Imprecise conjugate prior densities for the one-parameter exponential family of distributions

F.P.A. Coolen

    Research output: Book/ReportReportAcademic

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    Abstract

    A generalization of the standard Bayesian theory of statistical inference is presented for members of the one-parameter exponential family of distributions, such that imprecise prior densities are allowed. This enables representation of lack of perfect prior information about the probability distribution for the parameter of interest. A model is suggested with imprecise conjugate prior densities to enable simple updating of the prior densities if new data come available.
    Original languageEnglish
    Place of PublicationEindhoven
    PublisherTechnische Universiteit Eindhoven
    Number of pages26
    Publication statusPublished - 1991

    Publication series

    NameMemorandum COSOR
    Volume9136
    ISSN (Print)0926-4493

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