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)
“Man is the end of nature; nothing so easily organizes itself in every part of the universe as he; no moss, no lichen is so easily born; and he takes along with him and puts out from himself the whole apparatus of society and condition extempore, as an army encamps in a desert, and where all was just now blowing sand, creates a white city in an hour, a government, a market, a place for feasting, for conversation, and for love.”
—Ralph Waldo Emerson (1803–1882)