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Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Using examples largely from the authors' own experiences, the book focuses on modern computational tools and obtains inferences using computer simulations. Its unique features include thorough discussions of the methods for checking Bayesian models and the role of the design of data collection in influencing Bayesian statistical analysis.
Bayesian Data Analysis offers the practicing statistician singular guidance on all aspects of the subject.
Fundamentals of Bayesian Inference Background Single-Parameter Models Introduction to Multiparameter Models Large-Sample Inference and Connections to Standard Statistical Methods Fundamentals of Bayesian Data Analysis Hierarchical Models Model Checking and Sensitivity Analysis Study Design in Bayesian Analysis Introduction to Regression Models Advanced Computation Approximations Based on Posterior Modes Posterior Simulation and Integration Markov Chain Simulation Specific Models Models of Robust Inference and Sensitivity Analysis Hierarchical Linear Models Generalized Linear Models Multivariate Models Mixture Models Models for Missing Data Concluding Advice Appendixes Standard Probability Distributions Outline of Proofs of Asymptotic Theorems