Markov Random Fields (MRFs) are a type of probabilistic graphical model used to represent the joint distribution of a set of random variables. They are particularly useful in situations where the variables exhibit spatial or contextual dependencies, allowing for the modeling of complex relationships in data. MRFs are defined by an undirected graph, where nodes represent random variables and edges indicate dependencies between them. In MRFs, the Markov property states that a variable is conditionally independent of all other variables given its neighbors. This property simplifies the computation of probabilities and makes MRFs suitable for applications in areas like computer vision, image processing, and statistical physics.