Spring 2024 Quantitative Methods: Social Sciences GR5065 section 001

BAYESIAN STATS FOR THE SOC SCI

BAYESIAN STATS FOR THE SO

Call Number 13160
Day & Time
Location
R 6:10pm-8:00pm
332 Uris Hall
Points 3
Grading Mode Standard
Approvals Required None
Instructor Benjamin K Goodrich
Type SEMINAR
Method of Instruction In-Person
Course Description

An introduction to Bayesian statistical methods with applications to the social sciences. Considerable emphasis will be placed on regression modeling and model checking. The primary software used will be Stan, which students do not need to be familiar with in advance. Students in the course will access the Stan library via R, so some experience with R is necessary. Any QMSS student is presumed to have sufficient background. Any non-QMSS students interested in taking this course should have a comparable background to a QMSS student in basic probability. Topics to be covered are a review of calculus and probability, Bayesian principles, prediction and model checking, linear regression models, Bayesian calculations with Stan, hierarchical linear models, nonlinear regression models, missing data, and decision theory.

Web Site Vergil
Department Quantitative Methods/Social Sciences
Enrollment 18 students (40 max) as of 6:05PM Wednesday, July 24, 2024
Subject Quantitative Methods: Social Sciences
Number GR5065
Section 001
Division Graduate School of Arts and Sciences
Note PRIORITY QMSS STUDENTS
Section key 20241QMSS5065G001