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Shue, L. and Dey, Subhrakanti (2002) Complexity Reduction in Fixed-Lag Smoothing for Hidden Markov Models. IEEE Transactions on Signal Processing, 50 (5). ISSN 1053-587X
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Abstract
In this paper, we investigate approximate smoothing
schemes for a class of hidden Markov models (HMMs), 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 also to obtain
systematically approximate full-order smoothed estimates with
computational savings and rigorous performance guarantees,
unlike many of the aggregation methods proposed earlier.
| Item Type: | Article |
|---|---|
| Keywords: | Hidden Markov model; nearly completely decomposable; reduced-complexity; slow–fast decomposition; state aggregation; |
| Academic Unit: | Faculty of Science and Engineering > Electronic Engineering Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 14414 |
| Identification Number: | 10.1109/78.995068 |
| Depositing User: | Subhrakanti Dey |
| Date Deposited: | 11 May 2021 14:06 |
| Journal or Publication Title: | IEEE Transactions on Signal Processing |
| Publisher: | Institute of Electrical and Electronics Engineers |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/14414 |
| 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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