Markov model: Difference between revisions

Revision as of 18:52, 17 June 2018

Revision as of 23:48, 7 October 2018

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{{refimprovemore citations needed|date=July 2017}}

In [[probability theory]], a '''Markov model''' is a [[stochastic model]] used to [[Mathematical model|model]] randomly changing systems.<ref name=":0">{{Cite book|title=Markov Chains: From Theory to Implementation and Experimentation|last=Gagniuc|first=Paul A.|publisher=John Wiley & Sons|year=2017|isbn=978-1-119-38755-8|___location=USA, NJ|pages=1-2561–256}}</ref> It is assumed that future states depend only on the current state, not on the events that occurred before it (that is, it assumes the [[Markov property]]). Generally, this assumption enables reasoning and computation with the model that would otherwise be [[Intractability (complexity)|intractable]]. For this reason, in the fields of [[predictive modelling]] and [[probabilistic forecasting]], it is desirable for a given model to exhibit the Markov property.

==Introduction==

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{{DEFAULTSORT:Markov Model}}

[[Category:Markov models| ]]