Subjective and objective Bayesian statistics : principles, models, and applications

Preface. Preface to the First Edition. A Bayesian Hall of Fame. PART I: FOUNDATIONS AND PRINCIPLES. 1. Background. 2. A Bayesian Perspective on Probability. 3. The Likelihood Function. 4. Bayes' Theorem. 5. Prior Distributions. PART II: NUMERICAL IMPLEMENTATION OF THE BAYESIAN PARADIGM. 6. Markov Chain Monte Carlo Methods (Siddhartha Chib). 7. Large Sample Posterior Distributions and Approximations. PART III: BAYESIAN STATISTICAL INFERENCE AND DECISION MAKING. 8. Bayesian Estimation. 9. Bayesian Hypothesis Testing. 10. Predictivism. 11. Bayesian Decision Making. PART IV: MODELS AND APPLICATIONS. 12. Bayesian Inference in the General Linear Model. 13. Model Averaging (Merlise Clyde). 14. Hierarchical Bayesian Modeling (Alan Zaslavsky). 15. Bayesian Factor Analysis. 16. Bayesian Inference in Classification and Discrimination. Description of Appendices. Appendix 1. Bayes, Thomas, (Hilary L. Seal). Appendix 2. Thomas Bayes. A Bibliographical Note (George A. Barnard). Appendix 3. Communication of Bayes' Essay to the Philosophical Transactions of the Royal Society of London (Richard Price). Appendix 4. An Essay Towards Solving a Problem in the Doctrine of Chances (Reverend Thomas Bayes). Appendix 5. Applications of Bayesian Statistical Science. Appendix 6. Selecting the Bayesian Hall of Fame. Appendix 7. Solutions to Selected Exercises. Bibliography. Subject Index. Author Index.