State Space Markov Models

ST 797M

Michael Lavine, Professor of Statistics

This is an advanced statistics course on state-space models and their Bayesian analysis. Some examples include During the course we will learn how to build and analyze state space models to incorporate many different features including locally smooth behavior, quasi-cyclic behavior, heteroscedasticity and multivariate observations. We will work heavily with the statistical software R and especially with the dlm package.

R is the software of choice for many research statisticians. It can be downloaded freely on your own computer from the R web site.

Students will find their own data, develop state-space models for their data, and report frequently to the whole class.

We will begin with a paper on the Kalman filter, then use the book Dynamic Linear Models with R. This book is not yet published; please download the pdf file; it is permissible to use it only for the purpose of this class; it is not to be distributed otherwise. Finally, in the last portion of the course we will use Gaussian Markov Random Fields by Rue and Held.