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STATS270

Bayesian Statistics

Statistics H&S - Humanities & Sciences

Course Description

This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will examine the construction of priors and the asymptotic properties of likelihoods and posterior distributions. The discussion will include but will not be limited to the case of finite dimensional parameter space. There will also be some discussions on the computational algorithms useful for Bayesian inference. Prerequisites: Stats 116 or equivalent probability course, plus basic programming knowledge; basic calculus, analysis and linear algebra strongly recommended; Stats 200 or equivalent statistical theory course desirable.

Cross Listed Courses

Grading Basis

ROP - Letter or Credit/No Credit

Min

3

Max

3

Course Repeatable for Degree Credit?

No

Course Component

Lecture

Enrollment Optional?

No

Does this course satisfy the University Language Requirement?

No

Programs

STATS270 is a completion requirement for:
  • (from the following course set: )