Includes bibliographical references (pages 607-639) and indexes.
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.
Reproduction
Electronic reproduction. Perth, W.A. Available via World Wide Web.