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    Complexity Reduction in Fixed-Lag Smoothing for Hidden Markov Models

    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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    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:
    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
    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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