Advanced Bayesian Models for the Social Sciences
- Daniel Stegmueller, University of Essex
- Jeffrey Harden, University of Colorado at Boulder
This course covers the theoretical and applied foundations of Bayesian statistical analysis at a level that goes beyond the introductory course. Therefore, knowledge of basic Bayesian statistics (such as that obtained from the 'Introduction to Applied Bayesian Modeling for the Social Sciences' workshop) is assumed. The course will consist of four modules. First, we will discuss Bayesian stochastic simulation (Markov chain Monte Carlo) in depth with an orientation towards deriving important properties of the Gibbs sampler and the Metropolis Hastings algorithm. Extensions and hybrids will be discussed. Second, the course will cover model checking, model assessment, and model comparison, with an emphasis on computational approaches. The third module introduces Bayesian variants of "workhorse" political science models, such as linear models, models for binary and count outcomes, discrete choice models, and seemingly unrelated regression. The fourth week will focus on more advanced Bayesian models, such as hierarchical/multilevel models, models for panel and time-series cross-section data, latent factor and item response theory (IRT) models, as well as instrumental variable models. Throughout the workshop, we emphasize not only estimation with modern programming software (R, and JAGS), but also how to communicate results effectively.
Fees: Consult the fee structure.