A Markov Blanket is a concept from probability theory and statistics that defines a set of nodes in a graphical model that shields a specific node from the influence of the rest of the network. More formally, for a given node , its Markov Blanket consists of its parents, children, and the parents of its children. This means that if you know the state of the Markov Blanket, the state of is conditionally independent of all other nodes in the network. This property is crucial in simplifying the computations in probabilistic models, allowing for effective learning and inference. The Markov Blanket can be particularly useful in fields like machine learning, where understanding the dependencies between variables is essential for building accurate predictive models.
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