| Call Number | 14020 |
|---|---|
| Day & Time Location |
TR 7:40pm-8:55pm To be announced |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Dobrin Marchev |
| 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 |
| Department | Statistics |
| Enrollment | 14 students (25 max) as of 9:14PM Wednesday, November 20, 2024 |
| Subject | Statistics |
| Number | GU4224 |
| Section | 001 |
| Division | Interfaculty |
| Open To | Barnard College, Columbia College, Engineering:Undergraduate, Engineering:Graduate, GSAS, General Studies, SIPA, Professional Studies |
| Section key | 20251STAT4224W001 |