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Unformatted text preview: h transition probability u(i, j ). Then Xt = YN (t) is a continuous-time Markov chain. In words, Xt takes one jump according to u(i, j ) at each arrival of N (t). Why is this true? Intuitively, this follows from the lack of memory property of the exponential distribution. If Xs = i, then independent of what has happened in the past, the time to the next jump will be exponentially distributed with rate and will go to state j with probability u(i, j ). Discrete time Markov chains were described by giving their transition probabilities p(i, j ) = the probability of jumping from i to j in one step. In continuous 119 120 CHAPTER 4. CONTINUOUS TIME MARKOV CHAINS time there is no first time t > 0, so we introduce for each t > 0 a transition probability pt (i, j ) = P (Xt = j |X0 = i) To compute this for Example 4.1, we note that N (t) has a Poisson number of jumps with mean t, so pt (i, j ) = 1 X e t( n=0 t)n n u (i, j ) n! where un (i, j ) is the nth power of the transition probability u(i, j )....
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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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