Complexity: knots, colourings and counting
Complexity: knots, colourings and counting
Restart: a straightforward method for fast simulation of rare events
WSC '94 Proceedings of the 26th conference on Winter simulation
A comparison of RESTART implementations
Proceedings of the 30th conference on Winter simulation
Convergence assessment techniques for Markov chain Monte Carlo
Statistics and Computing
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Probability in the Engineering and Informational Sciences
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)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Simulation and the Monte Carlo Method (Wiley Series in Probability and Statistics)
Monte Carlo Strategies in Scientific Computing
Monte Carlo Strategies in Scientific Computing
Sequential Monte Carlo for rare event estimation
Statistics and Computing
Approximating the tail of the Anderson-Darling distribution
Computational Statistics & Data Analysis
Small Variance Estimators for Rare Event Probabilities
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
Self-Avoiding Random Dynamics on Integer Complex Systems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
Markov chain importance sampling with applications to rare event probability estimation
Statistics and Computing
Fitting mixture importance sampling distributions via improved cross-entropy
Proceedings of the Winter Simulation Conference
Graph reductions to speed up importance sampling-based static reliability estimation
Proceedings of the Winter Simulation Conference
An importance sampling method based on a one-step look-ahead density from a Markov chain
Proceedings of the Winter Simulation Conference
Static Network Reliability Estimation via Generalized Splitting
INFORMS Journal on Computing
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We describe a new Monte Carlo algorithm for the consistent and unbiased estimation of multidimensional integrals and the efficient sampling from multidimensional densities. The algorithm is inspired by the classical splitting method and can be applied to general static simulation models. We provide examples from rare-event probability estimation, counting, and sampling, demonstrating that the proposed method can outperform existing Markov chain sampling methods in terms of convergence speed and accuracy.