# 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]
Collins English Dictionary – Complete and Unabridged, 12th Edition 2014 © HarperCollins Publishers 1991, 1994, 1998, 2000, 2003, 2006, 2007, 2009, 2011, 2014
ThesaurusAntonymsRelated WordsSynonymsLegend:
 Noun 1 Markov chain - a Markov process for which the parameter is discrete time valuesMarkoff chainMarkoff 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
Based on WordNet 3.0, Farlex clipart collection. © 2003-2012 Princeton University, Farlex Inc.
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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.

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