DMCA
Sequential Monte Carlo Methods for Dynamic Systems (1998)
| Venue: | Journal of the American Statistical Association |
| Citations: | 663 - 13 self |
BibTeX
@ARTICLE{Liu98sequentialmonte,
author = {Jun S. Liu and Rong Chen},
title = {Sequential Monte Carlo Methods for Dynamic Systems},
journal = {Journal of the American Statistical Association},
year = {1998},
volume = {93},
pages = {1032--1044}
}
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Abstract
A general framework for using Monte Carlo methods in dynamic systems is provided and its wide applications indicated. Under this framework, several currently available techniques are studied and generalized to accommodate more complex features. All of these methods are partial combinations of three ingredients: importance sampling and resampling, rejection sampling, and Markov chain iterations. We deliver a guideline on how they should be used and under what circumstance each method is most suitable. Through the analysis of differences and connections, we consolidate these methods into a generic algorithm by combining desirable features. In addition, we propose a general use of Rao-Blackwellization to improve performances. Examples from econometrics and engineering are presented to demonstrate the importance of Rao-Blackwellization and to compare different Monte Carlo procedures. Keywords: Blind deconvolution; Bootstrap filter; Gibbs sampling; Hidden Markov model; Kalman filter; Markov...
Keyphrases
dynamic system sequential monte carlo method different monte carlo procedure general use kalman filter bootstrap filter general framework importance sampling blind deconvolution partial combination wide application rejection sampling monte carlo method hidden markov model desirable feature complex feature gibbs sampling available technique markov chain iteration generic algorithm
