TY - JOUR

T1 - Sequential updating of conditional probabilities on directed graphical structures

AU - SPIEGELHALTER, DJ

AU - Lauritzen, Steffen L.

PY - 1990

Y1 - 1990

N2 - 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.

AB - 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.

U2 - 10.1002/net.3230200507

DO - 10.1002/net.3230200507

M3 - Journal article

VL - 20

SP - 579

EP - 605

JO - Networks

JF - Networks

SN - 0028-3045

IS - 5

ER -