Advanced Bayesian Models for the Social Sciences

Instructor(s):

• Skyler Cranmer, University of North Carolina
• Daniel Stegmueller, University of Essex

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 the Bayesian approach to modeling time series data. This includes basic forms as well as recent developments such as Bayesian vector autoregression methods. The fourth week will focus on Bayesian item response theory (IRT) models, looking at theoretical foundations as well as practical issues such as identification and specification of hierarchies. Throughout the workshop, estimation with modern programming software (R, C, C++, and WinBUGS) will be emphasized.

Fees: Consult the fee structure.

Tags: Bayes,

Course Sections