Chapman & Hall, 2022. — 390 p. — ISBN 9781032105710. The complexity of large-scale data sets (“Big Data”) has stimulated the development of advanced computational methods for analysing them. Introduction to Statistical Modelling and Inference covers simple experimental and survey designs, and probability models up to and including generalised linear (regression) models and some...
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 averaging to...
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...
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...
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...
Boca Raton: CRC Press, 2019. — 679 p. As the world becomes increasingly complex, so do the statistical models required to analyse the challenging problems ahead. For the very first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and application of ABC methods. These simple, but powerful...
World Scientific Publishing, 2024. — 380 p. — ISBN 978-981-12-8492-2(eBook). Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves...
World Scientific Publishing, 2024. — 380 p. — eBook ISBN 978-981-12-8492-2. Bayesian analysis is today understood to be an extremely powerful method of statistical analysis, as well an approach to statistics that is particularly transparent and intuitive. It is thus being extensively and increasingly utilized in virtually every area of science and society that involves analysis...
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...
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...
Chapman and Hall/CRC, 2021. — 260 p. — ISBN 9780367350505. Bayesian Approaches in Oncology Using R and OpenBUGS serves two audiences: those who are familiar with the theory and applications of bayesian approach and wish to learn or enhance their skills in R and OpenBUGS, and those who are enrolled in R and OpenBUGS-based course for bayesian approach implementation. For those...
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...
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...
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...
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...
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...
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...
Third Edition. — Hoboken, New Jersey: John Wiley & Sons, 2017. — 602 p. — ISBN: 978-1-118-09315-8. Our original goal for this book was to introduce Bayesian statistics at the earliest possible stage to students with a reasonable mathematical background. This entailed coverage of a similar range of topics as an introductory statistics text, but from a Bayesian perspective. The...
New York: John Wiley & Sons, 1992. — 600 p. Nature of Bayesian inference Standard normal theory inference problems Bayesian assessment of assumptions 1. Effect of non-normality Bayesian assessment of assumptions 2. Comparison of variances Random effect models Analysis of cross classification designs Inference about means with information from more than one source Some aspects...
New York: John Wiley & Sons, 1992. — 600 p. Nature of Bayesian inference Standard normal theory inference problems Bayesian assessment of assumptions 1. Effect of non-normality Bayesian assessment of assumptions 2. Comparison of variances Random effect models Analysis of cross classification designs Inference about means with information from more than one source Some aspects...
Springer, 2022. — 195 p. — (Springer Texts in Statistics). — ISBN 3031098382. Bayes Factors for Forensic Decision Analyses with R provides a self-contained introduction to computational Bayesian statistics using R. With its primary focus on Bayes factors supported by data sets, this book features an operational perspective, practical relevance, and applicability―keeping...
Springer, 2022. — 362 p. — (Springer Texts in Statistics). — ISBN 978-3-031-09839-0. Over the past decades, probabilistic methods have established a firm position as a reference approach for the management of uncertainty in virtually all areas of science, including forensic science, with Bayes' theorem providing the fundamental logical tenet for assessing how new...
Boca Raton: CRC Press, 1985. — 475 p. With Bayesian statistics rapidly becoming accepted as a way to solve applied statisticalproblems, the need for a comprehensive, up-to-date source on the latest advances in thisfield has arisen.Presenting the basic theory of a large variety of linear models from a Bayesian viewpoint,Bayesian Analysis of Linear Models fills this need. Plus,...
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...
New York: CRC Press, 2015. — 568 p. 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...
ITexLi, 2024. — 78 p. — ISBN 1837693552 9781837693559 1837693560 9781837693566 1837693579 9781837693573. This book is an invaluable resource for anyone interested in the intersection of statistics, machine learning, and data science. It offers a unique perspective on Bayesian inference, revealing its potential to provide robust solutions in an increasingly data-driven world....
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...
Pearson Education, Inc., 2023. — 381 p. — ISBN-13 978-0-13-758098-9. Leverage the full power of Bayesian analysis for competitive advantage. Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and...
Pearson Education, Inc., 2023. — 381 p. — ISBN-13 978-0-13-758098-9. Leverage the Full Power of Bayesian Analysis for Competitive Advantage Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and...
Pearson Education, Inc., 2023. — 381 p. — ISBN-13 978-0-13-758098-9. Leverage the Full Power of Bayesian Analysis for Competitive Advantage Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and...
Pearson Education, Inc., 2023. — 192 p. — ISBN-13 978-0-13-758098-9. Leverage the Full Power of Bayesian Analysis for Competitive Advantage Bayesian methods can solve problems you can't reliably handle any other way. Building on your existing Excel analytics skills and experience, Microsoft Excel MVP Conrad Carlberg helps you make the most of Excel's Bayesian capabilities and...
New York: Chapman & Hall, 2000. — 432 p. In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. Building on the first edition of their popular text, Carlin and Louis introduce these methods, demonstrate their usefulness in challenging applied settings, and show how they can be implemented using modern Markov chain Monte Carlo...
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...
MIT Press, 2020. — 472 p. — ISBN: 0262044587, 9780262044585. An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis.This book offers an introduction to the Bayesian approach to statistical inference, with a focus on nonparametric and distribution-free methods. It covers not only...
Springer, 2010. — 636 p. — 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 exhaustively...
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...
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...
2nd Edition. — Wiley & Sons, Ltd, 2006. — 598 p. — (Wiley Series in Probability and Statistics). 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 and...
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...
SPIE Press, 2020. — 360 p. The most basic problem of engineering is the design of optimal operators. Design takes different forms depending on the random process constituting the scientific model and the operator class of interest. This book treats classification, where the underlying random process is a feature-label distribution, and an optimal operator is a Bayes classifier,...
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 that...
Boca Raton: CRC Press, 2023. — 262 p. — (Monographs on Statistics and Applied Probability 173). — ISBN 9780429324222. Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and...
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...
Springer, 2024. — 493 p. — (International Series in Operations Research & Management Science 352). — ISBN 978-3-031-48207-6. This book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions...
The Guilford Press, 2021. — 550 p. — ISBN 978-1462547746. This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social...
Boca Raton: CRC Press, 2000. — 442 p. This volume describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs and containing over 1000 references and equations, Generalized Linear Models...
Elsevier, 2005. — 1062 p. — (Handbook of Statistics 25). — 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...
Oxford: Oxford University Press, 2019. — 429 p. — ISBN: 0198841302. Bayesian statistics is currently undergoing something of a renaissance . At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial...
Oxford: Oxford University Press, 2019. — 430 p. Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on...
2nd edition. — O’Reilly Media, 2021. — 338 p. — ISBN 9781492089469. 2021-05-18: First Release If you know how to program with Python, you’re ready to tackle Bayesian statistics. With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions instead of continuous mathematics. Once you get...
2nd edition. — O’Reilly Media, 2021. — 338 p. — ISBN 9781492089469. 2021-05-18: First Release If you know how to program with Python, you’re ready to tackle Bayesian statistics. With this book, you’ll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions instead of continuous mathematics. Once you get...
2nd Edition. — O’Reilly Media, 2021. — 338 p. — ISBN 978-1-492-08946-9. 2021-05-18: First Release If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the...
2nd Edition. — O’Reilly Media, 2021. — 338 p. — ISBN 978-1-492-08946-9. 2021-05-18: First Release If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics. Once you get the...
Academic Press, 2020. — 292 p. — ISBN: 978-0-12-815862-3. This book is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three...
New York: Routledge, 2025. - 170 p. - ISBN 1032744006. Bayesian Statistics: The Basics provides a comprehensive yet accessible introduction to Bayesian statistics, specifically tailored for any researcher with an interest in statistical methods. It covers the theoretical foundations of Bayesian inference, contrasting it with classical statistical methods like null hypothesis...
Boca Raton: CRC Press, 2018. — 542 p. General Definitions Dominated Experiments Three Remarks on Regular and Dominated Experiments A Remark Regarding the Interpretation of Bayesian Experiments A Remark on Sampling Theory and Bayesian Methods A Remark Regarding So-called "Improper" Prior Distributions Families of Bayesian Experiments Some Examples of Bayesian Experiments...
Springer, 2019. — 210 p. — ISBN: 9811336083. This book first provides a review of various aspects of Bayesian statistics. It then investigates three types of claims reserving models in the Bayesian framework: chain ladder models, basis expansion models involving a tail factor, and multivariate copula models. For the Bayesian inferential methods, this book largely relies on...
New York: Springer, 1997. — 482 p. Like the first two volumes, this third volume of case studies presents detailed applications of Bayesian statistical analysis, emphasizing the sci entific context. The papers were presented and discussed at a workshop at Carnegie Mellon University, October 5-7, 1995. In this volume, which is dedicated to the memory of Morris H. DeGroot,...
New York: Springer, 1993. — 445 p. The past few years have witnessed dramatic advances in computational methods for Bayesian inference. As a result, Bayesian approaches to solving a wide variety of problems in data analysis and decision-making have become feasible, and there is currently a growth spurt in the application of Bayesian methods. The purpose of this volume is to...
New York: Springer, 1995. — 376 p. Like its predecessor, this second volume presents detailed applications of Bayesian statistical analysis, each of which emphasizes the scientific context of the problems it attempts to solve. The emphasis of this volume is on biomedical applications. These papers were presented at a workshop at Carnegie-Mellon University in 1993. Front Matter...
New York: Springer, 1999. — 435 p. The 4th Workshop on Case Studies in Bayesian Statistics was held at the Car negie Mellon University campus on September 27-28, 1997. As in the past, the workshop featured both invited and contributed case studies. The former were presented and discussed in detail while the latter were presented in poster format. This volume contains the four...
New York: Springer, 2002. — 440 p. The 5th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University campus on September 24-25, 1999. As in the past, the workshop featured both invited and contributed case studies. The former were presented and discussed in detail while the latter were presented in poster format. This volume contains the three...
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...
3rd ed. — CRC Press, 2013. — 675 p. — 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 continues to take an applied approach to analysis using up-to-date Bayesian methods. The...
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...
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 and...
Springer, 2006. — 366 p. — 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...
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...
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...
New York: Springer, 1997. — 296 p. Bayesian foundations A noninformative Bayesian approach Extensions of the Polya posterior Empirical Bayes estimation Hierarchical Bayes estimation
New York: Chapman & Hall/CRC, 2019. — 288 p. Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The authors include many examples...
CRC, 2019. — 152 p. — ISBN: 978-0815378648. Solutions manual only! Bayesian Statistical Methods provides data scientists with the foundational and computational tools needed to carry out a Bayesian analysis. This book focuses on Bayesian methods applied routinely in practice including multiple linear regression, mixed effects models and generalized linear models (GLM). The...
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...
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...
New York: Springer, 2008. — 445 p. Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based...
Amazon Digital Services LLC, 2016. — 56 p. From spam filters, to Netflix recommendations, to drug testing, Bayes Theorem (also known as Bayes Theory, Bayes Rule or Bayes Formula) is used through a huge number of industries. The reason it is so useful is it provides a systematic way to update estimated probability as new data is found out. Bayesian data analysis is taught in many...
New York: Springer, 2021. — 176 p. These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian...
Cambridge: Cambridge University Press, 2010. — 308 p. 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 picture? Further topics Challenges and future...
CRC Press, Taylor & Francis Group, LLC., 2020. — 461 p. — ISBN: 9780367428150. The Equation of Knowledge: From Bayes' Rule to a Unified Philosophy of Science introduces readers to the Bayesian approach to science: teasing out the link between probability and knowledge. The author strives to make this book accessible to a very broad audience, suitable for professionals,...
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...
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...
Boca Raton: CRC Press/Chapman & Hall, 2022. — 544 p. An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students...
2nd.ed. — CRC, 2021. — xxvi, 498 p. — (Texts in Statistical Science Series). — ISBN 978-1-1380-5273-4, 978-1-3151-6756-5. Praise for the first edition: Principles of Uncertainty is a profound and mesmerising book on the foundations and principles of subjectivist or behaviouristic Bayesian analysis. … the book is a pleasure to read. And highly recommended for teaching as it can...
NY.: Chapman and Hall/CRC, 2011. — 499 p. — (Texts in Statistical Science). — ISBN: 978-1-4398-6162-2. An intuitive and mathematical introduction to subjective probability and Bayesian statistics. An accessible, comprehensive guide to the theory of Bayesian statistics, Principles of Uncertainty presents the subjective Bayesian approach, which has played a pivotal role in game...
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...
2nd Edition. — Guilford Press, 2024. — xxii, 250 p. — (Methodology in The Social Sciences). — ISBN 9781462553549. The second edition of this practical book equips social science researchers to apply the latest Bayesian methodologies to their data analysis problems. It includes new chapters on model uncertainty, Bayesian variable selection and sparsity, and Bayesian workflow for...
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...
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...
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...
New York: Academic Press, 2014. — 776 p. 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...
Sage, 2018. — 682 p. Publisher Note Title Page Copyright Page Acknowledgements Online resources Acknowledgements About the Author How Best to use this Book An Introduction to Bayesian Inference The Subjective Worlds of Frequentist and Bayesian Statistics Probability – The Nuts and Bolts of Bayesian Inference Understanding the Bayesian Formula Likelihoods Priors The Devil is in...
Boca Raton: CRC Press/Chapman & Hall, 2021. — 622 p. Praise for Bayesian Thinking in Biostatistics: "This thoroughly modern Bayesian book …is a 'must have' as a textbook or a reference volume. Rosner, Laud and Johnson make the case for Bayesian approaches by melding clear exposition on methodology with serious attention to a broad array of illuminating applications. These are...
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...
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...
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...
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...
Cambridge: Cambridge University Press, 1999. — 346 p. This exposition of the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students is unique in presenting its subject with a practical flavor and an emphasis on mainstream statistics. It shows how to infer scientific, medical, and social conclusions from numerical data. The authors...
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.
Wiley, 2019. — 308 p. — (Quality & Reliability Engineering). — ISBN 9781119287971. Demonstrates how to solve reliability problems using practical applications of Bayesian models This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches...
Wiley, 2019. — 308 p. — (Quality & Reliability Engineering). — ISBN: 978-1-119-28797-1. Demonstrates how to solve reliability problems using practical applications of Bayesian models This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches...
Sage Publications, 2022. — 217 p. Bayesian statistical analyses have become increasingly common over the last two decades. The rapid increase in computing power that facilitated their implementation coincided with major changes in the research interests of, and data availability for, social scientists. Specifically, the last two decades have seen an increase in the availability...
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...
Without authors. — The Open University, 2007. — 36 p. M249 Practical modern statistics uses the software packages IBM SPSS Statistics (SPSS Inc.) and Win BUGS, and other software. This software is provided as part of the module, and its use is covered in the Introduction to statistical modeling and in the four computer books associated with Books 1 to 4. This publication forms...
3rd ed. — Birmingham: Packt Publishing, 2024. — 392 p. — ISBN 1805127160. Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these libraries. Key Features Conduct Bayesian data analysis with step-by-step guidance Gain insight into a modern,...
Packt Publishing, 2016. — 282 p. — ISBN10: 1785883801, ISBN13: 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...
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,...
Springer, 2023. — 394 p. — ISBN 9811947546. This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes...
Springer, 2023. — 395 p. — ISBN 978-981-19-4754-4. This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that...
2nd. ed. — Boca Raton: CRC Press, 2020. — 611 p. — (Chapman & Hall/CRC Texts in Statistical Science). — ISBN: 036713991X. Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step...
2nd. ed. — Boca Raton: CRC Press, 2020. — 611 p. — (Chapman & Hall/CRC Texts in Statistical Science). — ISBN: 036713991X. Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step...
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...
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...
New York: Springer, 1999. — 405 p. This volume presents an overview of Bayesian methods for inference in the wavelet domain. The papers in this volume are divided into six parts: The first two papers introduce basic concepts. Chapters in Part II explore different approaches to prior modeling, using independent priors. Papers in the Part III discuss decision theoretic aspects of...
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...
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...
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...
Springer, 2024. — 237 p. This book offers a comprehensive overview of statistical methodology for modelling and evaluating spatial variables useful in a variety of applications. These spatial variables fall into three categories: continuous, like terrain elevation; events, like tree locations; and mosaics, like medical images. Definitions and discussions of random field models...
New York: Thomas Y. Crowell Company, 1975. — 455 p. Over the past few years I have been encouraged by colleagues and students who knew of my interest in Bayesian statistics to write a book that would explain the Bayesian approach in reasonably simple language, and would serve as a practical guide to carrying out Bayesian analyses. This book is the result. It attempts to...
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...
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...
InTech, 2012. — 124 p. 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 conjunction with...
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. Author...
ANU Press, 2017. — 697 p. — ISBN: 9781921934254. Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical models, Markov chain Monte Carlo...
InTech, 2010. — 442 p. 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 named after...
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,...
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...
New York: Springer, 2021. — 475 p. Design Research uses scientific methods to evaluate designs and build design theories. This book starts with recognizable questions in Design Research, such as A/B testing, how users learn to operate a device and why computer-generated faces are eerie. Using a broad range of examples, efficient research designs are presented together with...
2nd. ed. — CRC Press, 2021. — 252 p. — ISBN 0367366517. Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and...
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...
SAGE Publications Ltd., 2022. — 272 p. — ISBN 978-1-5297-6861-9. This book walks you through learning probability and statistics from a Bayesian point of view. From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with...
SAGE Publications Ltd., 2022. — 272 p. — ISBN 978-1-5297-6861-9. This book walks you through learning probability and statistics from a Bayesian point of view. From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with...
SAGE Publications Ltd., 2022. — 272 p. — ISBN 978-1-5297-6861-9. This book walks you through learning probability and statistics from a Bayesian point of view. From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with...
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...
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...
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...
New York: Sebtel Press, 2016. — 187 p. Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical...
Springer Singapore, 2023. — 239 p. — eBook ISBN: 978-981-99-3838-4. Focuses on widely applicable information criterion (WAIC) & widely applicable Bayesian information criterion (WBIC) Presents 100 carefully selected exercises accompanied by solutions in the main text Contains detailed source programs and Stan codes to enhance readers’ grasp of the mathematical concepts...
Springer Singapore, 2023. — 239 p. — eBook ISBN: 978-981-99-3838-4. Focuses on widely applicable information criterion (WAIC) & widely applicable Bayesian information criterion (WBIC) Presents 100 carefully selected exercises accompanied by solutions in the main text Contains detailed source programs and Stan codes to enhance readers’ grasp of the mathematical concepts...
New York: Chapman and Hall/CRC, 2022. — 491 p. Bayesian variable selection has experienced substantial developments over the past 30 years with the proliferation of large data sets. Identifying relevant variables to include in a model allows simpler interpretation, avoids overfitting and multicollinearity, and can provide insights into the mechanisms underlying an observed...
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...
ITexLi, 2022. — 109 p. — ISBN 1803560452, 9781803560458, 1803560444, 9781803560441, 1803560460, 9781803560465. With growing interest in data mining and its merits, including the incorporation of historical or experiential information into statistical analysis, Bayesian inference has become an important tool for analyzing complicated data and solving inverse problems in various...
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...
New York: Springer, 2021. — 503 p. Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models. The celebrated Kalman filter, with its numerous extensions, takes centre stage in the book. Univariate and multivariate models, linear Gaussian, non-linear and non-Gaussian...
Cambridge University Press, 2019. — 257 p. — ISBN: 978-1-108-48103-8. Meaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive...
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...
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...
Springer, 2020. — 209 p. — ISBN: 978-3-030-55897-0. This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show readers: Bayesian thinking isn’t difficult and can be used in virtually every kind of research. In addition to revealing the underlying simplicity of statistical methods, the book explains how to parameterise and compare models...
2nd edition. — Springer, 2024. — 261 p. This book describes how Bayesian methods work. Aiming to demystify the approach, it explains how to parameterize and compare models while accounting for uncertainties in data, model parameters and model structures. Bayesian thinking is not difficult and can be used in virtually every kind of research. How exactly should data be used in...
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....
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 This book addresses the applications of extensively used regression models under a Bayesian framework. It emphasizes efficient Bayesian inference through integrated...
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...
No Starch Press, 2019. — 258 p. — ISBN: 978-1-59327-956-1. Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples. Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it.Bayesian Statistics the Fun...
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...
CRC Press, 2012. — 364 p. — ISBN10: 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 not...
Chapman and Hall/CRC, 2023. — 234 p. — (Chapman & Hall/CRC Biostatistics Series). — ISBN 978-0-367-14624-5. Bayesian adaptive designs provide a critical approach to improve the efficiency and success of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they form the basis for the...
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...
Amsterdam: North-Holland, 1980. — 485 p. The main objective of this volume is to honour Sir Harold Jeffreys for the major theoretical and applied contributions he made to Bayesian analysis. Leading experts in the field, such as S. Geisser, I.J. Good and D.V. Lindley review and comment on these contributions.
Boca Raton: CRC Press, 2024. — 347 p. Preface Introduction Bayesian Modelling Statistical Model Bayes Model Advantages Sequential Analysis Big Data Hierarchical Models List of Problems Choice of Prior Subjective Priors Conjugate Priors Non-informative Priors Laplace Prior Jeffreys Prior Reference Priors List of Problems Decision Theory Basics of Decision Theory Bayesian...
Пер. с анг. В. А. Яроцкого. — М.: ДМК Пресс, 2018. — 183 с.: ил. — ISBN: 978-5-97060-664-3. Если вы знаете, как программировать на Python, и немного знаете о теории вероятности, значит, вы готовы освоить байесовскую статистику. Эта книга расскажет вам, как решать статистические задачи с помощью языка Python вместо математических формул и использовать дискретные вероятностные...
Пер. с анг. В. А. Яроцкого. — М.: ДМК Пресс, 2018. — 184 с.: ил. — ISBN: 978-5-97060-664-3. Если вы знаете, как программировать на Python, и немного знаете о теории вероятности, значит, вы готовы освоить байесовскую статистику. Эта книга расскажет вам, как решать статистические задачи с помощью языка Python вместо математических формул и использовать дискретные вероятностные...
СПб.: Питер, 2021. — 304 с. — (Библиотека программиста). Нужно решить конкретную задачу, а перед вами куча непонятных данных, в которой черт ногу сломит? «Байесовская статистика» расскажет, как принимать правильные решения, задействуя свою интуицию и простую математику. Пора забыть про заумные и занудные университетские лекции! Эта книга даст вам полное понимание байесовской...
СПб.: Питер, 2021. — 304 с. — (Библиотека программиста). Нужно решить конкретную задачу, а перед вами куча непонятных данных, в которой черт ногу сломит? «Байесовская статистика» расскажет, как принимать правильные решения, задействуя свою интуицию и простую математику. Пора забыть про заумные и занудные университетские лекции! Эта книга даст вам полное понимание байесовской...
Пер. с англ. А. Павлов. — СПб.: Питер, 2021. — 304 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-1655-3. Нужно решить конкретную задачу, а перед вами куча непонятных данных, в которой черт ногу сломит? «Байесовская статистика» расскажет, как принимать правильные решения, задействуя свою интуицию и простую математику. Пора забыть про заумные и занудные университетские...
Пер. с англ. А. Павлов. — СПб.: Питер, 2021. — 304 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-1655-3. Нужно решить конкретную задачу, а перед вами куча непонятных данных, в которой черт ногу сломит? «Байесовская статистика» расскажет, как принимать правильные решения, задействуя свою интуицию и простую математику. Пора забыть про заумные и занудные университетские...
Пер. с англ. А. Павлов. — СПб.: Питер, 2021. — 304 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-1655-3. Нужно решить конкретную задачу, а перед вами куча непонятных данных, в которой черт ногу сломит? «Байесовская статистика» расскажет, как принимать правильные решения, задействуя свою интуицию и простую математику. Пора забыть про заумные и занудные университетские...
Пер. с англ. А. Павлов. — СПб.: Питер, 2021. — 304 с.: ил. — (Библиотека программиста). — ISBN 978-5-4461-1655-3. Нужно решить конкретную задачу, а перед вами куча непонятных данных, в которой черт ногу сломит? «Байесовская статистика» расскажет, как принимать правильные решения, задействуя свою интуицию и простую математику. Пора забыть про заумные и занудные университетские...
Москва: Наука, Главная редакция физико-математической литературы, 1989. — 328 с. — ISBN 5020141038. Описывается современное состояние прикладной теории байесовского статистического оценивания. Особое внимание уделяется непараметрическим методам и способам выбора априорного распределения. Исследуется ряд новых типов байесовских оценок: квазипараметрические оценки, байесовские...
Учебное пособие. — Перевод с англ. А.А. Рывкина. — М.: Финансы и статистика, 1987. — 335 с. В современном статистическом анализе существует два подхода: частотный и байесовский. Согласно байесовской теории, случайность определяется как мера нашего незнания, и такая интерпретация возможна почти у каждого случайного процесса. Книга об альтернативном методе в статистике и теории...
Учебное пособие. — Перевод с англ. А.А. Рывкина. — М.: Финансы и статистика, 1987. — 335 с. — (Библиотечка иностранных книг для экономистов и статистиков). 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...
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