Winner of the 2016 De Groot Prize from the International Society for Bayesian AnalysisNow 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

part |2 pages

Part I: Fundamentals of Bayesian Inference

chapter 1|26 pages

Probability and inference

chapter 2|34 pages

Single-parameter models

chapter 3|20 pages

Introduction to multiparameter models

chapter 5|38 pages

Hierarchical models

part |2 pages

Part II: Fundamentals of Bayesian Data Analysis

chapter 6|24 pages

Model checking

chapter 8|40 pages

Modeling accounting for data collection

chapter 9|22 pages

Decision analysis

part |2 pages

Part III: Advanced Computation

chapter 10|14 pages

Introduction to Bayesian computation

chapter 11|18 pages

Basics of Markov chain simulation

chapter 13|40 pages

Modal and distributional approximations

part |2 pages

Part IV: Regression Models

chapter 14|28 pages

Introduction to regression models

chapter 15|24 pages

Hierarchical linear models

chapter 16|30 pages

Generalized linear models

chapter 17|14 pages

Models for robust inference

chapter 18|20 pages

Models for missing data

part |2 pages

Part V: Nonlinear and Nonparametric Models

chapter 19|16 pages

Parametric nonlinear models

chapter 20|14 pages

Basis function models

chapter 21|18 pages

Gaussian process models

chapter 22|26 pages

Finite mixture models

chapter 23|30 pages

Dirichlet process models