# Example 41 is atypical there we started with the

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Unformatted text preview: = 0, 1, 2, . . . In this chapter we will extend the notion to a continuous time parameter t 0, a setting that is more convenient for some applications. In discrete time we formulated the Markov property as: for any possible values of j, i, in 1 , . . . i0 P (Xn+1 = j |Xn = i, Xn 1 = in 1 , . . . , X0 = i0 ) = P (Xn+1 = j |Xn = i) In continuous time, it is technically di cult to deﬁne the conditional probability given all of the Xr for r s, so we instead say that Xt , t 0 is a Markov chain if for any 0 s0 &lt; s1 · · · &lt; sn &lt; s and possible states i0 , . . . , in , i, j we have P (Xt+s = j |Xs = i, Xsn = in , . . . , Xs0 = i0 ) = P (Xt = j |X0 = i) In words, given the present state, the rest of the past is irrelevant for predicting the future. Note that built into the deﬁnition is the fact that the probability of going from i at time s to j at time s + t only depends on t the di↵erence in the times. Our ﬁrst step is to construct a large collection of examples. In Example 4.6 we will see that this is almost the general case. Example 4.1. Let N (t), t 0 be a Poisson process with rate and let Yn be a discrete time Markov chain wit...
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## This document was uploaded on 03/06/2014 for the course MATH 4740 at Cornell University (Engineering School).

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