Fall 2024 Statistics UN3104 section 001

Applied Bayesian Analysis

Call Number 15168
Day & Time
Location
TR 2:40pm-3:55pm
140 Uris Hall
Points 3
Grading Mode Standard
Approvals Required None
Instructor Dobrin Marchev
Type LECTURE
Method of Instruction In-Person
Course Description

This is a course in intermediate statistical inference techniques in the context of applied research
questions in data science. Assuming some prior exposure to probability and statistics, this course will
first introduce the student to the principles of Bayesian inference, then apply them in estimation and
prediction in the context of linear and generalized linear models, counting and classification, mixture and
multilevel models, including scientific computation (like MCMC methods). Students will also learn
about the main benefits of using Bayesian vs. frequentist methods, like naturally combining prior
information with the data; posterior probabilities as easier to interpret alternatives to p-values; parameter
estimation “pooling” in hierarchical model and so on.

Web Site Vergil
Department Statistics
Enrollment 11 students (50 max) as of 9:14PM Wednesday, November 20, 2024
Subject Statistics
Number UN3104
Section 001
Division Interfaculty
Open To Barnard College, Columbia College, Engineering:Undergraduate, General Studies
Section key 20243STAT3104W001