Editing Markov blanket (section)

{{Short description|Subset of variables that contains all the useful information}}
[[Image:Diagram of a Markov blanket.svg|frame|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.<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 |url-access=registration |url=https://archive.org/details/probabilisticrea00pear }}</ref> A Markov blanket can be constituted by a set of [[Markov chain]]s.<!--[[Markov chain#Testing]]-->