STAT 341
Bayesian Statistics Spring 2013
Division III Quantitative/Formal Reasoning
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Class Details

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.
The Class: Format: lecture
Limit: none
Expected: 10
Class#: 3242
Grading: yes pass/fail option, yes fifth course option
Requirements/Evaluation: evaluation will be based on homework and exams
Prerequisites: Statistics 201 and Mathematics 211, or permission of instructor
Enrollment Preferences: Juniors and Seniors, Math Majors
Distributions: Division III Quantitative/Formal Reasoning

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