Department of Statistics

Recent Instructors Ghosh, Sujit	ST 740	Bayesian Inference and Analysis
	Course Description Introduction to Bayesian inference; specifying prior distributions; conjugate priors, summarizing posterior information, predictive distributions, hierachical models, asymptotic consistency and asymptotic normality. A prime objective of the ST740 course is to present techniques and basic results of theory and application of Bayesian inference at a rigorous and advanced calculus level. In ST740 we develop the probabilistic language and computational tools of Bayesian statistics. The course describes probabilistic models for specifying prior distributions, summarizing posterior information, evaluating predictive distributions, formulating hierachical models and asymptotic consistency and asymptotic normality of posterior distributions. Course Syllabus The Bayesian paradigm Prior Information to Distriburion Decision Theory Point Estimation Tests and Credible Regions Bayesian Calculations Hierarchical and Empirical Bayes Course Prerequisites ST522 Course Corequisites None Recent Textbooks The Bayesian Choice 2nd ed, by Robert C. P. (2001) (ISBN: 0-387-95231-4)