Bayesian Data Analysis, Third Edition
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.
Examples of real statistical analyses appear throughout the book, and we hope thereby to give an applied flavor to the entire development. Indeed, given the conceptual simplicity of the Bayesian approach, it is only in the intricacy of specific applications that novelty arises. Non-Bayesian approaches dominated statistical theory and practice for most of the last century, but the last few decades have seen a re-emergence of Bayesian methods. This has been driven more by the availability of new computational techniques than by what many would see as the theoretical and logical advantages of Bayesian thinking.
In our treatment of Bayesian inference, we focus on practice rather than philosophy. We demonstrate our attitudes via examples that have arisen in the applied research of ourselves and others. Chapter 1 presents our views on the foundations of probability as empirical and measurable; see in particular Sections 1.4–1.7.
Changes for the third edition
The biggest change for this new edition is the addition of Chapters 20–23 on nonparametric modeling. Other major changes include weakly informative priors in Chapters 2, 5, and elsewhere; boundary-avoiding priors in Chapter 13; an updated discussion of cross validation and predictive information criteria in the new Chapter 7; improved convergence monitoring and effective sample size calculations for iterative simulation in Chapter 11; presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation in Chapters 12 and 13; and new and revised code in Appendix C. We have made other changes throughout. During the eighteen years since completing the first edition of Bayesian Data Analysis, we have worked on dozens of interesting applications which, for reasons of space, we are not able to add to this new edition. Many of these examples appear in our book, Data Analysis Using Regression and Hierarchical/Multilevel Models, as well as in our published research articles.
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