Without-replacement sampling for particle methods on finite state spaces

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 signi...

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Veröffentlicht in:	Statistics and computing 2018-05, Vol.28 (3), p.633-652
Hauptverfasser:	Shah, Rohan, Kroese, Dirk P.
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial Intelligence Combinatorial analysis Computer simulation Mathematics and Statistics Monte Carlo simulation Particle methods (mathematics) Probabilistic methods Probability and Statistics in Computer Science Sampling methods State space models Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs
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description	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.
doi_str_mv	10.1007/s11222-017-9752-8
format	Article
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subjects	Artificial Intelligence Combinatorial analysis Computer simulation Mathematics and Statistics Monte Carlo simulation Particle methods (mathematics) Probabilistic methods Probability and Statistics in Computer Science Sampling methods State space models Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs
title	Without-replacement sampling for particle methods on finite state spaces
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