Bayesian Data Analysis, Third Edition, 3rd Edition

Table of contents

1. Cover
2. Title Page
3. Copyright
4. Contents
5. Preface
6. Part I: Fundamentals of Bayesian Inference
   1. 1 Probability and inference
   2. 2 Single-parameter models
   3. 3 Introduction to multiparameter models
   4. 4 Asymptotics and connections to non-Bayesian approaches
   5. 5 Hierarchical models
7. Part II: Fundamentals of Bayesian Data Analysis
   1. 6 Model checking
   2. 7 Evaluating, comparing, and expanding models
   3. 8 Modeling accounting for data collection
   4. 9 Decision analysis
8. Part III: Advanced Computation
   1. 10 Introduction to Bayesian computation
   2. 11 Basics of Markov chain simulation
   3. 12 Computationally efficient Markov chain simulation
   4. 13 Modal and distributional approximations
9. Part IV: Regression Models
   1. 14 Introduction to regression models
   2. 15 Hierarchical linear models
   3. 16 Generalized linear models
   4. 17 Models for robust inference
   5. 18 Models for missing data
10. Part V: Nonlinear and Nonparametric Models
    1. 19 Parametric nonlinear models
    2. 20 Basis function models
    3. 21 Gaussian process models
    4. 22 Finite mixture models
    5. 23 Dirichlet process models
11. A Standard probability distributions
12. B Outline of proofs of limit theorems
13. C Computation in R and Stan
14. References
15. Author Index
16. Subject Index