- The Markov Blanket of node A is the set of nodes composed of A’s parents, its children, and its children’s other parents (i.e., spouses).
- The Markov Blanket of node A contains all the nodes that, if we know their states, i.e., we have hard evidence for these nodes, will make A independent of all other nodes.
- This means that the Markov Blanket of node A is the only knowledge needed to predict the posterior probability distribution of that node.
- Learning a Markov Blanket selects the most relevant predictor nodes, which is particularly helpful when there are many variables in a data set. As a result, this can serve as a highly efficient variable selection method.