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A statistical and mathematical structure with some hidden layer configurations, the Markov chain model can be interpreted as the simple Basyian network that is directly visible to the Spectator Basyian network. For supervised and supervised simulations, this model makes a remarkable contribution. Education for strengthening and for pattern recognition, i.e. groups, if two instances are taken into account. A and B and it has 4 transitions when the system is in A, so it can be viewed similarly, as a transition from B when a system is in B, it can be viewed as a transition from A (Figure 1.2). In this way, a transition matrix will be created that will define the probability of the transformation of the state. In this way, it states not only in two classes, but even without classes or classes, that the model can be created [3].