September 2012

**Lectures**

Part I: Inference and computation

Bayes post-MCMC I – Prior, likelihood, posterior, predictive, etc

Bayes post-MCMC II – MC and MCMC schemes

Bayes post-MCMC III – Bayesian model criticism

Part II: Linear models

Limited dependent variable models

Part III: Multivariate analysis

Bayesian vector autoregressions

Part IV: Dynamic models

Sequential Monte Carlo methods: pure filtering

Sequential Monte Carlo methods: state and parameter learning

Homework assignments

Homework 1 – simple linear regression with conjugate and non-conjugate priors

Other useful links

My book on “MCMC: Stochastic Simulation for Bayesian Inference”