Bayesian Inference

Catalog description

This course provides the student with an introduction to Bayesian inference. The course will emphasize the theoretic aspects of the Bayesian paradigm. Topics covered in the course will be: utility, loss functions, minimaxity and admissibility, Bayes’ estimators, maximum entropy priors, conjugate priors, non-informative priors, improper priors, Bayes factors, Markov chain Monte-Carlo algorithms including Metropolis-Hastings and Gibbs sampling. Prerequisite(s): STAT 7620 >= C Lecture Hours: 3 Repeatability: May not be repeated for credit. Grade Mode: Normal, Audit Program Restrictions: DPHIL_BIOS, MS_BIOS Schedule Type (Primary): Lecture Click here for the Schedule of Classes.