Description
Book SynopsisFocuses on key methodologies and applications for Bayesian models and computation. This book describes parametric and nonparametric Bayesian methods for modeling, and how to use modern computational methods to summarize inferences using simulation.
Table of Contents1. Model Selection and Hypothesis Testing based on Objective Probabilities and Bayes Factors; 2. Bayesian Model Checking and Model Diagnostics; 3. Bayesian Nonparametric Modeling and Data Analysis: An Introduction; 4. Some Bayesian Nonparametric Models; 5. Bayesian Modeling in the Wavelet Domain; 6. Bayesian Methods for Function Estimation; 7. MCMC Methods to Estimate Bayesian Parametric Models; 8. Bayesian Computation: From Posterior Densities to Bayes Factors, Marginal Likelihoods, and Posterior Model Probabilities; 9. Bayesian Modelling and Inference on Mixtures of Distributions; 10. Variable Selection and Covariance Selection in Multivariate Regression Models; 11. Dynamic Models; 12. Elliptical Measurement Error Models – A Bayesian Approach; 13. Bayesian Sensitivity Analysis in Skew-elliptical Models; 14. Bayesian Methods for DNA Microarray Data Analysis; 15. Bayesian Biostatistics; 16. Innovative Bayesian Methods for Biostatistics and Epidemiology; 17. Modeling and Analysis for Categorical Response Data; 18. Bayesian Methods and Simulation-Based Computation for Contingency Tables; 19. Teaching Bayesian Thought to Nonstatisticians
