A graphical model, or Bayesian network, encodes probabilistic relationships among variables. Techniques based on these models are becoming increasingly important in data analysis applications of many types. In areas such as foreign-language translation, microchip manufacturing, and drug discovery, the volume of data can slow progress because of the difficulty of finding causal connections or dependencies. The new Bayesian methods enable these tangled interconnections to be sorted out and produce useful tools for handling large data sets. Google is already using these techniques to find and take advantage of patterns of interconnections between Web pages, and Bill Gates has been quoted as saying that expertise in Bayesian networks is an essential part of Microsoft's competitive advantage, particularly in such areas as speech recognition. (Bayesian networks now pervade Microsoft Office.) Recently, the MIT Technology Review named Bayesian networks as one of the top ten emerging technologies.

- Go to the talk on An Introduction to Probabilistic Graphical Models and Their Lyapunov Functions and Algorithms for Inference and Learning (by Prof. Brendan J. Frey)
- Go to the talk on Graphical Models for Linear Systems, Codes and Networks (by Prof. Ralf Koetter)
- Go to the talk on Graphical Models, Exponential Families and Variational Inference (by Prof. Michael I. Jordan)

Remark: This workshop was held on February 19, 2004 as part of the Computational Sciences Lecture Series (CSLS) at the University of Wisconsin-Madison.