Bayesian Modeling for the Social Sciences II: Advanced Topics

Instructor(s):

  • Daniel Stegmueller, Duke University

This workshop will be offered in an online video format.

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 Bayesian Modeling for the Social Sciences I: Introduction and Application 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. Advanced topics covered during these two modules include data augmentation and Bayesian model averaging. The third module introduces Bayesian variants of "workhorse" social 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.

Fee and Registration: This course is part of the second four-week session. Please see our fee chart on our Registration page for the cost of attending one (or both) four-week session(s). Participants who enroll in a four-week session may take as many courses (workshops and lectures) as desired during the session for which they are enrolled. Participants in the four-week sessions are also welcome to attend all of the lectures and discussions offered in the Blalock Lecture Series.

Tags: Bayes, hierarchical models, multilevel models, discrete choice models, MCMC, model checking, model comparison

Course Sections

Section 1

Location: Online -- Video format,

Date(s): July 20 - August 14

Time: 1:00 PM - 3:00 PM

Instructor(s):

  • Daniel Stegmueller, Duke University