Context-Specific Independence in Bayesian Networks

Understand

Bayesian networks provide a language for 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.