Evidence evaluation: a study of likelihoods and independence

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    Abstract

    In the context of evidence evaluation, where the probability of evidence given a certain hypothesis is
    considered, different pieces of evidence are often combined in a naive way by assuming conditional
    independence. In this paper we present a number of results that can be used to assess both the
    importance of a reliable likelihood-ratio estimate and the impact of neglecting dependencies among
    pieces of evidence for the purpose of evidence evaluation. We analytically study the effect of
    changes in dependencies between pieces of evidence on the likelihood ratio, and provide both
    theoretical and empirical bounds on the error in likelihood occasioned by assuming independences
    that do not hold in practice. In addition, a simple measure of influence strength between pieces of
    evidence is proposed.
    Original languageEnglish
    Title of host publicationConference on Probabilistic Graphical Models, 6-9 September 2016, Lugano, Switzerland
    EditorsAlessandro Antonucci, Giorgio Corani, Cassio Polpo Campos
    PublisherMLResearchPress
    Pages426-473
    Publication statusPublished - 2016
    EventInternational Conference on Probabilistic Graphical Models - Lugano, Switzerland
    Duration: 6 Sept 20169 Sept 2016

    Publication series

    NameJournal of Machine Learning Research Workshop and Conference Proceedings
    PublisherMLResearchPress
    Volume52

    Conference

    ConferenceInternational Conference on Probabilistic Graphical Models
    Abbreviated titlePGM
    Country/TerritorySwitzerland
    CityLugano
    Period6/09/169/09/16

    Keywords

    • Evidence evaluation
    • independence violations
    • error in overall likelihood
    • influence measures

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