KSL-90-32 + redirect page
Separable and Transitive Graphoids + Has identifier
Separable and Transitive Graphoids + Ksl tr id
Separable and Transitive Graphoids + Number
| Separable and Transitive Graphoids |
Bibtype
techreport
Has publishing details
1990
Has title
Separable and Transitive Graphoids
Has where published
KSL-90-32
Has year
1990
Title
Separable and Transitive Graphoids
Year
1990
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 +
Has author
Dan Geiger and David Heckerman +
Has identifier
Separable and Transitive Graphoids +
Institution
Knowledge Systems, AI Laboratory +
Ksl tr id
Separable and Transitive Graphoids +
Number
Separable and Transitive Graphoids +
Process note
YES +
Categories KSL Technical Report +, Publication +, Technical Report +
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