Bayesian data analysis /

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Bibliographic Details
Author / Creator:	Gelman, Andrew, author.
Edition:	Third edition.
Imprint:	Boca Raton : CRC Press, [2014] ©2014
Description:	1 online resource (xiv, 661 pages) : illustrations.
Language:	English
Series:	Chapman & Hall/CRC texts in statistical science Texts in statistical science.
Subject:	Bayesian statistical decision theory. Bayesian statistical decision theory
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/13637280

Hidden Bibliographic Details
Varying Form of Title:	BDA3
Other authors / contributors:	Carlin, John B., author. Stern, Hal Steven, author. Dunson, David B., author. Vehtari, Aki, author. Rubin, Donald B., author.
ISBN:	1439898200 9781439898208 9781439840962 1439840962 9781439840955 1439840954
Notes:	Includes bibliographical references (pages 607-639) and indexes. Print version record.
Summary:	"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"--
Other form:	Print version: Gelman, Andrew. Bayesian data analysis. Third edition. Boca Raton : CRC Press, 2014 9781439840955
Standard no.:	40023006895 7448428

Table of Contents:

Part I:
Fundamentals of Bayesian inference.
Probability and inference
Single-parameter models
Introduction to multiparameter models
Asymptotics and connections to non-Bayesian approaches
Hierarchical models Part II: Fundamentals of Bayesian data analysis.
Model checking
Evaluating, comparing, and expanding models
Modeling accounting for data collection
Decision analysis Part III:
Advanced computation.
Introduction to Bayesian computation
Basics of Markov chain simulation
Computationally efficient Markov chain simulation
Modal and distributional approximations Part IV:
Regression models.
Introduction to regression models
Hierarchical linear models
Generalized linear models
Models for robust inference
Models for missing data Part V:
Nonlinear and nonparametric models.
Parametric nonlinear models
Basis function models
Gaussian process models
Finite mixture models
Dirichlet process models
A. Standard probability distributions
B. Outline of proofs of limit theorems
Computation in R and Stan.

Bayesian data analysis /

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