NDLI: A Class of Fast Exact Bayesian Filters in Dynamical Models With Jumps

Content Provider	IEEE Xplore Digital Library
Author	Petetin, Y. Desbouvries, F.
Copyright Year	1991
Abstract	We address the statistical filtering problem in dynamical models with jumps. When a particular application is adequately modeled by linear and Gaussian probability density functions with jumps, a usual method consists in approximating the optimal Bayesian estimate [in the sense of the minimum mean square error (MMSE)] in a linear and Gaussian jump Markov state space system (JMSS). Practical solutions include algorithms based on numerical approximations or on sequential Monte Carlo (SMC) methods. In this paper, we propose a class of alternative methods which consists in building statistical models which, locally, similarly model the problem of interest, but in which the computation of the MMSE estimate can be be computed exactly (without numerical nor SMC approximations) and at a computational cost which is linear in the number of observations.
Sponsorship	IEEE Signal Processing Society
Starting Page	3643
Ending Page	3653
Page Count	11
File Size	4188622
File Format	PDF
ISSN	1053587X
Volume Number	62
Issue Number	14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Hidden Markov models Approximation methods Probability density function Markov processes Numerical models Bayes methods pairwise Markov chains Conditional pairwise Markov chains exact Bayesian filtering hidden Markov chains jump Markov state space systems NP-hard problems
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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