Title :
Bayesian filtering for hidden Markov models via Monte Carlo methods
Author :
Doucet, A. ; Andrieu, C. ; Fitzgerald, W.
Author_Institution :
Dept. of Eng., Cambridge Univ., UK
fDate :
31 Aug-2 Sep 1998
Abstract :
We propose a new Monte Carlo method for Bayesian filtering of general nonlinear and non-Gaussian hidden Markov models. This method is an extension of the well known importance sampling method. It is especially well-suited to sequential simulation as it allows one to split or kill trajectories according to a given score function. The model and estimation objectives are described. The new Monte Carlo method is presented. A few results on this method are established and its application to Bayesian filtering is described. Simulation results for several nonlinear and non-Gaussian time series are presented
Keywords :
Bayes methods; Monte Carlo methods; filtering theory; hidden Markov models; probability; recursive estimation; Bayesian filtering; Monte Carlo methods; hidden Markov models; nonlinear time series; probability; recursive estimation; sequential simulation; Bayesian methods; Equations; Filtering; Hidden Markov models; Monte Carlo methods; Neural networks; Signal processing; State estimation; State-space methods; Terminology;
Conference_Titel :
Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop
Conference_Location :
Cambridge
Print_ISBN :
0-7803-5060-X
DOI :
10.1109/NNSP.1998.710649
