A Course in Bayesian Statistics


Stanford School of Humanities and Sciences


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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

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.

Note on Course Availability

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.

002 Winter 2020-21 Online

Pre-register Now

Dates:January 11 - March 19, 2021
Units: 3.00
Instructors: Wing Wong
Delivery Option:
For Credit $4,056.00
Notes: Pre-registration Dates: November 2, 2020 at 9:00am to December 4, 2020 at 5:00pm

Pre-registration for this course will secure your enrollment request and ensure timely processing of your application for potential course approval. Please note: course enrollment will be confirmed after December 11, 2020; after completing your pre-registration, no further action is required on your part.


This course may not currently be available to learners in some states and territories.