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

    … of Machine Learning Research, 7:551585, 2006. [8] D. Freitag and N. Kushmerick. Boosted wrapper induction (pdf). In Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-00), pages 577583, Austin, TX, July 2000. [9] M. A. Hearst. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 15th International…

    Bill Slawski/ SEO by the Sea- 22 readers -
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