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Activity Number: 141
Type: Invited
Date/Time: Monday, August 3, 2009 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract - #303031
Title: Particle Learning and Smoothing
Author(s): Hedibert F. Lopes*+ and Carlos M. Carvalho and Nicholas Polson and Michael Johannes
Companies: The University of Chicago and The University of Chicago and The University of Chicago and Columbia Business School
Address: Booth School of Business, Chicago, IL, 60637,
Keywords: Particle Learning ; Filtering and Smoothing ; State Space Models ; Mixture Kalman Filter ; Bayesian Inference ; Bayes factor
Abstract:

This paper provides 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.

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