AMS206: Applied Bayesian Statistics

Introduces Bayesian statistical modeling from a practitioner's perspective. Covers basic concepts (e.g., prior-posterior updating, Bayes factors, conjugacy, hierarchical modeling, shrinkage, etc.), computational tools (Markov chain Monte Carlo, Laplace approximations), and Bayesian inference for some specific models widely used in the literature (linear and generalized linear mixed models).(Formerly Classical and Bayesian Inference.) Prerequisite(s): course 131 or 203, or by permission of the instructor. Enrollment is restricted to graduate students.

5 credits

Year	Fall	Winter	Spring	Summer

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