The Markov condition for a Bayesian network states that any node in a Bayesian network is conditionally independent of its nondescendents, given its parents. A node is conditionally independent of the entire network, given its Markov blanket. The related causal Markov condition is that a phenomenon is independent of its noneffects, given its direct causes. In the event that the structure of a Bayesian network accurately depicts causality, the two conditions are equivalent.

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  • The Markov condition for a Bayesian network states that any node in a Bayesian network is conditionally independent of its nondescendents, given its parents. A node is conditionally independent of the entire network, given its Markov blanket. The related causal Markov condition is that a phenomenon is independent of its noneffects, given its direct causes. In the event that the structure of a Bayesian network accurately depicts causality, the two conditions are equivalent. However, a network may accurately embody the Markov condition without depicting causality, in which case it should not be assumed to embody the causal Markov condition.
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  • The Markov condition for a Bayesian network states that any node in a Bayesian network is conditionally independent of its nondescendents, given its parents. A node is conditionally independent of the entire network, given its Markov blanket. The related causal Markov condition is that a phenomenon is independent of its noneffects, given its direct causes. In the event that the structure of a Bayesian network accurately depicts causality, the two conditions are equivalent.
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  • Causal Markov condition
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