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Author : J. M. Bernardo
Publisher : Oxford University Press
Page : 886 pages
File Size : 52,8 Mb
Release : 1999-08-12
Category : Mathematics
ISBN : 0198504853
Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.
Author : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publisher : CRC Press
Page : 677 pages
File Size : 55,7 Mb
Release : 2013-11-01
Category : Mathematics
ISBN : 9781439840955
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 authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Author : Constantine Gatsonis,Robert E. Kass,Alicia Carriquiry,Andrew Gelman,David Higdon,Donna K. Pauler,Isabella Verdinelli
Publisher : Springer
Page : 384 pages
File Size : 54,9 Mb
Release : 2018-08-17
Category : Mathematics
ISBN : 9781461220787
This volume contains invited case studies with the accompanying discussion as well as contributed papers selected by a refereeing process of 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001.
Author : Peter D. Hoff
Publisher : Springer Science & Business Media
Page : 271 pages
File Size : 53,5 Mb
Release : 2009-06-02
Category : Mathematics
ISBN : 9780387924076
A self-contained introduction to probability, exchangeability and Bayes’ rule provides a theoretical understanding of the applied material. Numerous examples with R-code that can be run "as-is" allow the reader to perform the data analyses themselves. The development of Monte Carlo and Markov chain Monte Carlo methods in the context of data analysis examples provides motivation for these computational methods.
Author : P. K. Goel,Prem K. Goel,Arnold Zellner
Publisher : North Holland
Page : 512 pages
File Size : 46,8 Mb
Release : 1986
Category : Business & Economics
ISBN : MINN:319510013920610
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 from several countries. Part I of this book relates most directly to de Finetti's interests whilst Part II deals specifically with the implications of the assumption of finitely additive probability. Parts III & IV discuss applications of Bayesian methodology in econometrics and economic forecasting, and Part V examines assessment of prior parameters in specific parametric setting and foundational issues in probability assessment. The following section deals with state of the art for comparing probability functions and gives an assessment of prior distributions and utility functions. In Parts VII & VIII are a collection of papers on Bayesian methodology for general linear models and time series analysis (the most often used tools in economic modelling), and papers relevant to modelling and forecasting. The remaining two Parts examine, respectively, optimality considerations and the effectiveness of the Conditionality-Likelihood Principle as a vehicle to convince the non-Bayesians about the usefulness of the Bayesian paradigm.
Author : Therese M. Donovan,Ruth M. Mickey
Publisher : Oxford University Press, USA
Page : 430 pages
File Size : 53,8 Mb
Release : 2019
Category : Mathematics
ISBN : 9780198841296
This is an entry-level book on Bayesian statistics written in a casual, and conversational tone. The authors walk a reader through many sample problems step-by-step to provide those with little background in math or statistics with the vocabulary, notation, and understanding of the calculations used in many Bayesian problems.
Author : J. M. Bernardo
Publisher : Unknown
Page : 0 pages
File Size : 53,9 Mb
Release : 2023
Category : Bayesian statistical decision theory
ISBN : 1383035342
Bayesian statistics is a dynamic and fast-growing area of statistical research and the Valencia International Meetings provide the main forum for discussion. These resulting proceedings form an up-to-date collection of research.
Author : J. M. Bernardo
Publisher : Unknown
Page : 786 pages
File Size : 48,8 Mb
Release : 1985
Category : Bayesian statistical decision theory
ISBN : UOM:39015046280973
Author : Ming-Hui Chen,Peter Müller,Dongchu Sun,Keying Ye,Dipak K. Dey
Publisher : Springer Science & Business Media
Page : 631 pages
File Size : 41,7 Mb
Release : 2010-07-24
Category : Mathematics
ISBN : 9781441969446
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 cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Author : P. Sander,R. Badoux
Publisher : Springer Science & Business Media
Page : 244 pages
File Size : 52,8 Mb
Release : 1991
Category : Mathematics
ISBN : UCAL:B4527922
1. Introduction to Bayesian Methods in Reliability.- 1. Why Bayesian Methods?.- 1.1 Sparse data.- 1.2 Decision problems.- 2. Bayes' Theorem.- 3. Examples from a Safety Study on Gas transmission Pipelines.- 3.1 Estimating the probability of the development of a big hole.- 3.2 Estimating the leak rate of a gas transmission pipeline.- 4. Conclusions.- References.- 2. An Overview of the Bayesian Approach.- 1. Background.- 2. Probability Concepts.- 3. Notation.- 4. Reliability Concepts and Models.- 5. Forms of Data.- 6. Statistical Problems.- 7. Review of Non-Bayesian Statistical Methods.- 8. Desiderata for Decision-Oriented Statistical Methodology.- 9. Decision-Making.- 10. Degrees of Belief as Probabilities.- 11. Bayesian Statistical Philosophy.- 12. A Simple Illustration of Bayesian Learning.- 13. Bayesian Approaches to Typical Statistical Questions.- 14. Assessment of Prior Densities.- 15. Bayesian Inference for some Univariate Probability Models.- 16. Approximate Analysis under Great Prior Uncertainty.- 17. Problems Involving many Parameters: Empirical Bayes.- 18. Numerical Methods for Practical Bayesian Statistics.- References.- 3. Reliability Modelling and Estimation.- 1. Non-Repairable Systems.- 1.1 Introduction.- 1.2 Describing reliability.- 1.3 Failure time distributions.- 2. Estimation.- 2.1 Introduction.- 2.2 Classical methods.- 2.3 Bayesian methods.- 3. Reliability estimation.- 3.1 Introduction.- 3.2 Binomial sampling.- 3.3 Pascal sampling.- 3.4 Poisson sampling.- 3.5 Hazard rate estimation.- References.- 4. Repairable Systems and Growth Models.- 1. Introduction.- 2. Good as New: the Renewal Process.- 3. Estimation.- 4. The Poisson Process.- 5. Bad as old: the Non-Homogeneous Poisson Process.- 6. Classical Estimation.- 7. Exploratory Analysis.- 8. The Duane Model.- 9. Bayesian Analysis.- References.- 5. The Use of Expert Judgement in Risk Assessment.- 1. Introduction.- 2. Independence Preservation.- 3. The Quality of Experts' Judgement.- 4. Calibration Sets and Seed Variables.- 5. A Classical Model.- 6. Bayesian Models.- 7. Some Experimental Results.- References.- 6. Forecasting Software Reliability.- 1. Introduction.- 2. The Software Reliability Growth Problem.- 3. Some Software Reliability Growth Models.- 3.1 Jelinski and Moranda (JM).- 3.2 Bayesian Jelinski-Moranda (BJM).- 3.3 Littlewood (L).- 3.4 Littlewood and Verrall (LV).- 3.5 Keiller and Littlewood (KL).- 3.6 Weibull order statistics (W).- 3.7 Duane (D).- 3.8 Goel-Okumoto (GO).- 3.9 Littlewood NHPP (LNHPP).- 4. Examples of Use.- 5. Analysis of Predictive Quality.- 5.1 The u-plot.- 5.2 The y-plot, and scatter plot of u's.- 5.3 Measures of 'noise'.- 5.3.1 Braun statistic.- 5.3.2 Median variability.- 5.3.3 Rate variability.- 5.4 Prequential likelihood.- 6. Examples of Predictive Analysis.- 7. Adapting and Combining Predictions; Future Directions.- 8 Summary and Conclusions.- Acknowledgements.- References.- References.- Author index.
Author : David Kaplan
Publisher : Guilford Publications
Page : 275 pages
File Size : 43,7 Mb
Release : 2023-10-02
Category : Social Science
ISBN : 9781462553556
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 statistical modeling. Clearly explaining frequentist and epistemic probability and prior distributions, the second edition emphasizes use of the open-source RStan software package. The text covers Hamiltonian Monte Carlo, Bayesian linear regression and generalized linear models, model evaluation and comparison, multilevel modeling, models for continuous and categorical latent variables, missing data, and more. Concepts are fully illustrated with worked-through examples from large-scale educational and social science databases, such as the Program for International Student Assessment and the Early Childhood Longitudinal Study. Annotated RStan code appears in screened boxes; the companion website (www.guilford.com/kaplan-materials) provides data sets and code for the book's examples. New to This Edition *Utilizes the R interface to Stan--faster and more stable than previously available Bayesian software--for most of the applications discussed. *Coverage of Hamiltonian MC; Cromwell’s rule; Jeffreys' prior; the LKJ prior for correlation matrices; model evaluation and model comparison, with a critique of the Bayesian information criterion; variational Bayes as an alternative to Markov chain Monte Carlo (MCMC) sampling; and other new topics. *Chapters on Bayesian variable selection and sparsity, model uncertainty and model averaging, and Bayesian workflow for statistical modeling.
Author : Borek Puza
Publisher : ANU Press
Page : 698 pages
File Size : 43,9 Mb
Release : 2015-10-01
Category : Mathematics
ISBN : 9781921934261
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 methods, finite population inference, biased sampling and nonignorable nonresponse. The book contains many exercises, all with worked solutions, including complete computer code. It is suitable for self-study or a semester-long course, with three hours of lectures and one tutorial per week for 13 weeks.
Author : Ben Lambert
Publisher : SAGE
Page : 744 pages
File Size : 43,8 Mb
Release : 2018-04-20
Category : Social Science
ISBN : 9781526418265
Supported by a wealth of learning features, exercises, and visual elements as well as online video tutorials and interactive simulations, this book is the first student-focused introduction to Bayesian statistics. Without sacrificing technical integrity for the sake of simplicity, the author draws upon accessible, student-friendly language to provide approachable instruction perfectly aimed at statistics and Bayesian newcomers. Through a logical structure that introduces and builds upon key concepts in a gradual way and slowly acclimatizes students to using R and Stan software, the book covers: An introduction to probability and Bayesian inference Understanding Bayes′ rule Nuts and bolts of Bayesian analytic methods Computational Bayes and real-world Bayesian analysis Regression analysis and hierarchical methods This unique guide will help students develop the statistical confidence and skills to put the Bayesian formula into practice, from the basic concepts of statistical inference to complex applications of analyses.
Author : Will Kurt
Publisher : No Starch Press
Page : 258 pages
File Size : 51,8 Mb
Release : 2019-07-09
Category : Mathematics
ISBN : 9781593279561
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 Way will change that. This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples. By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to: - How to measure your own level of uncertainty in a conclusion or belief - Calculate Bayes theorem and understand what it's useful for - Find the posterior, likelihood, and prior to check the accuracy of your conclusions - Calculate distributions to see the range of your data - Compare hypotheses and draw reliable conclusions from them Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.
Author : D. V. Lindley
Publisher : SIAM
Page : 88 pages
File Size : 40,7 Mb
Release : 1972-01-31
Category : Mathematics
ISBN : 1611970652
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.
