Recent advances in Markov Chain Monte Carlo (MCMC) simulation have led to the development of a high-level probability modeling language called Stan. In this presentation, Sean Raleigh will give a gentle introduction to Bayesian inference using R and Stan.

Sean Raleigh received his Ph.D. in mathematics from U.C. San Diego, specializing in geometric topology and knot theory. He is a professor of mathematics at Westminster College and currently chairs the data science program. As part of Sean's professional work, he advocates for Bayesian methods in data analysis and co-directs QUARC, the Quantitative Analysis and Research Cooperative.