Variable-order Markov model: Difference between revisions

Revision as of 09:27, 21 April 2007

Revision as of 09:27, 21 April 2007

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{{Wikify|date=April 2007}}

'''Variable order Markov (VOM)''' Models are an important class of models that extend the well known [[Markov Chain]] Models. In contrast to the [[Markov Chain Models]] Models, where each random variable in a sequence with a [[Markov property]] depends on a fixed number of [[random variable]]s, in VOM models this number of conditioning [[random variable]]s may vary based on the specific observed realization.

This realization sequence is often called the ''context'' and thus the VOM models are also called ''Context Trees'' [1]. The flexibility in the number of conditioning [[random variable]]s turns out to be of real advantage for many applications, such as statistical analysis, classification and prediction.