convolution.m	Computes (exactly) the probability that a sum of independent exponentially-distributed random variables, each with a distinct parameter, exceeds a given threshold.
expt.m	Returns samples from a truncated exponential distribution.
f_bar.m	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.
hetero_PMC.m	Estimates the reliability of a certain network via permutation Monte Carlo. Uses S.m and convolution.m.
IS_bounds.m	Estimates the reliability of a network using importance sampling with bounds. Uses f_bar.m, path_sampling.m, and S.m.
leap_evolve.m	Estimates the reliability of a certain network via the leap-evolve algorithm. Uses S.m and convolution.m
merge.m	Applies the merge algorithm of Elperin et al. (1991). Is used in merge_process.m.
merge_process.m	Estimates the reliability of a certain network via the merge algorithm. Uses merge.m.
path_sampling.m	Samples repair times from a set of paths. Uses expt.m.
relbty_marginals (folder)	Estimates the reliability of a network using Markov chain Monte Carlo and the splitting method.
S.m	Computes the time at which a network becomes operational (first and last nodes connected) given the times of repair.
using_Sx.m	Illustrates how to use S.m for a certain graph. Uses S.m.

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