| Call Number | 10599 |
|---|---|
| Day & Time Location |
MTWR 6:15pm-7:50pm To be announced |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Benjamin K Goodrich |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | This course introduces the Bayesian paradigm for statistical inference. Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models; Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software. Prerequisites: A course in the theory of statistical inference, such as STAT GU4204 a course in statistical modeling and data analysis, such as STAT GU4205.
|
| Web Site | Vergil |
| Subterm | 07/07-08/15 (B) |
| Department | Summer Session (SUMM) |
| Enrollment | 1 student (25 max) as of 4:06PM Thursday, April 3, 2025 |
| Subject | Statistics |
| Number | GU4224 |
| Section | 001 |
| Division | Summer Session |
| Section key | 20252STAT4224W001 |