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In many stochastic models, the assumption of independence among random variables is not plausible.
Therefore, many stochastic models for single-species or multi-species have been developed [26 30].
Many stochastic models for epidemic populations have been developed in [6 17].
In order to take random factors into account, many stochastic models were proposed and various achievements were obtained; see [5, 9 13] and references therein.
The number of states in the model and processor used previously in many stochastic models is presented in the table which can be helpful for quick selection of the model for any particular application.
Many stochastic models of the type (11) which arise in physics need to satisfy the constraint that the process ((X_{t})) is valued in the hypercube, say, ([0,1]^{k}).
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22 Just as there may be many plausible topological models to fit a particular degree distribution, there are many plausible stochastic models of network evolution that could generate a given topology.
On the other hand when a population of cells is modeled by averaging many stochastic simulations, the model converges to a steady state distribution.
Recently, many authors have studied stochastic models with white noise, such as [11, 22, 23].
As we know, the limiting behavior for the maximum of weighted sums (max_{1leq mleq n}sum_{i=1}^{m} b_{ni}X_{ni}) is very useful in many probabilistic derivations and stochastic models.
Many researchers use dynamic stochastic models in which simulated data based on various distributions is represented as differential equations.
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