IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v49y2022i5p1203-1234.html
   My bibliography  Save this article

MAP segmentation in Bayesian hidden Markov models: a case study

Author

Listed:
  • Alexey Koloydenko
  • Kristi Kuljus
  • Jüri Lember

Abstract

We consider the problem of estimating the maximum posterior probability (MAP) state sequence for a finite state and finite emission alphabet hidden Markov model (HMM) in the Bayesian setup, where both emission and transition matrices have Dirichlet priors. We study a training set consisting of thousands of protein alignment pairs. The training data is used to set the prior hyperparameters for Bayesian MAP segmentation. Since the Viterbi algorithm is not applicable any more, there is no simple procedure to find the MAP path, and several iterative algorithms are considered and compared. The main goal of the paper is to test the Bayesian setup against the frequentist one, where the parameters of HMM are estimated using the training data.

Suggested Citation

  • Alexey Koloydenko & Kristi Kuljus & Jüri Lember, 2022. "MAP segmentation in Bayesian hidden Markov models: a case study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(5), pages 1203-1234, April.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:5:p:1203-1234
    DOI: 10.1080/02664763.2020.1858273
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2020.1858273
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2020.1858273?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.

    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:japsta:v:49:y:2022:i:5:p:1203-1234. 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/CJAS20 .

    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.