Spring 2024 Statistics GR5224 section 001

BAYESIAN STATISTICS

Call Number 13648
Day & Time
Location
TR 7:40pm-8:55pm
501 Schermerhorn Hall [SCH]
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/GR5204 a  course in statistical modeling and data analysis such as STAT GU4205/GR5205.

Web Site Vergil
Department Statistics
Enrollment 32 students (125 max) as of 9:04AM Wednesday, December 4, 2024
Subject Statistics
Number GR5224
Section 001
Division Interfaculty
Open To GSAS
Note STAT MA students only
Section key 20241STAT5224W001