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