STAT0412A-F23
Bayesian Statistics
Bayesian Statistics (formerly MATH 0412)
In this course, we will learn about the Bayesian paradigm of statistics, in which one’s inferences about parameters or hypotheses are updated as evidence accumulates. The goals of the course include understanding basic concepts of Bayesian inference; deriving posterior distributions; assessing the adequacy of Bayesian models; and effectively communicating results. Topics covered include one-parameter models, conjugacy, and Gibbs samplers. Real-world data and applications will feature heavily in this course. (MATH 0311 or STAT 0311) 2.5 hr. lect.
In this course, we will learn about the Bayesian paradigm of statistics, in which one’s inferences about parameters or hypotheses are updated as evidence accumulates. The goals of the course include understanding basic concepts of Bayesian inference; deriving posterior distributions; assessing the adequacy of Bayesian models; and effectively communicating results. Topics covered include one-parameter models, conjugacy, and Gibbs samplers. Real-world data and applications will feature heavily in this course. (MATH 0311 or STAT 0311) 2.5 hr. lect.
- Term:
- Fall 2023
- Location:
- Warner Hall 011(WNS 011)
- Schedule:
- 11:15am-12:05pm on Tuesday, Thursday, Friday (Sep 11, 2023 to Dec 11, 2023)
- Type:
- Lecture
- Course Modality:
- In-Person
- Instructors:
- Becky Tang
- Subject:
- Statistics
- Department:
- Mathematics & Statistics
- Division:
- Natural Sciences
- Requirements Fulfilled:
- DED
- Levels:
- Undergraduate
- Availability:
- View availability, prerequisites, and other requirements.
- Course Reference Number (CRN):
- 92858
- Subject Code:
- STAT
- Course Number:
- 0412
- Section Identifier:
- A