Bayesian data analysis / Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin.

Format:

Book

Author/Creator:

Gelman, Andrew, author.

Contributor:

Clarence J. Marshall Memorial Library Fund.

ProQuest ebook central.

Series:

Texts in statistical science

Chapman & Hall/CRC texts in statistical science

Language:

English

Subjects (All):

Bayesian statistical decision theory.

Physical Description:

1 online resource (xiv, 667 pages) : illustrations.

Edition:

Third edition.

Other Title:

BDA3

Place of Publication:

Boca Raton : CRC Press, 2014.

System Details:

text file

Summary:

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 Suitable for both students and researchers, the book introduces Bayesian inference starting from first principles, presents effective current approaches to Bayesian modeling and computation in statistics and related fields, and provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, are available on the book's web page. Book jacket.

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.

Notes:

Includes bibliographical references (pages 607-639) and indexes.

Electronic reproduction. Ann Arbor, MI Available via World Wide Web.

Description based on print version record.

Local Notes:

Acquired for the Penn Libraries with assistance from the Clarence J. Marshall Memorial Library Fund.

ISBN:

9781439898208

1439898200

Publisher Number:

99978497092

7448428

Access Restriction:

Restricted for use by site license.