Bayesian Data Analysis,Third Edition (Chapman & Hall/CRC Texts in Statistical Science)
by: Andrew Gelman – John B. Carlin – Hal S. Stern – David B. Dunson – Aki Vehtari – Donald B. Rubin
ISBN-10: 1439840954
ISBN-13: 9781439840955
Edition 版本: 3
Released: 2013-11-01
Pages: 675 )
Book Description
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.
Contents
Preface
Part l: Fundamentals of Bayesian Inference
1 Probability and Inference
2 Single-parameter Models
3Introduction to Multiparameter Models
4 Asymptotics and Connections to non-Bayesian
Approaches
5 Hierarchical Models
Part Il: Fundamentals of Bayesian Data Analysis
6 Model Checking
7 Evaluating,Comparing,and Expanding Models
8 Modeling Accounting for Data Colection
9 Decision Analysis
Part lIl: Advanced Computation
10 Introduction to Bayesian Computation
11Basics of Markov Chain Simulation
12 Computationally Efficient Markov Chain Simulation
13 Modal and Distributional Approximations
Part IV: Regression Models
14 Introduction to Regression Models
15 Hierarchical Linear Models
16 Generalized Linear Models
17 Models for Robust Inference
18 Models for Missing Data
Part V: Nonlinear and Nonparametric Models
19 Parametric Nonlinear Models
20 Basis Function Models
21 Gaussian Process Models
22 Finite Mixture Models
23 Dirichlet Process Models
