Particle Learning

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• Nicholas Polson University of Chicago Booth School of Business
• Friday 15 May 2009, 16:00-17:00
• MR12, CMS, Wilberforce Road, Cambridge, CB3 0WB.

If you have a question about this talk, please contact rbg24.

This talk will introduce novel particle learning (PL) methods for sequential filtering, parameter learning and smoothing in a general class of state space models. The approach extends existing particle methods by incorporating unknown fixed parameters, utilizing sufficient statistics, for the parameters and/or the states, and allowing for nonlinearities in the model. We also show how to solve the state smoothing problem, integrating out parameter uncertainty. We show that our algorithms outperform MCMC , as well as existing particle filtering algorithms.

This talk is part of the Statistics series.

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