Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Directed cyclic graphical representations of feedback models
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Identifying independencies in causal graphs with feedback
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Cyclic causal models with discrete variables: Markov Chain equilibrium semantics and sample ordering
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Pearl and Dechter (1996) claimed that the d-separation criterion for conditional independence in acyclic causal networks also applies to networks of discrete variables that have feedback cycles, provided that the variables of the system are uniquely determined by the random disturbances. I show by example that this is not true in general. Some condition stronger than uniqueness is needed, such as the existence of a causal dynamics guaranteed to lead to the unique solution.