Markov Random Fields


Markov Random Fields (MRFs) are a type of probabilistic graphical model used in machine learning and computer vision to model complex dependencies between random variables. MRFs are defined by a graph where nodes represent random variables and edges represent the conditional dependencies between them. The joint probability distribution of the random variables is factorized into a product of potential functions, each of which depends on a subset of the variables. MRFs are used in a variety of applications, including image segmentation, object recognition, and natural language processing.


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