markov random fields
Markov random fields (MRFs) are probabilistic models that represent a set of random variables as a graph, where each node represents a variable and each edge represents a dependency relationship. They are used to model complex systems where variables are interdependent and their joint distribution is characterized by a set of conditional probabilities. MRFs are particularly useful for analyzing spatial and relational data, as they capture both local and global dependencies among variables.
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