Markov chain in the stochastic process is widely used in the industrial problems particularly in the problem of determining the market share of products. In this paper we are going to extend the one in the random field so called the Markov Random Field and applied also in the market share problem with restriction the market is considered as a discrete lattice and Pott?s models are going to be used as the potential function. Metropolis sampler is going to be used to determine the stability condition.

For stochastic nonequilibrium dynamics like a Langevin equation for a colloidal particle or a master equation for discrete states, entropy production along a single trajectory is studied. It involves both genuine particle entropy and entropy production in the surrounding medium. The integrated sum of both Deltastot is shown to obey a fluctuation theorem <exp[-Deltastot]>=1 for arbitrary initial conditions and arbitrary time-dependent driving over a finite time interval.

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An approximation is derived for the expected time to extinction in a stochastic model for recurrent epidemics. Numerical illustrations indicate that the approximation is crude but that it has the correct order of magnitude. The quasi-stationary distribution plays an important role in the derivation. Approximations for the critical community size and of the persistence threshold are derived. Comments are made on the classical study by Bartlell (1956-1960).