Propagation of variances in belief networks

Richard E. Neapolitan*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    2 Scopus citations

    Abstract

    The belief network is a graphical structure for representing independencies in a joint probability distribution. The methods which perform probabilistic inference in belief networks often treat the conditional probabilities which are stored in the network as certain values. If one takes either a subjectivistic or a limiting frequency approach to probability, one can never be certain of probability values. An algorithm should not only be capable of reporting the probabilities of the outcomes of remaining nodes when other nodes are instantiated, it should also be capable of reporting the uncertainty in these probabilities relative to the uncertainty in the probabilities which are stored in the network. In this paper a method for determining the variances in inferred probabilities is obtained under the assumption that a prosterior distribution on the uncertainty variables can be approximated by the prior distribution. This assumption is plausible if there is a reasonable amount of confidence in the probabilities stored in the network.

    Original languageEnglish (US)
    Pages (from-to)333-344
    Number of pages12
    JournalProceedings of SPIE - The International Society for Optical Engineering
    Volume1468
    Issue numberpt 1
    StatePublished - Jan 1 1991

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Condensed Matter Physics

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