Continuous-time Markov chains in Visual Studio .NET

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We now examine how the previous framework applies to continuous-time Markov chains (CTMC) Following [13], let Y = {Y (t), t 0} be a CTMC evolving in Y up to some stopping time T = inf{t 0 : Y (t) }, where Y The initial distribution is 0 and the jump rate from y to z, for z = y, is ay,z Let ay = z=y ay,z be the departure rate from y The aim is to estimate E[X], where X = h(Y ) is a function of the entire sample path of the CTMC up to its stopping time T A sample path for this chain is determined uniquely by the sequence (Y0 , V0 , Y1 , V1 , , Y , V ) where Yj is the j th visited state of the chain, Vj the
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time spent in that state, and is the index of the jump that corresponds to the stopping time (the rst jump that hits ) Therefore h(Y ) can be re-expressed as h (Y0 , V0 , Y1 , V1 , , Yn , Vn ), and a sample path (y0 , v0 , y1 , v1 , , yn , vn ) has density (or likelihood)
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each term ayj ,yj +1 /ayj being the probability of moving from yj to yj +1 and ayj exp[ ayj vj ] the density for leaving yj after a sojourn time vj Then we have E[X] =
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where c : Y [0, ) Y [0, ) In this case, a standard technique that always reduces the variance, and often reduces the computations as well, is to replace the estimator X by
Xcmc = E[X | Y0 , , Y ] =
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c(Yj 1 , Yj ),
where c(Yj 1 , Yj ) = E[c (Yj 1 , Vj 1 , Yj ) | Yj 1 , Yj ] [9] In other words, we would never generate the sojourn times We are now back in our previous DTMC setting and the zero-variance transition probabilities are given again by (24) Consider now the case of a xed time horizon T , which therefore no longer has the form T = inf{t 0 : Y (t) } We then have two options: either we again reformulate the process as a DTMC, or retain a CTMC formulation In the rst case, we can rede ne the state as (Yj , Rj ) at step j , where Rj is the remaining clock time (until we reach time T ), as in [6] Then the zero-variance scheme is the same as for the DTMC setting if we replace the state Yj there by (Yj , Rj ), and if we rede ne We then have a non-denumerable state space, so the sums must be replaced by combinations of sums and integrals In this context of a nite time horizon, effective IS schemes will typically use non-exponential (often far from exponential) sojourn time distributions This means that we will no longer have a CTMC under IS Assume now that we want to stick with a
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CTMC formulation, and that we restrict ourselves to choosing a Markovian IS measure with new initial distribution 0 and new generator A such that 0 (y) > 0 (or ay,z > 0) whenever 0 (y) > 0 (or ay,z > 0) Let be the index j of the rst jump to a state Yj = Y (tj ) at time tj such that tj T Then, similarly to the discrete-time case, it can be shown that, provided is a stopping time with nite expectation under ( 0 , A), E[X] = E[XL ], with L the likelihood ratio given by 0 (Y0 ) L = 0 (Y0 )
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