Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graphoids: a qualitative framework for probabilistic inference
Graphoids: a qualitative framework for probabilistic inference
The Design and Analysis of Computer Algorithms
The Design and Analysis of Computer Algorithms
Directed cyclic graphical representations of feedback models
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Modeling discrete interventional data using directed cyclic graphical models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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Although the concept of d-separation was originally defined for directed acyclic graphs (see Pearl 1988), there is a natural extension of the concept to directed cyclic graphs. When exactly the same set of d-separation relations hold in two directed graphs, no matter whether respectively cyclic or acyclic, we say that they are Markov equivalent. In other words, when two directed cyclic graphs are Markov equivalent, the set of distributions that satisfy a natural extension of the Global Directed Markov Condition (Lauritzen et al. 1990) is exactly the same for each graph. There is an obvious exponential (in the number of vertices) time algorithm for deciding Markov equivalence of two directed cyclic graphs; simply check all of the d-separation relations in each graph. In this paper I state a theorem that gives necessary and sufficient conditions for the Markov equivalence of two directed cyclic graphs, where each of the conditions can be checked in polynomial time. Hence, the theorem can be easily adapted into a polynomial time algorithm for deciding the Markov equivalence of two directed cyclic graphs. Although space prohibits inclusion of correctness proofs, they are fully described in Richardson (1994b).