09 - 11 January 2023

Bayesian Thinking: Fundamentals, Regression, and Multilevel Modeling

Date 09 - 11 Jan 2023
Time 14:00 GMT+01:00 - 17:00 GMT+01:00
Level of instruction
Instructor
Jim Albert
Jingchen (Monika) Hu
Registration fee
Developed country
100,-
Developing country / Students
60,-

Course description

This series of webinar provides a general introduction to Bayesian modeling with a particular focus on regression and multilevel models. The use of the system R in Bayesian computation is described, including the programming of the Bayesian model and the use of different R tools to summarize the posterior. Special focus will be on the application of Markov chain Monte Carlo (MCMC) 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 runjags and rstan packages.

Part 1 (2 hours): Introduction to Bayesian Inference. Basic tenets of Bayesian thinking including construction of priors, summarization of the posterior to perform inferences, and the use of prediction distributions for prediction and model checking.

Part 2 (2 hours): Bayesian Regression. Implementation of Bayesian thinking for regression models for continuous and categorical response data.

Part 3 (2 hours): Bayesian Multilevel Modeling. Introduction to multilevel models as a flexible way of modeling regressions over groups.

Instructors

Jim Albert
Instructor
Jim Albert

About the instructor

Jim Albert is Emeritus Professor at Bowling Green State University.

Jingchen (Monika) Hu
Instructor
Jingchen (Monika) Hu

About the instructor

Jingchen (Monika) Hu is Associate Professor at Vassar College.