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...

• Type: Conference
• Year: 1996
• Where: UAI
• Authors: Craig Boutilier, Nir Friedman, Moisés Goldszmidt, Daphne Koller
• Keywords: Bayesian Network Structure, Conditional Independence Properties, Effective Inference Algorithms, UAI 1996, UAI 2008