IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v111y2016i513p200-215.html
   My bibliography  Save this article

Statistical Inference in Hidden Markov Models Using k -Segment Constraints

Author

Listed:
  • Michalis K. Titsias
  • Christopher C. Holmes
  • Christopher Yau

Abstract

Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward--backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-time dynamic programming recursions that, conditional on a user-specified constraint in the number of segments, allow us to (i) find MAP sequences, (ii) compute posterior probabilities, and (iii) simulate sample paths. We collectively call these recursions k -segment algorithms and illustrate their utility using simulated and real examples. We also highlight the prospective and retrospective use of k -segment constraints for fitting HMMs or exploring existing model fits. Supplementary materials for this article are available online.

Suggested Citation

  • Michalis K. Titsias & Christopher C. Holmes & Christopher Yau, 2016. "Statistical Inference in Hidden Markov Models Using k -Segment Constraints," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 200-215, March.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:513:p:200-215
    DOI: 10.1080/01621459.2014.998762
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2014.998762
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2014.998762?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Haeran Cho & Claudia Kirch, 2022. "Two-stage data segmentation permitting multiscale change points, heavy tails and dependence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(4), pages 653-684, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:jnlasa:v:111:y:2016:i:513:p:200-215. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.