"Graphical models and variational approximation"

1/14/2000: [time not recorded]
[location not recorded]

Abstract: Probabilistic models have become increasingly prominent in recent years in artificial intelligence. General inference algorithms have been discovered that apply to a wide class of interesting and useful models known as ``graphical models'' (aka, Bayesian networks and Markov random fields). These algorithms essentially treat probability theory as a combinatorial calculus, and make creative use of graph theory to stave off the inevitable exponential growth in complexity. There is another feature of probability theory, however, which recommends it as a general tool for computational modeling. Probability involves taking averages, and when averaging is present complex models can be probabilistically simple. In this talk, I discuss variational methodology, which aims to leverage the laws of large numbers and laws of large deviations of probability theory as computational tools within a graphical model framework. I will discuss applications of the variational approach to a variety of probabilistic graphical models, including layered networks with logistic or noisy-OR nodes, coupled hidden Markov models, factorial hidden Markov models, hidden Markov decision trees, and hidden Markov models with long-range dependencies.

Last updated: Mon Jun 19 17:44:06 2006