| Call Number | 15198 |
|---|---|
| Day & Time Location |
MW 6:10pm-7:25pm 428 Pupin Laboratories |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Ronald Neath |
| 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/GR5204 a course in statistical modeling and data analysis such as STAT GU4205/GR5205. |
| Web Site | Vergil |
| Department | Statistics |
| Enrollment | 18 students (86 max) as of 8:06PM Monday, March 10, 2025 |
| Subject | Statistics |
| Number | GR5224 |
| Section | 001 |
| Division | Interfaculty |
| Note | STAT MA students only. |
| Section key | 20243STAT5224W001 |