2020년 12월 29일

Bayesian Statistics for Data Analysis (IMEN 891E)

Course Objectives:

 This course will introduce elementary Bayesian inference and computational methods for data analysis.

Textbook : 

1) Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A. and Rubin, D. (2013). Bayesian Data Analysis. (3rd edition). CRC Press.

2) Berger, J. (1985). Statistical Decision Theory and Bayesian Analysis. (2nd edition). Springer.

 

Materials

[Chapter 1] – Introduction

[Chapter 2] – Single Parameter Models

[Chapter 3] – Multi Parameter Models

[Chapter 4] – Random Number Generation

[Chapter 5] – Markov Chain Monte Carlo Methods

[Chapter 6] – Hamiltonian Monte Carlo Methods

[Chapter 7] – Hierarchical Models

[Chapter 8] – Linear Regression Models

[Chapter 9] – Model Assessment

[Chapter 10] – Finite Mixture Models

[Chapter 11] – Latent Dirichlet Allocation

[Chapter 12] – Gaussian Process Regression

[Chapter 13] – Dirichlet Process Prior

[Chapter 14] – Frequentist Properties of Bayes Procedures