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Analysis of a splitting estimator for rare event probabilities in Jackson networks

2011
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Stochastic Systems
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We consider a standard splitting algorithm for the rare-event simulation of overflow probabilities in any subset of stations in a Jackson network at level n, starting at a fixed initial position. It was shown in [8] that a subsolution to the Isaacs equation guarantees that a subexponential number of function evaluations (in n) suffice to estimate such overflow probabilities within a given relative accuracy. Our analysis here shows that in fact O n 2β+1 function evaluations suffice to achieve a

doi:10.1214/11-ssy026
fatcat:dypdocszwvecrauubm32vzxevq