Abstract
What is in this chapter? We consider a sequence of random variables (Xn)n≥1 defined on the same probability space and having countably many possible values. We think of X n as being the state of a certain system at time n. Given that Xn-1 is in some state i then X n will be in some state j with a probability denoted by p(i, j); the transition probabilities p(i, j) are built in the model. The fact that given Xn-1 we may compute the distribution of X n (we do not need to know the X k for k < n - 1) is a particular case of the Markov property: the future depends on the present and on the past only through the present.
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