Bayesian Multilevel Modeling

Learning Objectives: Participants will be able to elicit models for analysis of multilevel data using a Bayesian framework and they will be able to implement those models in R using the programming language Stan.

Topics Covered

1. Review of Bayesian inference: Prior, Likelihood and Posterior distributions.
2. MCMC for obtaining samples from posterior distributions, convergence diagnostics.
3. Basics of the Stan Language
4. Applications to Multilevel models: a) Gaussian responses, b) Poisson and Binomial outcomes. 

MSU graduate students can apply for a waiver of the $10 registration fee: https://msu.co1.qualtrics.com/jfe/form/SV_7ao9XSJkxZuXTLM