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It is possible to show that the estimator obtained by this method has smaller variance than the one corresponding to a crude Monte Carlo sample Much effort is needed to compute the rst index r, this depending on the effort needed to compute (x (k) ), as it is necessary to determine when the network becomes operational as the links are repaired one by one In the worst case, and using a depth rst search for computing , the complexity is O(|E| max(|V|, |E|)) Nevertheless, for the case of the two-terminal metric, it is possible to determine the value of r with a computation cost similar to a single computation of , so that this method obtains a single sample at a cost similar to that of the crude method As already mentioned, the sequential destruction method is very similar; in the case of very low-reliability systems, it may exhibit better performance in computation terms, as the value of r would be determined in fewer iterations than needed by the sequential construction method Other methods based in graph evolution models have been published in [29] In particular, three methods are discussed in that work: destruction processes, construction processes, and merge processes All the methods rely on constructing a Markov chain (Y (t)) such that at time t = 1 we have that P( (Y (1)) = 1) = P( (X) = 1), so that computing the expectation of (Y (1)) also gives the reliability R Then the algorithm samples a permutation of the order at which links go up (in a creation or merge process) or down (in a destruction process), it identi es the critical link (that which causes the system to change from a down to an up state, or vice versa), and it computes (using a convolution of exponential random variables) the exact conditional probability that at time 1 the critical link will have changed its state In the case of merge processes, they improve on previous ideas identifying irrelevant links and partitions of the subjacent network In these methods, the sample complexity is higher, of order O(|E|2 ), but the variance is much smaller than the one obtained by the sequential construction method An important result is that, for xed E, the merge processes method has coef cient of variation uniformly bounded for all values of links reliabilities Another related work is [36], which gives a hybrid variant of crude Monte Carlo and graph evolution models, completed with the use of importance sampling to further speed up the simulations More recently, Hui et al [37] have applied cross-entropy techniques to improve the performance of crude Monte Carlo and graph evolution methods,.

java data matrix barcode reader

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java data matrix reader

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java data matrix reader