Bayesian modeling in the social sciences emphasizing applications in political science, anthropological science, sociology, and education testing. Topics include: Bayesian computation via Markov chain Monte Carlo; Bayesian hierarchical modeling; Bayesian models for latent variables and latent states (measurement modeling); dynamic models; and Bayesian analysis of spatial models. Implementation of Bayesian approaches (priors, efficient sampling from posterior densities), data analysis, and model comparisons. Final project. Prerequisites: exposure to statistical modeling such as 200-level
Many of the readings for this class are available online in electronic format and can be found in this restricted directory.
This restricted directory holds the various hand-outs (e.g., lecture notes, supplementary readings, etc.) that we will accumulate throughout the quarter.
This restricted directory will hold the R/BUGS/JAGS code and data files that we accumulate over the quarter
This page contains pointers to resources for practical Bayesian analysis for the social and biological sciences.Last Modified: 04.03.07