Importance sampling for stochastic simulations
Management Science
Restart: a straightforward method for fast simulation of rare events
WSC '94 Proceedings of the 26th conference on Winter simulation
Analysis of an importance sampling estimator for tandem queues
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Fast simulation of rare events in queueing and reliability models
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A comparison of RESTART implementations
Proceedings of the 30th conference on Winter simulation
Efficient simulation of a tandem Jackson network
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Multilevel Splitting for Estimating Rare Event Probabilities
Operations Research
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
On the efficiency of RESTART for multidimensional state systems
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Splitting for rare-event simulation
Proceedings of the 38th conference on Winter simulation
Rare events, splitting, and quasi-Monte Carlo
ACM Transactions on Modeling and Computer Simulation (TOMACS)
System Modeling and Analysis: Foundations of System Performance Evaluation
System Modeling and Analysis: Foundations of System Performance Evaluation
Fundamentals of Queueing Theory
Fundamentals of Queueing Theory
| Hi-index | 0.00 |
Importance splitting is a simulation technique to estimate very small entrance probabilities for Markov processes by splitting sample paths at various stages before reaching the set of interest. This can be done in many ways, yielding different variants of the method. In this context, we propose a new one, called fixed number of successes. We prove unbiasedness for the new and some known variants, because in many papers, the proof is based on an incorrect argument. Further, we analyze its behavior in a simplified setting in terms of efficiency and asymptotics in comparison to the standard variant. The main difference is that it controls the imprecision of the estimator rather than the computational effort. Our analysis and simulation examples show that it is rather robust in terms of parameter choice and we present a two-stage procedure which also yields confidence intervals.