MATH 598: Topics in Statistics
Fall 2020
Bayesian Inference, Computational Methods and Monte Carlo

  • Instructor: David A. Stephens (Burnside 1225)
  • Email: David Stephens
  • Office Hours: TBA
  • Please post questions on the MS TEAMS channel:
  • 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

Lecture Slides

  1. Bayesian Theory Slides
  2. MCMC and Computation Slides

Handouts

  1. de Finetti's Theorem in the 0-1 case

     

Projects

  1. Project 1 Solutions knitr
  2. Project 2 Solutions knitr
  3. Project 3 Solutions
  4. Project 4 Solutions knitr
  5. Project 5 Solutions
  6. Project 6 Solutions
  7. Project 7 Solutions
  8. Project 8 Solutions

 

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. MCMC Continuous Rnw pdf
8. Weibull model Rnw pdf
9. Non-linear regression 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
16. Sequential Monte Carlo for the SV model Rnw pdf
17. Reversible Jump MCMC Rnw pdf
18. The Galaxy data Rnw pdf
19. Flexible regression modelling Rnw pdf
20. Bayesian Nonparametric methods Rnw pdf
21. The Bayesian Bootstrap Rnw pdf
22. The Langevin Algorithm Rnw pdf
23. Hamiltonian Monte Carlo Rnw pdf
24 HMC: a 1d example Rnw pdf
25. HMC for a GLM Rnw pdf



 

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


E-mail : David Stephens