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    A Modified Minibatch Sampling Method for Parameter Estimation in Hidden Markov Models using Stochastic Variational Bayes

    Files
    Majumder_PAMM2021.pdf (265.9Kb)
    Links to Files
    https://onlinelibrary.wiley.com/doi/abs/10.1002/pamm.202100203
    Permanent Link
    http://hdl.handle.net/11603/24664
    Collections
    • UMBC Faculty Collection
    • UMBC Mathematics and Statistics Department
    • UMBC Student Collection
    Metadata
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    Author/Creator
    Majumder, Reetam
    Gobbert, Matthias
    Neerchal, Nagaraj K.
    Author/Creator ORCID
    https://orcid.org/0000-0003-1745-2295
    Date
    2021-12-14
    Type of Work
    2 pages
    Text
    journal articles
    postprints
    Citation of Original Publication
    Majumder, R., Gobbert, M.K. and Neerchal, N.K. (2021), A Modified Minibatch Sampling Method for Parameter Estimation in Hidden Markov Models using Stochastic Variational Bayes. Proc. Appl. Math. Mech., 21: e202100203. https://doi.org/10.1002/pamm.202100203
    Rights
    This is the peer reviewed version of the following article: Majumder, R., Gobbert, M.K. and Neerchal, N.K. (2021), A Modified Minibatch Sampling Method for Parameter Estimation in Hidden Markov Models using Stochastic Variational Bayes. Proc. Appl. Math. Mech., 21: e202100203. https://doi.org/10.1002/pamm.202100203, which has been published in final form at https://doi.org/10.1002/pamm.202100203. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
    Access to this item will begin on 12/14/2022
    Subjects
    UMBC High Performance Computing Facility (HPCF)
    Abstract
    Parameter estimation using stochastic variatonal Bayes (SVB) under a mean field assumption can be carried out by sampling a single data point at each iteration of the optimization algorithm. However, when latent variables are dependent like in hidden Markov models (HMM), a larger sample is required at each iteration to capture that dependence. We describe a minibatch sampling procedure for HMMs where the emission process can be segmented into independent and identically distributed blocks. Instead of sampling a block and using all elements within it, we divide the block into subgroups and sample subgroups from different blocks using simple random sampling with replacement. Simulation results are provided for an HMM for precipitation data, where each block of 90 days represents 3 months of wet season data. SVB based on the proposed sampling method is shown to provide parameter estimates comparable with existing methods.


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.