Spring 2023 Statistics GR5224 section 001

BAYESIAN STATISTICS

Call Number 14272
Day & Time
Location
MW 6:10pm-7:25pm
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 23 students (125 max) as of 4:06PM Thursday, March 28, 2024
Subject Statistics
Number GR5224
Section 001
Division Interfaculty
Open To GSAS
Campus Morningside
Note STAT MA students only
Section key 20231STAT5224W001