convolution.m
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Computes (exactly) the probability that a sum of independent exponentially-distributed random variables, each with a distinct parameter, exceeds a given threshold.
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expt.m
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Returns samples from a truncated exponential distribution.
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f_bar.m
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Samples from the conditional density of repair times, given that at least one edge in every
path in the path set is not operational at time 1. Uses path_sampling.m.
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hetero_PMC.m
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Estimates the reliability of a certain network via permutation Monte Carlo. Uses S.m and convolution.m.
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IS_bounds.m
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Estimates the reliability of a network using importance sampling with bounds. Uses f_bar.m, path_sampling.m, and S.m.
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leap_evolve.m
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Estimates the reliability of a certain network via the leap-evolve algorithm. Uses S.m and convolution.m
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merge.m
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Applies the merge algorithm of Elperin et al. (1991). Is used in merge_process.m.
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merge_process.m
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Estimates the reliability of a certain network via the merge
algorithm. Uses merge.m.
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path_sampling.m
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Samples repair times from a set of paths. Uses expt.m.
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relbty_marginals (folder)
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Estimates the reliability of a network using Markov chain Monte Carlo and the splitting method.
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S.m
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Computes the time at which a network becomes operational (first and last nodes connected) given the times of repair.
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using_Sx.m
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Illustrates how to use S.m for a certain graph. Uses S.m.
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