{{Short description|Statistical tool to model changing systems}}
{{more citations needed|date=July 2017}}
In [[probability theory]], a '''Markov model''' is a [[stochastic model]] used to [[Mathematical model|model]] pseudo-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=1–256}}</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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{{main|Markov chain}}
The simplest Markov model is the [[Markov chain]]. It models the state of a system with a [[random variable]] that changes through time.<ref name=":0" /> In this context, the Markov property indicates that the distribution for this variable depends only on the distribution of a previous state. An example use of a Markov chain is [[Markov chain Monte Carlo]], which uses the Markov property to prove that a particular method for performing a [[random walk]] will sample from the [[joint distribution]].
