Probabilistic Data Analysis
(확률적 데이터 분석)

Spring 2021

Instructor: Yongdai Kim (ydkim903@snu.ac.kr)

Summary

This course considers data analysis based on probabilistic models. In particular, various methods of Bayesisan analysis are covered from basics to advanced machine learnings.

Content

  • · Introduction to Bayesian statistics
  • · Distributions
  • · Prior and posterior
  • · Markov Chain Monte Carlo
  • · Regression models
  • · Hierarchical models
  • · Model selelction and model assessment
  • · Finite mixture models
  • · Topic model
  • · Gaussian process prior
  • · Dirichlet process prior
  • · Advanced topics in Bayesian machine learnings

Textbooks

  • · Lecture slides found in the course web site.
  • · Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A. and Rubin, D. (2013). Bayesian Data Analysis. (3rd edition). CRC Press.
  • · PDF and additional materials are available at http://www.stat.columbia.edu/~gelman/book.
  • · Supplementary materials: [BER] – Berger, J. (1985). Statistical Decision Theory and Bayesian Analysis. (2nd edition). Springer.

Grading Policy

  • · Assignment: 20%

  • · Midterm Exam: 40%

  • · Final Exam: 40%