Difference between revisions of "Markov Logic Networks"
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| − | This is a [[category::method]] that combines | + | This is a [[category::method]] that combines first-order logic and probabilistic graphical models in a single representation. A Markov Logic Network (or MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, enabling uncertain inference. Markov logic networks generalize first-order logic, in the sense that, in a certain limit, all unsatisfiable statements have a probability of zero, and all tautologies have probability one. |
== Relevant Papers == | == Relevant Papers == | ||
Revision as of 03:06, 21 September 2011
This is a method that combines first-order logic and probabilistic graphical models in a single representation. A Markov Logic Network (or MLN) is a probabilistic logic which applies the ideas of a Markov network to first-order logic, enabling uncertain inference. Markov logic networks generalize first-order logic, in the sense that, in a certain limit, all unsatisfiable statements have a probability of zero, and all tautologies have probability one.
