Spring 2024 Statistics GU4224 section 001

BAYESIAN STATISTICS

Call Number 13634
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 a course in statistical modeling and data analysis, such as STAT GU4205.

 

Web Site Vergil
Department Statistics
Enrollment 18 students (25 max) as of 6:06PM Monday, December 2, 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 20241STAT4224W001