Change-point modelling in biological sequences via the bayesian adaptive independent sampler

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


    The change-point problem arises in wide variety of fields, including biomedical signal processing, speech and image processing, seismology, industry (e.g., fault detection) and financial mathematics. Multiple change-point models are also important in many biological applications and,particularly, in analysis of biomolecular sequences. We model genome sequences as a multiple change-point process, that is, a process in which the sequential data are separated into segments by an unknown number of change-points, with each segment supposed to have been generated by a different process. The parameters of the model are estimated by Adaptive Independent Samplers, which are adaptive Markov chain Monte Carlo methods based on the Independent Metropolis-Hastings algorithm. We discuss results of numerical experiments comparing different computing schemes.
    Original languageEnglish
    Title of host publicationComputer communication and management
    Subtitle of host publicationselected, peer reviewed papers from the 2011 International Conference on Computer Communication and Management (ICCCM 2011), May 2-3, Sydney Australia
    EditorsZhang Ting
    Place of PublicationSydney
    PublisherIACSIT Press
    Number of pages5
    ISBN (Print)9789810886363
    Publication statusPublished - 2011
    EventInternational Conference on Telecommunication Technology and Applications - Sydney
    Duration: 2 May 20113 May 2011

    Publication series

    NameInternational Proceedings of Computer Science and Information Technology
    PublisherIACSIT Press
    ISSN (Print)2010-460X


    ConferenceInternational Conference on Telecommunication Technology and Applications


    • Markov chain Monte Carlo
    • adaptive methods
    • multiple change-point problem
    • comparative genomics


    Dive into the research topics of 'Change-point modelling in biological sequences via the bayesian adaptive independent sampler'. Together they form a unique fingerprint.

    Cite this