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 critiques statistical analysis from a Bayesian perspective. Changes in the new edition include: added material on how Bayesian methods are connected to other approaches, stronger focus on MCMC, added chapter on further computation topics, more examples, and additional chapters on current models for Bayesian data analysis such as equation models, generalized linear mixed models, and more. The book is an introductory text and a reference for working scientists throughout their professional life. "Preface This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics. The mathematics used in our book is basic probability and statistics, elementary calculus, and linear algebra. A review of probability notation is given in Chapter 1 along with a more detailed list of topics assumed to have been studied. The practical orientation of the book means that the reader's previous experience in probability, statistics, and linear algebra should ideally have included strong computational components. To write an introductory text alone would leave many readers with only a taste of the conceptual elements but no guidance for venturing into genuine practical applications, beyond those where Bayesian methods agree essentially with standard non-Bayesian analyses. On the other hand, we feel it would be a mistake to present the advanced methods without first introducing the basic concepts from our data-analytic perspective. Furthermore, due to the nature of applied statistics, a text on current Bayesian methodology would be incomplete without a variety of worked examples drawn from real applications. To avoid cluttering the main narrative, there are bibliographic notes at the end of each chapter and references at the end of the book"-- Provided by publisher Descripción del editor: "Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow 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 codeThe 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." (CRC Press)
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 guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:
Stronger focus on MCMC
Revision of the computational advice in Part III
New chapters on nonlinear models and decision analysis
Several additional applied examples from the authors' recent research
Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more
Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.
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 analysis from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analysis, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include: stronger focus on MCMC; revision of the computational advice in Part III; new chapters on nonlinear models and decision analysis; several additional applied examples from the authors' recent research; additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more; and, reorganization of chapters 6 and 7 on model checking and data collection. Bayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make "Bayesian Data Analysis" an excellent introductory text and a reference that working scientists will use throughout their professional life The Second Edition Of Bayesian Data Analysis Continues To Emphasize Practice Over Theory, Clearly Describing How To Conceptualize, Perform, And Critique Statistical Analyses From A Bayesian Perspective. Its World-class Authors Provide Detailed Guidance On All Aspects Of Bayesian Data Analysis And Include Many Examples Of Real Statistical Analyses, Based On Their Own Research. Part I: Fundamentals Of Bayesian Inference -- Background -- Single-parameter Models -- Introduction To Multiparameter Models -- Large-sample Inference And Frequency Properties Of Bayesian Inference -- Part Ii: Fundamentals Of Bayesian Data Analysis -- Hierarchical Models -- Model Checking And Improvement -- Modeling Accounting For Data Collection -- Connections And Challenges -- General Advice -- Part Iii: Advanced Computation -- Overview Of Computation -- Posterior Simulation -- Approximations Based On Posterior Modes -- Special Topics In Computation -- Part Iv: Regression Models -- Introduction To Regression Models -- Hierarchical Linear Models -- Generalized Linear Models -- Models For Robust Inference -- Part V: Specific Models And Problems -- Mixture Models -- Multivariate Models -- Nonlinear Models -- Models For Missing Data -- Decision Analysis -- Appendixes. Standard Probability Distributions -- Outline Of Proofs Of Asymptotic Theorems -- Example Of Computation In R And Bugs. Andrew Gelman ... [et Al.]. Includes Bibliographical References (p. 611-646) And Indexes. "Bayesian Data Analysis is a comprehensive treatment of the statistical analysis of data from a Bayesian perspective. Modern computational tools are emphasized, and inferences are typically obtained using computer simulations.". "The principles of Bayesian analysis are described with an emphasis on practical rather than theoretical issues, and illustrated using actual data. A variety of models are considered, including linear regression, hierarchical (random effects) models, robust models, generalized linear models and mixture models.". "Two important and unique features of this text are thorough discussions of the methods for checking Bayesian models and the role of the design of data collection in influencing Bayesian statistical analysis." "Issues of data collection, model formulation, computation, model checking and sensitivity analysis are all considered. The student or practising statistician will find that there is guidance on all aspects of Bayesian data analysis."--BOOK JACKET. 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 critiques statistical analysis from a Bayesian perspective. Changes in the new edition include: added material on how Bayesian methods are connected to other approaches, stronger focus on MCMC, a chapter on further computation topics, more examples, and additional chapters on current models for Bayesian data analysis such as equation models amd generalized linear mixed models. The book is an i By Bayesian data analysis, we mean practical methods for making inferences from data using probability models for quantities we observe and for quantities about which we wish to learn.
