Markov chain


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Markov chain

(ˈmɑːkɒf)
n
(Statistics) statistics a sequence of events the probability for each of which is dependent only on the event immediately preceding it
[C20: named after Andrei Markov (1856–1922), Russian mathematician]
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Noun1.Markov chain - a Markov process for which the parameter is discrete time values
Markoff process, Markov process - a simple stochastic process in which the distribution of future states depends only on the present state and not on how it arrived in the present state
References in periodicals archive ?
First let me explain Markov chains. And then explain why HCLOS delivers a better outcome.
In the Markov Chain for classification, the user activitys as a function of time elapse can be measured.
To handle this structural issue, several studies used weights in Markov chain models to improve model accuracy and precision [12-16].
We model the [Ca.sup.2+] channel by using the 3-state Markov chain of Figure 1(a), where C corresponds to the closed state, O to the open state, and B to the inactivated (blocked) state of the calcium channel [11].
A discrete-state, discrete-time Markov stochastic process is called a Markov chain.
In this paper, we consider the so-called gambler's ruin problem for a discrete-time Markov chain that converges to a Bessel process.
Secondly, the state space of the Markov process for the system with K queues of capacity C is [(C + 1).sup.K] such that a direct solution of the Markov chain is not numerically feasible for moderate C and K.
Particularly, the sequence of image pixels is modeled as an n-order Markov chain to capture the interpixel correlations.
Markov Chain Monte Carlo simulation technique is employed using Metropolis-Hasting algorithm to simulate the samples from the posterior distribution.
Elements from Markov chain theory are used in many domains, among which: physics, medicine, chemistry, economics, sociology, IT&C, data storage etc.