STAT 365
Bayesian Statistics Spring 2024
Division III Quantative/Formal Reasoning

Prior knowledge being constantly updated by empirical observations — the essence of Bayesian thinking provides a natural, intuitive, and more importantly, mathematically sounded, probabilistically principled way to characterize the process of learning. With some of its key ideas formulated based on Bayes’ Theorem dating back to 18th century, Bayesian inference is one of oldest schools of statistics (more than a century earlier than the Frequentist!). Yet it was not until the recent developments in sampling algorithms and computational powers that Bayesian inference gained its revival. Bayesian, and Bayesian-based methods, with their flexibilities in modeling (generative) process of data, interpretability with posterior probability statements, and coherent principles to incorporate empirical evidence a priori, have played key roles in modern data analysis, especially for those “big data” with enhanced complexity and connectivity. This course is designed to provide students a comprehensive understanding to what is Bayesian and the how’s and why’s. Students will be introduced to classic Bayesian models, basic computational algorithms/methods for Bayesian inference, as well as their applications in various fields, and comparisons with classic Frequentist methods. As Bayesian inference finds its roots and merits particularly in application, this course puts great emphasis on enhancing students’ skills in statistical computation (mostly with R) and data analysis.