Dey, Subhrakanti and Mareels, Iven (2004) Reduced-complexity estimation for large-scale hidden Markov models. IEEE Transactions on Signal Processing, 52 (5). pp. 1242-1249. ISSN 1053-587X

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Abstract

In this paper, we address the problem of reduced-complexity estimation of general large-scale hidden Markov models (HMMs) with underlying nearly completely decomposable discrete-time Markov chains and finite-state outputs. An algorithm is presented that computes O(/spl epsi/) (where /spl epsi/ 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:	Article
Keywords:	Computational complexity; hidden Markov models; Markov chains; nearly completely decomposable; state estimation;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	14416
Identification Number:	https://doi.org/10.1109/TSP.2004.826171
Depositing User:	Subhrakanti Dey
Date Deposited:	11 May 2021 14:13
Journal or Publication Title:	IEEE Transactions on Signal Processing
Publisher:	Institute of Electrical and Electronics Engineers
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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