Bayesian Learning and Bayesian Computation
This is a graduate course for students and researchers who want to learn Bayesian modeling and data analysis using Bayesian techniques. Also the advanced Bayesian computation methods, for example, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods will be discussed in detail.
Lecturer
Date
11th March ~ 17th May, 2024
Location
| Weekday | Time | Venue | Online | ID | Password |
|---|---|---|---|---|---|
| Monday,Wednesday,Friday | 15:15 - 16:45 | A3-1-103 | ZOOM 08 | 787 662 9899 | BIMSA |
Prerequisite
Probability Distributions, Statistical Inference, Regression Techniques
Syllabus
Bayesian Statistics, Bayesian Estimation, Model building, Hierarchical Bayes, Monte Carlo methods, Markov Chain Monte Carlo, Bayesian model selection, Analysis of dependent data using Bayesian modeing, Nonparametric Bayes, Dirichlet Process (DP), Hierarchical DP
Reference
1. Bayesian Data Analysis by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin. CRC Press.
2. Applied Bayesian Hierarchical Methods by Peter Congdon. CRC Press. CRC Press.
3. Introducing Monte Carlo Methods with R by Robert and Casella. Springer.
2. Applied Bayesian Hierarchical Methods by Peter Congdon. CRC Press. CRC Press.
3. Introducing Monte Carlo Methods with R by Robert and Casella. Springer.
Audience
Graduate
Video Public
Yes
Notes Public
Yes
Language
English
Lecturer Intro
Prof. Kiranmoy Das obtained his PhD from The Pennsylvania State University, Statistics Department, and has taught in Temple University (USA), Presidency University (India), Indian Statistical Institute (India). He has published nearly 50 research articles in Statistics, Medical Sciences, Computer Science and Biology, has supervised 2 PhD and more than 30 MS students. He has joined BIMSA as a professor in August 2023. His research interests include Bayesian modeling, Longitudinal data analysis, Biostatistics, Statistical Computing, Wireless Communications.