Volodymyr Minin (UC Irvine) will give an online mini-course on Bayesian Statistics. The course is intended for students in mathematics, statistics and related disciplines in their 3rd year and above or for beginning graduate students, as well as everybody who is interested in related topics. Students should have taken calculus and linear algebra and be familiar with elementary probability theory (random variables, expectations, standard family distributions, etc.) and basic concepts of mathematical statistics (parameter estimation, uncertainty quantification, linear regression). Some programming experience will be helpful as all mathematical concepts will be illustrated with computations. Lectures will be given in English.

Bayesian statistical inference provides a formal mathematical framework for encoding our existing knowledge using probabilities and then using observed data to update these probabilities. Such Bayesian learning underpins many modern data science applications such as forecasting election results, monitoring spread of infectious diseases, and numerous machine learning tasks, to name just a few. In this course, we will introduce students to mathematical formalism of Bayesian inference and will demonstrate how to interpret key quantities produced by this inferential framework. We will also explain main computational ideas that make Bayesian inference possible: Monte Carlo and Markov chain Monte Carlo algorithms. All mathematical concepts will be illustrated with computations and data-driven examples.

Software requirements: RStudio and R (4.5.1 or above) + ideally students will set up their computing environment in advance following the “Getting set up” section https://www.bayesrulesbook.com/preface#setup of the following textbook, freely available online: https://www.bayesrulesbook.com. In addition, we will try to provide access to a cloud computing environment, so students can follow the material and complete homework even if they couldn’t make their local computing environment satisfy the stated requirements.

Volodymyr Minin is a Professor of Statistics at UC Irvine Donald Bren School of Information & Computer Sciences and an Associate Director of the UCI Infectious Disease Science Initiative. He obtained his undergraduate degree in Mathematics at Odesa National University in 2000, his Master’s degree in Mathematics at the University of Idaho in 2002, and his PhD in Biomathematics at UCLA in 2007. Prof. Minin spent 10 years on the faculty of the University of Washington, Seattle before joining UC Irvine in 2017. Minin is interested in formulating stochastic models that can describe complex dynamics of biological systems and devising statistically rigorous and computationally efficient algorithms to fit these models to data. Prof. Minin is currently most active in infectious disease epidemiology, working on data integration for Bayesian inference of disease transmission model parameters and probabilistic forecasting.