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.
Famous quotes containing the words causal and/or condition:
“There is the illusion of time, which is very deep; who has disposed of it? Mor come to the conviction that what seems the succession of thought is only the distribution of wholes into causal series.”
—Ralph Waldo Emerson (1803–1882)
“In many places the road was in that condition called repaired, having just been whittled into the required semicylindrical form with the shovel and scraper, with all the softest inequalities in the middle, like a hog’s back with the bristles up.”
—Henry David Thoreau (1817–1862)