JSM 2005 - Toronto

Abstract #304220

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 265
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #304220
Title: Online Learning of Dynamic Graphical Models via Particle Filters
Author(s): Makram Talih*+
Companies: Hunter College, CUNY
Address: 695 Park Ave, New York, NY, 10021, United States
Keywords: Change-Points ; Dynamic State-Space Model ; Hidden Graphical Model ; Online Inference ; Sequential Monte Carlo Algorithm ; Time-Varying Graphs
Abstract:

Graphical models have been applied recently to a number of structured stochastic systems, such as a rise in the study of telecommunication networks, medical monitoring data, and asset pricing. In many of those applications, the data presents itself not as a static dataset, but as a continuous datastream. Talih and Hengartner (2005) developed a graphical model for sequences of Gaussian random vectors when changes in the underlying graph occur at random times; and a new block of data is created with the addition or deletion of an edge. An important practical goal is that of online learning and prediction of the continually updated precision matrix. The MCMC framework of Talih and Hengartner (2005), while flexible enough to accommodate posterior predictive structural inference, is inefficient when online; structural learning is needed. This paper proposes a novel online posterior inference strategy for dynamic graphical models based on well-known particle-filtering techniques.

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Revised March 2005