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

    …. A flexible learning system for wrapping tables and lists in HTML documents (pdf). In Proceedings of the 11th International World Wide Web Conference (WWW-02), pages 232241, Honolulu, HI, May 2002. (Presentation (PDF)) [7] K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer. Online passive-aggressive algorithm (pdf). Journal…

    Bill Slawski/ SEO by the Sea- 22 readers -
Get the top posts daily into your mailbox!