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Dec 26, 2024
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MATH153 PO - Bayesian StatisticsWhen Offered: Spring 2020.
Instructor(s): G. Chandler; J. Hardin
Credit: 1
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. Prerequisites: MATH 151 PO or by permission of the instructor.
Satisfies the following General Education Requirement(s), subject to conditions explained in the Degree Requirements section of this Catalog:
Area 5
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