BIMSA > Bayesian Learning and Bayesian Computation
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.
Professor Lars Aake Andersson
讲师
基兰莫伊·达斯
日期
2024年03月11日 至 05月17日
位置
Weekday Time Venue Online ID Password
周一,周三,周五 15:15 - 16:45 A3-1-103 ZOOM 08 787 662 9899 BIMSA
修课要求
Probability Distributions, Statistical Inference, Regression Techniques
课程大纲
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
参考资料
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.

听众
Graduate
视频公开
公开
笔记公开
公开
语言
英文
讲师介绍
2011年于宾夕法尼亚州立大学统计系获得博士学位，师从邬荣领教授和李润泽教授，2019年起在印度统计学院任副教授。主要研究兴趣包括贝叶斯半参数模型、纵向数据分析、模型选择和分位数回归、生物统计等，已发表论文近50篇。2021年荣获印度统计和计划实施部颁发的C.R. Rao国家统计奖。