Separable and Transitive Graphoids

Citation: Dan Geiger and David Heckerman. (1990) Separable and Transitive Graphoids. In KSL-90-32, 1990.

Publication techreport ( Edit )
type	Technical Report
bibtype	techreport
Bibtex basics
author	Dan Geiger and David Heckerman
title	Separable and Transitive Graphoids
number	KSL-90-32
institution	Knowledge Systems, AI Laboratory
year	1990
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abstract	An important step in organizing a large body of knowledge is the grouping of related pieces of information into more or less independent chunks. In constructing large Bayesian networks from expert's judgments, this amounts to identifying the connected components of the network. Asking the expert directly whether variables x and y are connected may be a hard question to answer, since the expert may not have a clear global view of the network topology. However, the query: "does the value of x ever tell you anything about the value of y?" should evoke a more reliable judgment. This paper identifies the class of distributions, called separable, for which the answer to this question can safely be interpreted as an assertion about the connectivity of x and y, and argues that it is reasonable to assume these distributions in the construction of Bayesian networks. Normal and strictly-positive binary distributions are examples of separable distributions.

KSL Technical Report ID: KSL-90-32

Abstract	An important step in organizing a large bo … An important step in organizing a large body of knowledge is the grouping of related pieces of information into more or less independent chunks. In constructing large Bayesian networks from expert's judgments, this amounts to identifying the connected components of the network. Asking the expert directly whether variables x and y are connected may be a hard question to answer, since the expert may not have a clear global view of the network topology. However, the query: "does the value of x ever tell you anything about the value of y?" should evoke a more reliable judgment. This paper identifies the class of distributions, called separable, for which the answer to this question can safely be interpreted as an assertion about the connectivity of x and y, and argues that it is reasonable to assume these distributions in the construction of Bayesian networks. Normal and strictly-positive binary distributions are examples of separable distributions. s are examples of separable distributions.
Author	Dan Geiger and David Heckerman +
Bibtype	techreport +
Has author	Dan Geiger and David Heckerman +
Has identifier	KSL-90-32 +
Has publishing details	1990 +
Has title	Separable and Transitive Graphoids +
Has where published	KSL-90-32 +
Has year	1990 +
Institution	Knowledge Systems, AI Laboratory +
Ksl tr id	KSL-90-32 +
Number	KSL-90-32 +
Process note	YES +
Title	Separable and Transitive Graphoids +
Year	1990 +