Bayesian Thinking: Fundamentals, Computation, and Multilevel Modeling (ADDED FEE) — Professional Development Continuing Education Course
ASA, Section on Bayesian Statistical Science
The basic tenets of Bayesian thinking are introduced, including construction of priors, summarization of the posterior to perform inferences, and the use of prediction distributions for prediction and model checking. There will be a focus on Bayesian regression for continuous and categorical response data. Bayesian multilevel models are introduced as a flexible way of modeling regressions over groups. The use of R in Bayesian computation is described, including the programming of the posterior distribution and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo algorithms and diagnostic methods to assess convergence of the algorithms. The LearnBayes and rethinking R packages are used to illustrate MCMC fitting by the use of gibbs sampling and Metropolis algorithms. Larger Bayesian models will be fit using JAGS and Stan and the accompanying rjags and rstan packages. Some familiarity of the participant with the R statistical language would be helpful.
Instructor(s): Jim Albert, Bowling Green State University