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Markov blanket information


In a Bayesian network, the Markov boundary of node A includes its parents, children and the other parents of all of its children.

In statistics and machine learning, when one wants to infer a random variable with a set of variables, usually a subset is enough, and other variables are useless. Such a subset that contains all the useful information is called a Markov blanket. If a Markov blanket is minimal, meaning that it cannot drop any variable without losing information, it is called a Markov boundary. Identifying a Markov blanket or a Markov boundary helps to extract useful features. The terms of Markov blanket and Markov boundary were coined by Judea Pearl in 1988.[1] A Markov blanket can be constituted by a set of Markov chains.

  1. ^ Pearl, Judea (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Representation and Reasoning Series. San Mateo CA: Morgan Kaufmann. ISBN 0-934613-73-7.

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conditionally independent of the entire network, given its Markov blanket. The related Causal Markov (CM) condition states that, conditional on the set of...

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graph in which each node of the original G is now connected to its Markov blanket. The name stems from the fact that, in a moral graph, two nodes that...

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approximate distribution at each node through messages in the node's Markov blanket. Given some set of hidden variables H {\displaystyle H} and observed...

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, T. Parr, E. Palacios, K. Friston and J. Kiverstein. (2018). The Markov blankets of life: autonomy, active inference and the free energy principle....

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growth – Statistical test and estimation methods IEC 61165 Application of Markov techniques IEC 61167 Metal halide lamps – Performance specification IEC...

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