SISMID MCMC I
Instructors:
| Kari Auranen | 
|
| M. Elizabeth Halloran | 
|
| Vladimir Minin | 
|
| Time | Place |
| July 14, 11:30 am - 2:30 pm | Zoom |
| July 15-16, 8:00 am - 2:30 pm | Zoom |
SISMID 2021, Module 8: MCMC Methods for Infectious Diseases I
2021 webpage moved to a new location: https://vnminin.github.io/sismid_mcmc_one/
Course Description: This module is an introduction to Markov chain Monte Carlo (MCMC) methods with some simple applications in infectious disease studies. The course includes an introduction to Bayesian statistics, Monte Carlo, MCMC, some background theory, and convergence diagnostics. Algorithms include Gibbs sampling, Metropolis-Hastings and their combinations. Familiarity with the R statistical package or other computing language would be helpful.
Course Introduction: pre-recorded video (coming soon)
Time table: mcmc_time_table.pdf
R tutorials: R for Beginners, Swirl (Learn R, in R), SISMID/SISG Introduction to R
Course materials:- C.P. Robert and G. Casella. Monte Carlo statistical methods, 2nd edition, Springer-Verlag, 2004.
- C.P. Robert and G. Casella. Introducing Monte Carlo methods with R, Springer-Verlag, 2009. (a more hands-on version of the first book by the same authors)
- J. Albert. Bayesian computation with R, 2nd edition, Springer-Verlag, 2009.
- P. Brémaud. Markov chains: Gibbs fields, Monte Carlo simulation, and queues, Springer-Verlag, 1999.
- L. Tierney. Markov Chains for Exploring Posterior Distributions, Annals of Statistics, 22, 1701-1762, 1994.
- S. Chib. and E. Greenberg. Understanding the Metropolis-Hastings Algorithm, The American Statistician, 49, 327-335, 1995.
- G. Casella and E.I. George. Explaining the Gibbs Sampler, The American Statistician, 46, 167-174, 1992.