Variable-order Markov model
Markov-based processes with variable "memory" / From Wikipedia, the free encyclopedia
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In the mathematical theory of stochastic processes, 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, where each random variable in a sequence with a Markov property depends on a fixed number of random variables, in VOM models this number of conditioning random variables may vary based on the specific observed realization.
This realization sequence is often called the context; therefore the VOM models are also called context trees.[1] VOM models are nicely rendered by colorized probabilistic suffix trees (PST).[2] The flexibility in the number of conditioning random variables turns out to be of real advantage for many applications, such as statistical analysis, classification and prediction.[3][4][5]