in Dynamic Models and Stochastic Volatility Models

Technical University of Catalonia

Barcelona, Spain

**Course material** (PDF FILE)

**Textbook**

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 www.dme.ufrj.br/mcmc.

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**)
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Other useful links

International Society for Bayesian Analysis (ISBA)

The R project

Sequential Monte Carlo homepage