A Markov Blanket is a concept from probability theory and statistics that defines a set of variables surrounding a specific variable in a graphical model. It includes the variable's parents, its children, and any other parents of its children. This blanket effectively shields the variable from the rest of the network, meaning that if you know the state of the Markov Blanket, you can predict the state of the variable without needing additional information. In simpler terms, the Markov Blanket provides a way to isolate a variable in a complex system, allowing for easier analysis and understanding. It is particularly useful in fields like machine learning and artificial intelligence, where models often involve many interconnected variables, such as in Bayesian networks or neural networks.