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ZXing ("Zebra Crossing") barcode scanning library for Java , Android .... The Barcode Scanner app can no longer be published, so it's unlikely any changes will ... In Section 441 we discussed the fact that, in some cases, the simulation technique can degrade when rarity increases, but the numerical values coming from the simulation run hides this phenomenon, leading the user to accept incorrect results We illustrated this by means of an example where rarity is parameterized by , and where in spite of the fact that RE is unbounded as 0, we will necessarily observe that RE suddenly becomes essentially constant, that is, independent of Of course, this is not a systematic fact appearing in these contexts, but it simply underlines the necessity of being careful if we observe this type of behavior Speci cally, as a diagnostic rule, the idea is to simulate (with small sample sizes) the network for different values of larger than in the original problem, that is, to simulate much less rare events, with a small and xed sample size, before running the real simulation if things seem to go well What is the incorrect behavior we try to detect We look to see whether the estimated relative variance seems rst to increase, then suddenly drops and stays xed This is due to the fact that important events (or paths, depending on the context) in terms of contribution to the variance (and to the estimation itself), are not sampled anymore This trend of regular growth and sudden drop is likely to be a good hint of rare event problems An illustration of this was provided by Example 1, Table 41 If we use a sample size n = 1000, ten times smaller than that used in Table 41, we observe the same phenomenon, always coherent with the formulas given in Section 441 We observed the same behavior with different con gurations This type of phenomenon does not appear in the case of the M/M/1/B model presented in Example 3 Increasing B (see Table 42), we observe uctuations of the relative error, but no trend similar to that exhibited before The diagnostic can hardly be conclusive in this model, as it was in the rst example This illustrates that the tests of the section are traditional rejection tests Let us now consider another example [1]: Example 5 Consider the discrete-time Markov chain X given in Figure 46 and de ne = E(K 1 K 1(X(k) = 1) | X(0) = 1) k=1. word pdf 417, word aflame upci, birt barcode4j, qr code microsoft word 2013, word ean 13, birt data matrix java ean 13 reader java barcode reader - Stack Overflow
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