MATH 598: Topics in Statistics
Bayesian Inference, Computational Methods and Monte Carlo
Fall 2018

  • Instructor: David A. Stephens (Burnside 1225)
  • Email: David Stephens
  • Office Hours: Tuesday and Thursday 12:00pm-2:00pm
  • Textbooks :
    • The Bayesian Choice, CP Robert.
    • Bayesian Core: A Practical Approach to Computational Bayesian Statistics,
      J-M Marin and CP Robert.
    • Monte Carlo Statistical Methods, CP Robert and G Casella.

  • Syllabus and Method of Evaluation

Handouts

  1. MCMC Slides: Part I
  2. MCMC Slides: Part II
  3. MCMC Slides: Part III
  4. MCMC Slides: Part IV




 

Assignments

  1. Project 1 Solutions
  2. Project 2 Solutions
  3. Project 3

Exercises

 

 knitr style file 
1. Normal Model Rnw pdf
2. Binomial Model Rnw pdf
3. GLM Rnw pdf
4. Basic Monte Carlo Rnw pdf
5. Variance Reduction Rnw pdf
6. Markov chains Rnw pdf
7. Non-linear regression Rnw pdf
8. MCMC Continuous Rnw pdf
9. Weibull model Rnw pdf
10. Auxiliary variables Rnw pdf
11. Missing data Rnw pdf
12. Hierarchical model Rnw pdf
13. Hierarchical linear regression Rnw pdf
14. Hierarchical non-linear regression Rnw pdf
15. Tempered MCMC and AIS Rnw pdf



 

 
Contact Details:
Professor David A. Stephens
Room 1225, Burnside Hall
Department of Mathematics and Statistics
McGill University


E-mail : David Stephens