• DocumentCode
    2077666
  • Title

    Least-informative Bayesian prior distributions for finite samples based on information theory

  • Author

    Spall, James C. ; Hill, Stacy D.

  • Author_Institution
    Appl. Phys. Lab., Johns Hopkins Univ., Laurel, MD, USA
  • fYear
    1989
  • fDate
    13-15 Dec 1989
  • Firstpage
    2567
  • Abstract
    A procedure, based on Shannon information theory, for producing least-informative prior distributions for Bayesian estimation and identification is presented. This approach relies on constructing an optimal mixture distribution and applies in small sample sizes. The procedure is illustrated in a small-scale numerical study and contrasted with an approach based on maximum entropy
  • Keywords
    Bayes methods; estimation theory; identification; information theory; Bayesian estimation; Shannon information theory; finite samples; identification; least-informative prior distributions; maximum entropy; optimal mixture distribution; Automatic control; Bayesian methods; Data analysis; Entropy; Information analysis; Information theory; Laboratories; Physics; Probability distribution; Research and development;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Decision and Control, 1989., Proceedings of the 28th IEEE Conference on
  • Conference_Location
    Tampa, FL
  • Type

    conf

  • DOI
    10.1109/CDC.1989.70640
  • Filename
    70640
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2077666