Part 1: Fundamentals of Bayesian Inference Part 2: Fundamentals of Bayesian Data Analysis Part 3: Advanced Computation Part 4: Regression Models Part 5: Nonlinear and Nonparametric Models   Part 1: 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 2: Fundamentals of Bayesian Data Analysis 1. Model Checking 2. Evaluating, Comparing, and Expanding Models 3. Modeling Accounting for Data Collection 4. Decision Analysis Part 3: Advanced Computation 1. Introduction to Bayesian Computation 2.  Basics of Markov Chain Simulation 3. Computationally Efficient Markov Chain Simulation 4. Modal Distributional Approximations Part 4: Regression Models 1. Introduction to Regression Models 2. Hierachical Linear Models 3. Generalized Linear Models 4. Models for Robust Inference 5. Models for Missing Data Part 5: Nonlinear and Nonparametric Models 1. Parametric Nonlinear Models 2. Basis Function Models 3. Gaussian Process Models 4. Dirichlet Process Models