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by Donald B. Rubin, Aki Vehtari, David B. Dunson, Hal S. Stern, John B. Carlin, And
Bayesian Data Analysis, Third Edition, 3rd Edition
Cover
Title Page
Copyright
Contents
Preface
Part I: Fundamentals of Bayesian Inference
1 Probability and inference
2 Single-parameter models
3 Introduction to multiparameter models
4 Asymptotics and connections to non-Bayesian approaches
5 Hierarchical models
Part II: Fundamentals of Bayesian Data Analysis
6 Model checking
7 Evaluating, comparing, and expanding models
8 Modeling accounting for data collection
9 Decision analysis
Part III: Advanced Computation
10 Introduction to Bayesian computation
11 Basics 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
A Standard probability distributions
B Outline of proofs of limit theorems
C Computation in R and Stan
References
Author Index
Subject Index
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