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MATH153 HM - Bayesian Statistics


Credit(s): 3

Instructor(s): Williams

Offered: Spring, alternate years

Description: An introduction to principles of data analysis and advanced statistical modeling using Bayesian inference. Topics include a combination of Bayesian principles and advanced methods; general, conjugate and noninformative priors, posteriors, credible intervals, Markov Chain Monte Carlo methods, and hierarchical models. The emphasis throughout is on the application of Bayesian thinking to problems in data analysis. Statistical software will be used as a tool to implement many of the techniques.

Prerequisite(s): Permission of instructor