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Activity Number: 101
Type: Topic Contributed
Date/Time: Monday, August 7, 2006 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #305931
Title: Strategies for Online Inference with Dynamic Graphs
Author(s): Makram Talih*+
Companies: City University of New York-Hunter College
Address: 695 Park Ave., Room 905 HE, New York, NY, 10021,
Keywords: dynamic graphs ; graphical models ; sequential Monte Carlo ; posterior inference ; hidden Markov model ; particle filters
Abstract:

We will present strategies for online learning of dynamic graphs via Sequential Monte Carlo (SMC). SMC algorithms are based on maintaining in parameter space an ensemble of particles, each of which tracks a particular realization of the process under study. In our framework, the process is governed by the posterior distribution of the parameters (e.g., the precision matrix) and hidden variables (e.g., the underlying undirected graph), given the data sequence. This talk will further explore the use of geometric methods for designing an efficient importance distribution.

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