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Diffusion estimation of mixture models with local and global parameters

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00461646" target="_blank" >RIV/67985556:_____/16:00461646 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/SSP.2016.7551775" target="_blank" >http://dx.doi.org/10.1109/SSP.2016.7551775</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/SSP.2016.7551775" target="_blank" >10.1109/SSP.2016.7551775</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Diffusion estimation of mixture models with local and global parameters

  • Original language description

    The state-of-art methods for distributed estimation of mixtures assume the existence of a common mixture model. In many practical situations, this assumption may be too restrictive, as a subset of parameters may be purely local, e.g., if the numbers of observable components differ across the network. To reflect this issue, we propose a new online Bayesian method for simultaneous estimation of local parameters, and diffusion estimation of global parameters. The algorithm consists of two steps. First, the nodes perform local estimation from own observations by means of factorized prior/posterior distributions. Second, a diffusion optimization step is used to merge the nodes' global parameters estimates. A simulation example demonstrates improved performance in estimation of both parameters sets.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    BD - Information theory

  • OECD FORD branch

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings of the 2016 IEEE Workshop on Statistical Signal Processing

  • ISBN

    978-1-4673-7802-4

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    362-366

  • Publisher name

    IEEE

  • Place of publication

    Palma de Mallorca, Španělsko

  • Event location

    Palma de Mallorca

  • Event date

    Jun 26, 2016

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000390840200071