A Course in Bayesian Statistics
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.
This course is cross-listed with STATS370 and requires a high level of math knowledge
- STATS116 or equivalent probability course, plus basic programming knowledge
- Basic calculus, analysis and linear algebra strongly recommended
- STATS200 or equivalent statistical theory course desirable
A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better.
We strongly recommend that you review the first problem set before enrolling. If this material looks unfamiliar or too challenging, you may find this course too difficult.
The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.
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