About this course
Welcome to the course
Bayesian models & data analysis (CGS698C). The course will be taught offline at IIT Kanpur in the even semester of the year 2023-24. This is an introductory course designed to equip students with a conceptual understanding of Bayesian inference and its applications on real-life datasets. A major part of this course will be focused on practicing Bayesian modeling using R (a programming language).
It is expected that you have basic programming knowledge, e.g., you should be able to write 'for' loops, conditional statements, and functions in R. You can also do the programming part in Python, but my lecture notes will use R. We are going to cover the following topics:
- Sets, probability, and random variables
- Bayes' theorem
- The likelihood function, the priors, and the posteriors
- Parameter estimation: Markov chain Monte Carlo, Hamiltonian Monte Carlo
- Bayesian regression models
- Model comparison and hypothesis testing
- Bayesian hierarchical modeling
The content for the course will be in the form of lecture notes, example codes, and in-class exercises on each topic. We will use the books
Bayesian Data Analysis by Gelman and Carlin, Bayesian data analysis by Kruschke, Statistical rethinking by Richard McElreath, and
An Introduction to Bayesian Data Analysis for Cognitive Science by Nicenboim, Schad, and Vasishth as additional resources for practice.
Prerequisites
- R programming (loops, conditional statements, functions)
- High-school mathematics (basic arithmetics, functions, equations, sets)
Timings and location
- Times: Tuesday 10:30 to 11:45, Thursday 12:00 to 13:15
- Location: TBA