Call Number | 13390 |
---|---|
Day & Time Location |
MW 6:10pm-7:25pm 614 Schermerhorn Hall [SCH] |
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 | 15 students (86 max) as of 5:08PM Saturday, September 7, 2024 |
Subject | Statistics |
Number | GR5224 |
Section | 001 |
Division | Interfaculty |
Open To | GSAS |
Note | STAT MA students only. |
Section key | 20233STAT5224W001 |