STAT 441 Bayesian Statistics (Not offered 2006-2007; to be offered 2007-2008) (Q)

The probability of an event can be defined in two ways: (1) the long-run frequency of the event, or (2) the belief that the event will occur. Classical statistical inference is built on the first definition given above, while Bayesian statistical inference is built on the second. This course will introduce the student to methods in Bayesian statistics. Topics covered include: prior distributions, posterior distributions, conjugacy, and Bayesian inference in single-parameter, multi-parameter, and hierarchical models. The computational issues associated with each of these topics will also be discussed.
Format: lecture. Evaluation will be based on homework, exams, and a final project.
Prerequisite: Statistics 201, or permission of instructor. No enrollment limit (expected:15).

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