In the present chapter, the methodology for classificatio of the inside threat activitys using

Markov Chain Model is introduced.

In this study, we aimed to develop a new weighting method to address the structural difficulties in traditional

Markov chain while forecasting short-term drought states.

The deterministic description of the 3-state

Markov chain model for the CaV channel is given by the following ODE system:

It follows from [G.sup.u] that each

Markov chain has two modes, which means [gamma] = 2 and [eta] = [2.sup.3x5+3x4] = [2.sup.27], and the transition rate matrices are considered as in Table 1.

To simplify the writing, we use the following notations: Since a

Markov chain is a discrete-time process, we can see it as a sequence of random variables {[X.sub.0],[X.sub.1],...} where [X.sub.k] describes the situation at time t = k.

Actual driving data were collected to design the

Markov chain model.

The probability [p.sub.n] that the discrete-time

Markov chain defined in Section 2.1, starting from n, will hit N before 1 is given by (40) if a [not equal to] 1/2.

So, in contrast to the power series algorithm, our approach does not primarily aim for simplifying the solution of the

Markov chain but aims for obtaining the solution in a wide subset of the parameter space at once and relies on iterative procedures to do so.

Section 2 gives a review of the two security measures with the deterministic statistical distribution model and introduces the n-order

Markov chain model.

Metropolis-Hasting

Markov Chain Monte Carlo (MCMC) simulation algorithm is employed to draw samples from the high dimensional posterior distribution.

Markov chain models have been used by many authors to capture the influence on demand by factors such as weather, product age, economic conditions, and price competition.

The modeling of Short-Range Prediction for photovoltaic power plant by weighted

Markov ChainOn the one hand, these conditions include irreducibility and aperiodicity of the underlying graph of the

Markov chain, which can be checked easily for a given

Markov chain.

Statistical embedded

Markov Chain processes were used to analyze facies transitions and to determine the stacking pattern of the lithofacies of the Guaduas formation.

For the single latent dual redundant system we can ignore the discrete repair assumption (repair only on ground) and model the system with a continuous time

Markov chain model.