DocumentCode
3010176
Title
Reduced-complexity smoothing for hidden Markov models
Author
Shue, L. ; Dey, Subhrakanti
Author_Institution
Centre for Signal Process., Nanyang Technol. Inst., Singapore
Volume
5
fYear
2000
fDate
2000
Firstpage
4697
Abstract
We investigate approximate smoothing schemes for a class of hidden Markov models (HMM), namely, HMMs with underlying Markov chains that are nearly completely decomposable. The objective is to obtain substantial computational savings. Our algorithm can not only be used to obtain aggregate smoothed estimates, but can be used to obtain systematically approximate full-order smoothed estimates with computational savings, unlike many of the aggregation methods proposed earlier
Keywords
hidden Markov models; matrix algebra; probability; smoothing methods; state estimation; Markov chains; aggregate smoothed estimates; approximate full-order smoothed estimates; approximate smoothing schemes; computational savings; reduced-complexity smoothing; Aggregates; Control systems; Engineering management; Environmental management; Hidden Markov models; Iterative methods; Signal processing algorithms; Smoothing methods; State estimation; Steady-state;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location
Sydney, NSW
ISSN
0191-2216
Print_ISBN
0-7803-6638-7
Type
conf
DOI
10.1109/CDC.2001.914669
Filename
914669
Link To Document