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Sequential updating of conditional probabilities on directed graphical structures
Author(s) -
Spiegelhalter David J.,
Lauritzen Steffen L.
Publication year - 1990
Publication title -
networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.977
H-Index - 64
eISSN - 1097-0037
pISSN - 0028-3045
DOI - 10.1002/net.3230200507
Subject(s) - graphical model , directed acyclic graph , computer science , dirichlet distribution , range (aeronautics) , algorithm , theoretical computer science , representation (politics) , graph , data mining , conditional probability , latent dirichlet allocation , mathematics , artificial intelligence , statistics , topic model , mathematical analysis , materials science , politics , political science , law , composite material , boundary value problem
A directed acyclic graph or influence diagram is frequently used as a representation for qualitative knowledge in some domains in which expert system techniques have been applied, and conditional probability tables on appropriate sets of variables form the quantitative part of the accumulated experience. It is shown how one can introduce imprecision into such probabilities as a data base of cases accumulates. By exploiting the graphical structure, the updating can be performed locally, either approximately or exactly, and the setup makes it possible to take advantage of a range of well‐established statistical techniques. As examples we discuss discrete models, models based on Dirichlet distributions and models of the logistic regression type.