Markov Networks, also known as Markov Random Fields, are graphical models used to represent the joint distribution of a set of variables. Unlike Bayesian Networks, they use undirected graphs to encode the dependencies, which makes them particularly useful for modeling scenarios where the direction of causality is not of interest or undetermined.