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Statistics and Computing

, Volume 28, Issue 3, pp 633–652 | Cite as

Without-replacement sampling for particle methods on finite state spaces

Article
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Abstract

Combinatorial estimation is a new area of application for sequential Monte Carlo methods. We use ideas from sampling theory to introduce new without-replacement sampling methods in such discrete settings. These without-replacement sampling methods allow the addition of merging steps, which can significantly improve the resulting estimators. We give examples showing the use of the proposed methods in combinatorial rare-event probability estimation and in discrete state-space models.

Keywords

Sequential Monte Carlo Sampling theory Rare-event simulation Network reliability 

Notes

Acknowledgements

This work was supported by the Australian Research Council Centre of Excellence for Mathematical & Statistical Frontiers, under grant number CE140100049. The authors would like to thank the reviewers for their valuable comments, which improved the quality of this paper.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Mathematics and PhysicsThe University of QueenslandBrisbaneAustralia

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