**Bayesian Statistics (Q)**

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.

**lecture**

*Class Format:***evaluation will be based on homework and exams**

*Requirements/Evaluation:*

*Additional Info:*

*Additional Info2:***Statistics 201 and Mathematics 211, or permission of instructor**

*Prerequisites:***Juniors and Seniors, Math Majors**

*Enrollment Preference:*

*Department Notes:*

*Material and Lab Fees:*

*Distribution Notes:***Division III,Quantitative and Formal Reasoning**

*Divisional Attributes:*

*Other Attributes:***none**

*Enrollment Limit:***10**

*Expected Enrollment:***3242**

*Class Number:*CLASSES | ATTR | INSTRUCTORS | TIMES | CLASS NUMBER |
---|---|---|---|---|

STAT341-01(S) LEC Bayesian Statistics (Q) | Wendy Wang |
MWF 10:00 AM-10:50 AM Bronfman 104 | 3242 |