Palestra sobre "Sequential Monte Carlo Methods" no IME-USP

Seminário Imperdível no Ime-USP, na segunda feira, dia 01/12/2008, apresentado pelo Hedibert Lopes:

Particle Learning and Smoothing
Carlos M. Carvalho, Michael Johannes, Hedibert F. Lopes
and Nicholas Polson
First draft: December 2007
This version: October 2008

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.
Keywords: Particle Learning, Filtering, Smoothing, Mixture Kalman Filter, Bayesian
Inference, Bayes factor, State Space Models.

Carvalho, Lopes and Polson are at the Graduate School of Business, University of Chicago, 5807
S. Woodlawn, Chicago IL 60637. email: carlos.carvalho@chicagogsb.edu, hlopes@chicagogsb.edu and ngp@chicagogsb.edu. Johannes is at the Graduate School of Business, Columbia University, 3022 Broadway, NY, NY, 10027. email: mj335@columbia.edu.