Bayesian Analyses in the Social Sciences

Catalog description

Discusses principles and practices of Bayesian approaches to statistical inference and modeling. Topics include: philosophical underpinnings of Bayesian inference; associated interpretations of probability; univariate and multivariate models prevalent in the social sciences; Markov chain Monte Carlo estimation; data-model fit; model comparisons procedures; missing data analyses. Course consists of lectures, demonstrations of software, reading assignments, homework assignments and a project. Exposes students to key concepts in Bayesian methods and gains technical foundations necessary to be consumers and contributors to applied and methodological research. Assumes previous knowledge of statistics through multiple regression, and recommends additional topics: multivariate statistics, latent variable modeling, probability theory.