Fitting mixture importance sampling distributions via improved cross-entropy

Authors:
Tim J. Brereton;Joshua C. C. Chan;Dirk P. Kroese
Affiliations:
University of Queensland, Brisbane, Australia;Australian National University, Canberra, Australia;University of Queensland, Brisbane, Australia
Venue:
Proceedings of the Winter Simulation Conference
Year:
2011

Citing 5
Cited 2

FAST SIMULATION OF A QUEUE FED BY A SUPERPOSITION OF MANY (HEAVY-TAILED) SOURCES

Probability in the Engineering and Informational Sciences
Rare Event Simulation using Monte Carlo Methods

Rare Event Simulation using Monte Carlo Methods
Introduction to Rare Event Simulation

Introduction to Rare Event Simulation
Efficient Monte Carlo simulation via the generalized splitting method

Statistics and Computing
On large deviations theory and asymptotically efficient Monte Carlo estimation

IEEE Transactions on Information Theory

Small Variance Estimators for Rare Event Probabilities

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

In some rare-event settings, exponentially twisted distributions perform very badly. One solution to this problem is to use mixture distributions. However, it is difficult to select a good mixture distribution for importance sampling. We here introduce a simple adaptive method for choosing good mixture importance sampling distributions.