, 636 pages. ISBN 9781441969439. Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not...
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CRC Press, 2011. — 518 p. — ISBN: 1439803544, 9781439803547. Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for...
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Wiley, 2005. — 447 p. — ISBN 0470092378, 9780470092378. The use of Bayesian methods for the analysis of data has grown substantially in areas as diverse as applied statistics, psychology, economics and medical science. Bayesian Methods for Categorical Data sets out to demystify modern Bayesian methods, making them accessible to students and researchers alike. Emphasizing the use...
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2nd Edition. — Wiley & Sons, Ltd, 2006. — 598 p. — (Wiley Series in Probability and Statistics). Contents: Introduction: The Bayesian Method, its Benefits and Implementation Bayesian Model Choice, Comparison and Checking The Major Densities and their Application Normal Linear Regression, General Linear Models and Log-Linear Models Hierarchical Priors for Pooling Strength...
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Springer, 2013. — 236p. — ISBN: 1461456959, 9781461456964 Series: Springer Texts in Statistics, Vol. 98 This book is based on over a dozen years teaching a Bayesian Statistics course. The material presented here has been used by students of different levels and disciplines, including advanced undergraduates studying Mathematics and Statistics and students in graduate programs...
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Oxford: Oxford University Press, 2013. - 700p. 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...
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Springer, 2015. — 366 p. — (Springer Proceedings in Mathematics & Statistics 118). — ISBN 978-3-319-12453-7. 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...
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Elsevier, 2005. — 1062 p. — ISBN: 0444515399, 9780444515391, 9780080461175 This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences...
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New York: O’Reilly Media, 2013. — 209 p. If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out...
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Andrew Gelman, John B. Carlin, Hal S. Stern, Donald B. Rubin, David B. Dunson, Aki Vehtari. CRC Press, 2013. — 675 p. — 3rd ed. — ISBN: 1439840954. Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition...
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Chapman and Hall/CRC – 2003, 696 pages, 2nd edition ISBN: 158488388X, 9781584883883 Incorporating new and updated information, this second edition of The bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide...
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Second Edition. — Chapman & Hall/CRC, 2004. — (Тexts in Statistical Science). Incorporating new and updated information, this second edition of THE bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide...
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Wiley, 2005. — 323 p. — ISBN: 0471679321, 9780471679325 Tools to improve decision making in an imperfect world This publication provides readers with a thorough understanding of Bayesian analysis that is grounded in the theory of inference and optimal decision making. Contemporary Bayesian Econometrics and Statistics provides readers with state-of-the-art simulation methods...
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Springer – 2006, 366 pages ISBN: 0387400842 This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current...
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Springer, 2006. — 355 p. This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory, methods, and applications. Starting from basic statistics, undergraduate calculus and linear algebra, ideas of both subjective and objective Bayesian analysis are developed to a level where real-life data can be analyzed using the current techniques of statistical...
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Springer, 2003. — 310 p. Bayesian nonparametrics has grown tremendously in the last three decades, especially in the last few years. This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. While the book is of special interest to Bayesians, it will also appeal to statisticians in general because Bayesian nonparametrics offers a...
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Amsterdam: North-Holland, 1986. - 254 p. The primary objective of this volume is to describe the impact of Professor Bruno de Finetti's contributions on statistical theory and practice, and to provide a selection of recent and applied research in Bayesian statistics and econometrics. Included are papers (all previously unpublished) from leading econometricians and statisticians...
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Wiley – 2007, 538 pages ISBN: 0470015624, 9780470015629 Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs...
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Cambridge: Cambridge University Press, 2010. — 308 p. Contents Contributors What is it all about? Who needs it? The aims, purposes and contents of this book What does this book do? How do alternative models relate to each other? A brief history of Bayesian nonparametrics From the start to the present Applications Where does this book fit in the broader...
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Springer, 2009. — 276 p. — ISBN 0387922997. This book provides a compact self-contained introduction to the theory and application of Bayesian statistical methods. The book is accessible to readers having a basic familiarity with probability, yet allows more advanced readers to quickly grasp the principles underlying Bayesian theory and methods. The examples and computer code...
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Springer, 2008. — 322 p. — ISBN: 978-3540850656, e-ISBN: 978-3540850663. Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained. Both...
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Chichester: John Wiley & Sons, Ltd, 2009. — 573 p. — (Wiley Series in Probablity and Statistics). — ISBN 0470011548. Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored...
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Elsevier, 1984. — 326 p. Robustness is a fundamental issue for all statistical analyses; in fact it might be argued that robustness is the subject of statistics. In Bayesian statistics, the prior distribution can be seen as weighting the possible values of the parameter by their probability. The studies reported in this volume concern the sensitivity of Bayesian analyses to the...
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Springer, 1992. — 242 p. — ISBN 9789048157907, 9048157900. The debate between the proponents of "classical" and "Bayesian" statistical methods continues unabated. It is not the purpose of the text to resolve those issues but rather to demonstrate that within the realm of actuarial science there are a number of problems that are particularly suited for Bayesian analysis. This has...
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Springer – 2007, 250 pages. ISBN: 354072723X. The Introduction to Bayesian Statistics (2nd Edition) presents Bayes’ theorem, the estimation of unknown parameters, the determination of confidence regions and the derivation of tests of hypotheses for the unknown parameters, in a manner that is simple, intuitive and easy to comprehend. The methods are applied to linear models, in...
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Wiley, 2009. — 368 p. 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...
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N.-Y.: Academic Press, 2014. - 776p. 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 with R, JAGS, and Stan provides an accessible approach to Bayesian data analysis, as material is explained clearly with...
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Wiley, 2012. — 486 p. — 4th ed. — ISBN: 1118332571 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...
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John Wiley & Sons, 2012. — 488 p. 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...
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N.-Y.: Wiley, 2012 - 396p. This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables. Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered...
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Chichester: John Wiley & Sons, 2007. — 460 p. — (Wiley Series in Probability and Statistics). — ISBN 0470024232. Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in...
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Philadelphia: SIAM, 1987. - 91p. A study of those statistical ideas that use a probability distribution over parameter space. The first part describes the axiomatic basis in the concept of coherence and the implications of this for sampling theory statistics. The second part discusses the use of Bayesian ideas in many branches of statistics.
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Springer, 2007. — 375 р. — (Statistics for Social and Behavioral Sciences). — ISBN 978-0-387-71264-2. Introduction to Bayesian Statistics and Estimation for Social Scientists covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers...
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Packt Publishing, 2016. — 282 p. — ISBN 10 1785883801, ISBN 13 978-1785883804. True PDF Simplify the Bayes process for solving complex statistical problems using Python Tutorial guide that will take the you through the journey of Bayesian analysis with the help of sample problems and practice exercises Learn how and when to use Bayesian analysis in your applications with this...
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Hoboken: Wiley, 2016. — 240 p. Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating,...
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New York: Chapman and Hall/CRC, 2015. — 470 p. Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational...
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Springer, 2015. — 193 p. 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...
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A John Wiley & Sons, Inc. , publication. 2009. Bayesian statistical decision theory. WinBUGS. Includes bibliographical references and index. 506 pages. Since the mid- 1980s, the development of widely accessible powerfbl computers and the implementation of Markov chain Monte Carlo (MCMC) methods have led to an explosion of interest in Bayesian statistics and modeling. This was...
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Wiley, 2019. — 501 p. — ISBN 978-1-119-24689-3. A comprehensive resource that offers an introduction to statistics with a Bayesian angle, for students of professional disciplines like engineering and economics The Bayesian Way offers a basic introduction to statistics that emphasizes the Bayesian approach and is designed for use by those studying professional disciplines like...
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Wiley, 2018. — 512 p. — ISBN 1119246873. A comprehensive resource that offers an introduction to statistics with a Bayesian angle, for students of professional disciplines like engineering and economics The Bayesian Way offers a basic introduction to statistics that emphasizes the Bayesian approach and is designed for use by those studying professional disciplines like engineering...
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Springer, 2018. — 306 p. MaxEnt 37, Jarinu, Brazil, July 09–14, 2017. These proceedings from the 37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2017), held in São Carlos, Brazil, aim to expand the available research on Bayesian methods and promote their application in the scientific community. They gather research...
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John Wiley & Sons, 2008. — 430 p. — (Statistics in Practice). — ISBN: 0470060301, 978-0470060308. Bayesian Networks, the result of the convergence of artificial intelligence with statistics, are growing in popularity. Their versatility and modelling power is now employed across a variety of fields for the purposes of analysis, simulation, prediction and diagnosis. This book...
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Издательство InTech, 2012, -124 pp. Over the last decade, a Bayesian network has become a popular representation for encoding uncertain expert knowledge in expert systems. A Bayesian network is a graphical model for probabilistic relationships among a set of variables. It is a graphical model that encodes probabilistic relationships among variables of interest. When used in...
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Wiley, 2003. – 590 p. – ISBN: 0471348430, 9780471348436 – 2nd ed. Shorter, more concise chapters provide flexible coverage of the subject. Expanded coverage includes: uncertainty and randomness, prior distributions, predictivism, estimation, analysis of variance, and classification and imaging. Includes topics not covered in other books, such as the de Finetti Transform....
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Издательство InTech, 2010, -442 pp. Bayesian networks are graphical models that represent the probabilistic relationships among a large number of variables and perform probabilistic inference with those variables. They constitute a formal framework for the representation and communication of decisions resulting from reasoning under uncertainty. Bayesian networks, which were...
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Chapman & Hall, 2003. — 323 p. Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients,...
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Atlantis – 2011, 331 pages. ISBN: 9491216139. Bayesian methods are becoming more and more popular in health sciences, engineering, environmental sciences, business and economics, social sciences, among others. In statistical estimation and analysis of the unknown phenomenon of interest, if we can justify that Bayesian analysis is applicable, we will obtain the best possible...
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John Wiley & Sons, Inc., USA, 2017. — 336 p. — ISBN 1118959019. Presents an introduction to Bayesian statistics, presents an emphasis on Bayesian methods (prior and posterior), Bayes estimation, prediction, MCMC, Bayesian regression, and Bayesian analysis of statistical models of dependence, and features a focus on copulas for risk management. Introduction to Bayesian Estimation...
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London: Guilford, 2017. — 330 p. Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to...
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New York: Packt Publishing, 2017. — 325 p. Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian...
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New York: Wiley-Interscience, 2007. — 400 p. — ISBN 978-0-470-16504-1. The first all-inclusive introduction to modern statistical research methods in the natural resource sciencesThe use of Bayesian statistical analysis has become increasingly important to natural resource scientists as a practical tool for solving various research problems. However, many important contemporary...
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Chapman and Hall/CRC, 2009. — 330 p. — (Chapman & Hall/CRC Biostatistics Series). — ISBN: 142007749X, 9781420077490 Bayesian Missing Data Problems: EM, Data Augmentation and Noniterative Computation presents solutions to missing data problems through explicit or noniterative sampling calculation of Bayesian posteriors. The methods are based on the inverse Bayes formulae...
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Imperial College Press, 2015. — 275 p. — ISBN 1848167563, 9781848167568. This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithms and...
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N.-Y.: Chapman & Hall/CRC, 2015. — 640 p. — ISBN 978-1-4822-3511-1 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,...
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Berlin: Springer, 2016. - 118p. This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Bayesian and frequentist approaches are subjected to a historical, cognitive and epistemological analysis, making it possible to not only compare the two competing theories, but to also find a potential...
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N.-Y.: Springer, 2013. — 718 p. This book provides a balanced, modern summary of Bayesian and frequentist methods for regression analysis. Introduction and Motivating Examples. Frequentist Inference. Bayesian Inference. Hypothesis Testing and Variable Selection. Linear Models. General Regression Models. Binary Data Models. Linear Models. General Regression Models....
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Boca Raton: CRC Press, 2018. — 313 p. Features: Covers a variety of regression models Discusses real case studies Includes R code examples Explains innovative and efficient Bayesian inference Handles complex data Summary: This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through...
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New York: Chapman and Hall/CRC, 2018. — 331 p. Mathematical Theory of Bayesian Statistics introduces the mathematical foundation of Bayesian inference which is well-known to be more accurate in many real-world problems than the maximum likelihood method. Recent research has uncovered several mathematical laws in Bayesian statistics, by which both the generalization loss and the...
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Oxford University Press, 2009. — 250 p. Bayesian nets are widely used in artificial intelligence as a calculus for casual reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover casual relationships. But many philosophers have criticized and ultimately rejected the central assumption on which such work is based-the causal Markov...
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CRC Press, 2012. — 364 p. — ISBN-10: 1439839549. Although the popularity of the Bayesian approach to statistics has been growing for years, many still think of it as somewhat esoteric, not focused on practical issues, or generally too difficult to understand. Bayesian Analysis Made Simple is aimed at those who wish to apply Bayesian methods but either are not experts or do...
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John Wiley & Sons (Asia) Pte Ltd, 2010. — 294 p. — ISBN 978-0-470-82454-2 Bayesian inference is a statistical process that quantifies the degree of belief of hypothesis, events or values of parameters. Many Bayesian methods have been developed in various areas of science and engineering, especially in statistical physics, medical sciences, electrical engineering, and information...
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М.: Финансы и статистика, 1987. — 335 с. В основе книги лежит концепция байесовского использования априорной информации в сочетании с накапливаемыми результатами наблюдений для выработки рациональных решений. Изложенные математические методы используются далее в задачах оценивания долей, средних дисперсий и регрессионных моделей. Кратко рассматриваются системы управлений....
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