Faculty Name: Dr. Arunabha Majumdar
Course Name: Introduction to Bayesian Statistics (MA4740)
Course Period: 04 th Aug to 28 th Nov 2025 (Monday 11:00 AM to 11:55 AM; Wednesday 10:00 AM to 10:55 AM; Thursday 09:00 AM to 09:55 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.
About the Instructor: Dr. Arunabha Majumdar, accompanied by teaching assistants from the Department of Mathematics.
Good background in probability theory and applied statistics.
Assessment may consist of assignments and/or quizzes and/or viva and/or exams.
Fee: Rs.15,000/- Plus GST
Apply Here
Last Date for Registration and Payment: TBA