Bayesian Statistical Modeling with Stan, R, and Python

Preface

Part I: Introduction Chapter 1: Overview of Statistical Modeling and StanChapter 2: Review of Bayesian InferenceChapter 3: Before Starting Statistical Modeling

Part II: Introduction of StanChapter 4: Start with Stan, RStan and PyStanChapter 5: Elementary Regression and Model Check

Part III: Essential Components and Techniques for ExpertsChapter 6: Introduction of Distributions from Modeling ViewpointsChapter 7: Issues of RegressionChapter 8: Nonlinear ModelChapter 9: Hierarchical ModelChapter 10: Advanced GrammarsChapter 11: How to Lead ConvergenceChapter 12: Discrete ParametersChapter 13: Usage of MCMC Samples

Part IV: Advanced  Topics for Real-world DataChapter 14: Longitudinal Data Analysis with State Space Model Chapter 15: Spatial Data Analysis with Markov Field ModelChapter 16: Survival AnalysisChapter 17: Causal InferenceChapter 18: Model selection

Appendix: Differences between Stan and BUGSReferenceIndex