Bayesian data analysis /
"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 han...
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| Main Authors: | , , , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton :
CRC Press,
[2014]
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| Edition: | Third edition. |
| Series: | Texts in statistical science.
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| Subjects: | |
| Online Access: | CONNECT |
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.
