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.

Contenuti:

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

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