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This paper compares the performance, in terms of convergence rates and precision of the estimates, for six Monte Carlo Simulation sampling methods: Quasi-Monte Carlo using Halton, Sobol, and Faure numeric sequences; Descriptive Sampling, based on the use of deterministic sets and Latin Hypercube Sampling, based on stratified numerical sets. Those methods are compared to the classical Monte Carlo. The comparison was made for two basic risky applications: the first one evaluates the risk in adoi:10.1109/wsc.2002.1166440 dblp:conf/wsc/SalibyP02 fatcat:bk3kgbtjxzhwfk3lehl64i74ze