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Suppose for now that all the chains have the same weight at any given level A population of N0 independent trajectories of the Markov process is created (their initial states can be either deterministic or generated independently from some initial distribution), and each trajectory is simulated until it enters the rst intermediate region B0 or until time T is reached, whichever occurs rst Let R0 be the number of trajectories that have managed to enter the rst intermediate region B0 before time T The fraction p0 = R0 /N0 is an unbiased estimate of P(A0 ) = P[T0 T ] At the next stage, N1 replicas (or offspring) of these R0 successful trajectories are created, so as to maintain a suf ciently large population; this is done by cloning some states if N1 > R0 or choosing them randomly otherwise Each new trajectory is simulated until it enters the second intermediate region B1 or until time T is reached, whichever occurs rst Again, the fraction p1 = R1 /N1 of the R1 successful trajectories that have managed to enter the second intermediate region B1 before time T is a natural estimate of P(A1 | A0 ) = P[T1 T | T0 T ] The procedure is repeated again until the last step, in which each trajectory is simulated until it enters the last (and critical) region Bn = B or until time T is reached, whichever occurs rst The fraction of the successful trajectories that have managed to enter the last (and critical) region Bn = B before time T is a natural estimate of P(An | An 1 ) = P[Tn T | Tn 1 T ] In other words, the probability of the rare event is estimated as the product of estimates of the transition probabilities from one intermediate region to the next intermediate region, where the transition probability at level k is estimated as the fraction pk = Rk /Nk of the number Rk of successful trajectories that have managed to enter the next ntermediate region before time T over the number Nk of trials If Rk = 0 at any given stage k, we de ne pk = 0 for all k > k It is worth noting that the resulting estimator is unbiased, although the successive estimates are dependent because the result at level k + 1 depends on the entrance states in region Bk [15, 26] Indeed, by induction, assuming that E[p0 pk 1 ] = p1 pk 1 with pk = P(Ak | Ak 1 ), we have E[p0 pk ] = E[p0 pk 1 E[pk | N0 , , Nk 1 , R0 , , Rk 1 ]] = E[p0 pk 1 (Nk 1 pk )/Nk 1 ] = p0 pk = All the implementations described below are also unbiased [27] The entrance distribution to Bk is the probability distribution k of X(Tk ), the rst entrance state into Bk , conditional on Tk T An important observation is that each of the Rk trajectories that hits Bk before T hits it for the rst time at a state having distribution k Using the same conditioning argument as in (32), one can see that for any measurable set C Bk , the proportion of these Rk trajectories that hit Bk for the rst time in C is a random variable (actually a ratio of two random variables) with expectation k (C) That is, the empirical distribution N of these Rk entrance states into Bk is an unbiased estimator of k (32). asp.net code 39 reader .NET Code-39 Barcode Reader for C#, VB.NET, ASP.NET Applications
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