Cover; Series; Contents; Preface; Part I: Fundamentals of Bayesian Inference; Chapter 1: Probability and Inference; Chapter 2: Single-parameter Models; Chapter 3: Introduction to Multiparameter Models; Chapter 4: Asymptotics and Connections to non-Bayesian Approaches; Chapter 5: Hierarchical Models; Part II: Fundamentals of Bayesian Data Analysis; Chapter 6: Model Checking; Chapter 7: Evaluating, Comparing, and Expanding Models; Chapter 8: Modeling Accounting for Data Collection; Chapter 9: Decision Analysis; Part III: Advanced Computation; Chapter 10: Introduction to Bayesian Computation.
Chapter 11: Basics of Markov Chain SimulationChapter 12: Computationally Efficient Markov Chain Simulation; Chapter 13: Modal and Distributional Approximations; Part IV: Regression Models; Chapter 14: Introduction to Regression Models; Chapter 15: Hierarchical Linear Models; Chapter 16: Generalized Linear Models; Chapter 17: Models for Robust Inference; Chapter 18: Models for Missing Data; Part V: Nonlinear and Nonparametric Models; Chapter 19: Parametric Nonlinear Models; Chapter 20: Basis Function Models; Chapter 21: Gaussian Process Models; Chapter 22: Finite Mixture Models.
Chapter 23: Dirichlet Process ModelsAppendix A: Standard Probability Distributions; Appendix B: Outline of Proofs of Limit Theorems; Appendix C: Computation in R and Stan; References; Back Cover.
FUNDAMENTALS OF BAYESIAN INFERENCEProbability and InferenceSingle-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian ApproachesHierarchical ModelsFUNDAMENTALS OF BAYESIAN DATA ANALYSISModel Checking Evaluating, Comparing, and Expanding ModelsModeling Accounting for Data Collection Decision AnalysisADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional ApproximationsREGRESSION MODELS Introduction to Regression Models Hierarchical Linear.