Approximation Methods for Inference, Learning and Decision-Making

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Graphical models have become a unifying focus for interdisciplinary research in the areas of probabilistic inference, learning and decision-making. Referred to in various settings as Bayesian networks, Markov random fields, influence diagrams, decision networks, or structured stochastic systems, the graphical model formalism is general enough to encompass a wide variety of classical probabilistic systems in AI and engineering, while providing a firm mathematical foundation on which to design new systems. This research will focus on approximation algorithms for large-scale problems to provide a significantly deeper empirical and theoretical understanding of graphical models. The approach will be based on probability propagation, variational and Monte Carlo methods for inference, learning and decision-making, the aim being to understand the kinds of graphical models for which these methods are appropriate. The PI will extend the scope of approximation methodology to include hybrid graphical models and decision networks, and to provide theoretical convergence analyses and error analyses for them. He will also test out the new methods empirically on standard benchmarks and in a variety of application areas. The ultimate goal of the research is to establish probabilistic graphical models as a full-fledged engineering discipline capable of providing robust, systematic solutions to large-scale problems in inference, learning and decision-making. A successful approximation methodology for graphical models would allow an engineer to design a graphical solution to meet performance specifications for a given problem, where these specifications are given in terms of time / accuracy tradeoffs and estimation / approximation tradeoffs. Even partial progress towards these goals will have wide impact in fields where large-scale probabilistic systems are used, including information retrieval, medical diagnosis, biological sequence analysis, source and error-control coding, speech recognition, and machine vision