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翻訳と辞書　辞書検索 [ 開発暫定版 ] スポンサード リンク Markov blanket ： ウィキペディア英語版 Markov blanket In machine learning, the Markov blanket for a node $A$ in a Bayesian network is the set of nodes $\backslash partial\; A$ composed of $A$'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 $MB(A)$. Every set of nodes in the network is conditionally independent of $A$ when conditioned on the set $\backslash partial\; A$, that is, when conditioned on the Markov blanket of the node $A$. The probability has the Markov property; formally, for distinct nodes $A$ and $B$: :$\backslash Pr(A\; \backslash mid\; \backslash partial\; A\; ,\; B)\; =\; \backslash Pr(A\; \backslash mid\; \backslash partial\; A).\; \backslash !$ 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 Pearl in 1988. 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. In a Markov random field, the Markov blanket for a node is simply its adjacent nodes. == See also == * Moral graph 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Markov blanket」の詳細全文を読む スポンサード リンク 翻訳と辞書 : 翻訳のためのインターネットリソース Copyright(C) kotoba.ne.jp 1997-2016. All Rights Reserved.