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The question therefore is: what is the validity of the proposed con dence interval The techniques presented in previous chapters (IS and splitting) consist of different ways to speed up the rare event occurrence, but dealing with the con dence interval coverage might still be an issue Consider now the classical M/M/1/B model, where we wish to evaluate = P(reaching B before 0 | N (0) = 1) (this is Example 3 in 2), N (t) being the number of customers at time t More formally: Example 3 Consider the discrete-time absorbing Markov chain X given in Figure 45 and de ne = P(X( ) = B | X(0) = 1) Observe that this is equal to P(reaching B before 0 | N (0) = 1) in the M/M/1/B queue with arrival rate and service rate , if p = /( + ) This is an elementary example in probability theory, and we know the answer: = (r 1 1)/(r B 1) if r = / = (1 p)/p = 1 (if = , that is, if p = 1/2, then = 1/B) Suppose that we want to estimate using the standard simulator In this example, rarity comes from the combination of values of the parameters p and B, the latter controlling the size of the model, a different situation than in previous example A typical line of analysis here involves xing p, varying B, and controlling rarity through = 1/B For instance, suppose that p = 04 and B = 40 The probability p is not very small, but combined with the size of the chain, we get 45 10 8 Suppose we try an IS scheme by simply changing the probability p into some p > 1/2, for instance, p = 09, and that we simulate n = 105 paths of the chain A standard implementation of this gave the approximate estimate 65 10 10 and estimated RE 40% Without knowing the exact value, it is dif cult to detect that there is a problem If we refer to the previous ideas, we can imagine the user increasing B (ie, increasing rarity), and looking at the behavior of the relative error In Table 42 we provide some numerical results obtained by keeping everything xed except B, which we increase The user may think that the RE looks bounded (while being pretty large), but observe that the exact value is never included in the observed con dence interval We can suspect the same problem as before, even if the numerical behavior is not exactly the same Looking again at the case of B = 40, it seems reasonable to try increasing the sample size Keeping everything xed except the sample size n = 106 , we get an estimate of 162 10 9 with a relative error 39% Again, we can suspect the same phenomenon as for the previous example. java ean 13 reader Java Barcode Reader Tutorial to scan, read linear, 2d barcodes in ...
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