Dey, Subhrakanti and Mareels, Iven (2003) Reduced complexity estimation for large scale hidden Markov models. In: 2003 European Control Conference (ECC). IEEE, pp. 2613-2618. ISBN 9783952417379

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Abstract

In this paper, we address the problem of reduced-complexity estimation of general large-scale hidden Markov models with underlying nearly completely decomposable discrete-time Markov chains and finite-state outputs. An algorithm is presented that computes O(ε) (where e is the related weak coupling parameter) approximations to the aggregate and full-order filtered estimates with substantial computational savings. These savings are shown to be quite large when the chains have blocks with small individual dimensions. Some simulation studies are presented to demonstrate the performance of the algorithm.

Item Type:	Book Section
Additional Information:	Cite as: S. Dey and I. Mareels, "Reduced complexity estimation for large scale hidden Markov models," 2003 European Control Conference (ECC), 2003, pp. 2613-2618, doi: 10.23919/ECC.2003.7086435.
Keywords:	hidden Markov models; state estimation; computational complexity; Markov chains; nearly completely decomposable;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	14444
Identification Number:	https://doi.org/10.23919/ECC.2003.7086435
Depositing User:	Subhrakanti Dey
Date Deposited:	19 May 2021 16:46
Publisher:	IEEE
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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