Markov Networks

In the domain of physics and probability, a Markov random field (often abbreviated as MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. A Markov random field is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic, whereas Markov networks are undirected and may be cyclic. Thus, a Markov network can represent certain dependencies that a Bayesian network cannot (such as cyclic dependencies); on the other hand, it can't represent certain dependencies that a Bayesian network can (such as induced dependencies).
Posts about Markov Networks
  • Direct Answers: Extracting Text from Pages Citations

    …), pages 5764, College Park, MD, June 1999. [3] R. C. Bunescu and R. J. Mooney. Collective information extraction with relational Markov networks (pdf). In Proceedings of the 42th Annual Meeting of the Association for Computational Linguistics (ACL-04), pages 439446, Barcelona, Spain, July 2004. [4] S. A. Caraballo. Automatic construction…

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