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Applied Bayesian Analysis

A new class (Spring 2007) that I co-teach with Stanford Political Science colleague Simon Jackman is on applied Bayesian analysis in the social sciences. A working syllabus can be found on the course webpage.

Course Description: 

ANTHSCI 254. Applied Bayesian Analysis (Same as POLISCI 354F.) 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 STATS or POLISCI 150/350B,C, or ANTHSCI 292. 3-5 units, Spr (Jones, J; Jackman, S)