Faculty Name: Dr. Arunabha Majumdar

Course Name: Introduction to Bayesian Statistics (MA4740)

Course Period: 28 ^th Jul to 18 ^th Nov 2025 (Tuesday 11:00 AM to 11:55 AM; Wednesday 03:00 PM to 03:55 PM; Friday 10:00 AM to 10:55 AM)

Number of Credits: 3 credits

Course Contents:
Bayes’ theorem, prior and posterior distributions, conjugate priors, Bayesian inference for single and multi-parameter models, posterior intervals, posterior predictive distribution, Markov chain Monte Carlo (MCMC), Metropolis-Hastings algorithm, Gibbs sampling, convergence diagnostics, Bayesian hypothesis testing, Bayesian regression and classification.

What you'll learn: Bayesian Statistics is a key topic in modern statistical science. It provides principled ways of combining prior information with data at hand. The objective of this course is to explore Bayesian inference techniques and discuss their application in real-life problems. Students will learn how to formulate a scientific question by constructing a Bayesian model and performing Bayesian statistical inference to answer that question. Throughout this course, students will be exposed to the theory of Bayesian inference and will learn several computational techniques, such as Markov Chain Monte Carlo (MCMC) algorithms, and use these techniques for Bayesian analysis of real data.