Short-courses and Tutorials

Markov Chain Monte Carlo and Sequential Monte Carlo Methods
in Dynamic Models and Stochastic Volatility Models

III Summer School
Technical University of Catalonia
Barcelona, Spain

Course material (PDF FILE)


Most of the material from lectures one through six are based on my book entitled MCMC: Stochastic Simulation for Bayesian Inference, co-authored by D. Gamerman and published by Chapman&Hall/CRC in 2006. R code for many of the book’s worked examples can be found at

Course outline

Lecture 1: Bayesian inference
Lecture 2: Bayesian model criticism
Lecture 3: Monte Carlo (MC) methods
Lecture 4: Markov chain Monte Carlo (MCMC) methods
Lecture 5: Dynamic linear models (DLM)
Lecture 6: Nonnormal, nonlinear dynamic models
Lecture 7: Stochastic volatility models as dynamic models
Lecture 8: Sequential Monte Carlo (SMC) methods
Lecture 9: SMC with parameter learning
Lecture 10: SMC in stochastic volatility models

R code

Lecture 1: (boxtiao.R)
Lecture 3: (montecarlo.R) (simplemontecarlo.R) (rejection.R) (SIR.R) (MC-classroom.R) (REJECTION-classroom.R) (SIR-classroom.R)
Lecture 4: (markovchain.R) (poisson-changepoint.R) (metropolis-hastings-mixtures.R) (AR1.R) (AR1-drift.R) (GARCH.R)
Lecture 5: (dlm.R) (dlm-ffbs.R)
Lecture 6: (bernoulli-regression.R) (nonlineardynamicmodel.R)
Lecture 7: (sv-ar1.R) (sv-ar1-routines.R)
Lecture 8: (dlm-smc.R) (bootstrapfilter-stepbystep.R) (LW-stepbystep.R) (nonlineardynamicmodel-smc.R) (dlm-smc-smoothing.R)
Lecture 9: (dlm-smc-learningsig2-LW.R) (dlm-smc-learningsig2-PL.R) (nonlinearmodel-LW.R)
Lecture 10: (sv-LW.R)

Other useful links

International Society for Bayesian Analysis (ISBA)
The R project
Sequential Monte Carlo homepage