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UAI
1996

Context-Specific Independence in Bayesian Networks

13 years 5 months ago
Context-Specific Independence in Bayesian Networks
Bayesiannetworks provide a languagefor qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective inference algorithms. It is well-known, however, that there are certain independencies that we cannot capture qualitatively within the Bayesian network structure: independencies that hold only in certain contexts, i.e., given a specific assignment of values to certain variables. In this paper, we propose a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node. We present a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network. We then focus on a particular qualitative representation scheme--tree-structured CPTs-for capturing CSI. We suggest ways in which this representation can be used to support effectiv...
Craig Boutilier, Nir Friedman, Moisés Golds
Added 02 Nov 2010
Updated 02 Nov 2010
Type Conference
Year 1996
Where UAI
Authors Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller
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