DEMOS: a system for discrete event modelling on Simula
DEMOS: a system for discrete event modelling on Simula
Importance sampling for stochastic simulations
Management Science
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Object-Oriented Programming with SIMULA
Object-Oriented Programming with SIMULA
Importance Sampling and the Cyclic Approach
Operations Research
Fast Simulation of Excessive Population Size in Tandem Jackson Networks
MASCOTS '04 Proceedings of the The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Numerical Methods for Structured Markov Chains (Numerical Mathematics and Scientific Computation)
Numerical Methods for Structured Markov Chains (Numerical Mathematics and Scientific Computation)
Rare Event Simulation using Monte Carlo Methods
Rare Event Simulation using Monte Carlo Methods
Asymptotic robustness of estimators in rare-event simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Importance sampling is a variance reduction technique that is particularly well suited for simulating rare events and, more specifically, estimating rare event probabilities. Properly applied, it often results in tremendous efficiency improvements compared to direct simulation schemes, but it can also yield unbounded variance increase. Its efficiency and robustness critically rely on a suitable change of the underlying probability measure, which is highly model-dependent. In recent years, significant progress greatly broadened the classes of models successfully accessible by importance sampling, but several model classes still require further investigation. We consider importance sampling simulations of finite capacity queues where interarrival and service times are Erlang distributed. A change of measure is proposed and experimentally studied. Numerical results for loss rates due to buffer overflows indicate that the change of measure provides accurate estimates and appears promising for adaptation to other models involving phase-type distributions.