| 词条 | Causal Markov condition |
| 释义 |
The Markov condition, sometimes called the Markov assumption, for a Bayesian network states that every 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 is independent of its noneffects, given its direct causes.[1] 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. Notes1. ^{{cite journal |last1=Hausman |first1=D.M. |last2=Woodward |first2=J. |title=Independence, Invariance, and the Causal Markov Condition |journal=British Journal for the Philosophy of Science |volume=50 |issue=4 |pages=521–583 |date=December 1999 |doi= 10.1093/bjps/50.4.521|url=http://philosophy.wisc.edu/hausman/papers/bjps.pdf |format=PDF}} {{DEFAULTSORT:Causal Markov Condition}}{{statistics-stub}} 2 : Bayesian networks|Causality |
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