Wednesday, September 30, 2009
03:30 PM in REC 315
Professor Sergey Kirshner
Department of Statistics, Purdue University

Crash Course on Graphical Models: Part I, Theory

Abstract

Over the last 20 years, graphical models have become an incredibly important tool in dealing with problems in high-dimensional structured domains. Representing the set conditional independence relations between the observations as a graph, graphical models provide a framework for inference and parameter estimation with the computational complexity dependent only on the properties of the underlying graph.

In the first part of a two part series, I will introduce two of the most commonly used types of graphical models, Bayesian networks (directed) and Markov networks (undirected). After describing the semantics for both types of models, I will provide a brief overview of related inference and parameter estimation methods.

The second part of the series will focus on applications of such models.