A diagnostic method that uses casual knowledge and linear programming in the application of bayes' formula

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abstract: Bayes' formula has been applied extensively in computer-based medical diagnostic systems. One assumption that is often made in the application of the formula is that the findings in a case are conditionally independent. This assumption is often invalid and leads to inaccurate posterior probability assignments to the diagnostic hypotheses. This paper discusses a method for using casual knowledge to structure findings according to their probabilistic dependencies. An inference procedure is discussed which propagates probabilities within a network of casually related findings in order to calculate posterior probabilities of diagnostic hypotheses. A linear programming technique is described that bounds the values of the propagated probabilities subject to known probabilistic constraints.

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AbstractBayes' formula has been applied extensivel Bayes' formula has been applied extensively in computer-based medical diagnostic systems. One assumption that is often made in the application of the formula is that the findings in a case are conditionally independent. This assumption is often invalid and leads to inaccurate posterior probability assignments to the diagnostic hypotheses. This paper discusses a method for using casual knowledge to structure findings according to their probabilistic dependencies. An inference procedure is discussed which propagates probabilities within a network of casually related findings in order to calculate posterior probabilities of diagnostic hypotheses. A linear programming technique is described that bounds the values of the propagated probabilities subject to known probabilistic constraints. ubject to known probabilistic constraints.
AuthorGregory F. Cooper  +
Bibtypetechreport  +
InstitutionKnowledge Systems, AI Laboratory  +
KeyKSL-86-09  +
NoteJournal Memo.  +
NumberKSL-86-09  +
TagComputer science  +
TitleA Diagnostic Method That Uses Casual Knowledge and Linear Programming in the Application of Bayes' Formula  +
Tr idKSL-86-09  +
Year1986  +
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