Bayesian Statistics is a useful framework for statistical inference that has found widespread application in various fields, including social science and business research. Unlike traditional frequentist statistics, Bayesian statistics incorporates prior knowledge or beliefs into the analysis, allowing for more flexible and nuanced modeling.

Assistant Professor of Quantitative Analysis Dr. Yifan Zhang will deliver the second presentation of this two-part series and will offer a technical introduction to Bayesian computation, delving into the popular Markov Chain Monte Carlo (MCMC) method and addressing the modern adaptations necessary for handling large datasets. Additionally, it provides tutorials and examples to guide the audience through the process of conducting Bayesian computing leveraging HPC.