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| [[Image:Diagram of a Markov blanket.svg|frame|In a Bayesian network, the Markov blanket of node ''A'' includes its parents, children and the other parents of all of its children.]] | | Nice to satisfy you, I am Marvella Shryock. Her family members life in [http://Www.onlinedatingmagazine.com/STDs/STDadvice/treatablestds.html Minnesota]. Hiring is her day at home std testing occupation now but she's usually needed her personal company. One of the very best things in the world for me is to [http://www.newhealthguide.org/Types-Of-Bacteria.html std testing] at home do aerobics and I've been performing it over the counter std test for quite a whilst.<br><br>My blog [http://webs.galiciadigital.com/directorio/busqueda?title-web=Eye+Diseases+Signs+And+Symptoms+Leads+To+And+Types&field_url_pax_web_url=http%3A%2F%2Fstdtestguide.com%2Fstd-testing%2Fhome-std-test%2F&cat=All&tid=All at home std test] |
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| In [[machine learning]], the '''Markov blanket''' for a [[Vertex (graph theory)|node]] <math>A</math> in a [[Bayesian network]] is the set of nodes <math>\partial A</math> composed of <math>A</math>'s parents, its children, and its children's other parents. In a [[Markov network]], the Markov blanket of a node is its set of neighboring nodes. A Markov blanket may also be denoted by <math>MB(A)</math>.
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| Every set of nodes in the network is [[conditional independence|conditionally independent]] of <math>A</math> when conditioned on the set <math>\partial A</math>, that is, when conditioned on the Markov blanket of the node <math>A</math>. The probability has the [[Markov property]]; formally, for distinct nodes <math>A</math> and <math>B</math>:
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| :<math>\Pr(A \mid \partial A , B) = \Pr(A \mid \partial A). \!</math>
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| The Markov blanket of a node contains all the variables that shield the node from the rest of the network. This means that the Markov blanket of a node is the only knowledge needed to predict the behavior of that node. The term was coined by [[Judea Pearl| Pearl]] in 1988.<ref>{{cite book |last=Pearl |first=Judea |authorlink=Judea Pearl |title=Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference |publisher=Morgan Kaufmann |location=San Mateo CA |year=1988 |isbn=0-934613-73-7 | series=Representation and Reasoning Series}}</ref>
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| In a Bayesian network, the values of the parents and children of a node evidently give information about that node; however, its children's parents also have to be included, because they can be used to explain away the node in question.
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| == See also ==
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| * [[Moral graph]]
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| ==Notes==
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| <references/>
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| [[Category:Probability theory]]
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| [[Category:Bayesian networks]]
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| [[Category:Markov networks]]
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Latest revision as of 23:00, 28 November 2014
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