AIMar 27, 2013
d-Separation: From Theorems to Algorithms
arXiv:1304.1505v116 citations
Originality Synthesis-oriented
AI Analysis
This provides a fast method for probabilistic inference in Bayesian networks, which is incremental as it builds on existing d-separation theory.
The paper tackles the problem of efficiently identifying all independencies implied by the topology of a Bayesian network, resulting in an algorithm that runs in O(|E|) time where E is the number of edges.
An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The algorithm runs in time O (l E l) where E is the number of edges in the network.