Descripción
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In this paper, we introduce multiple importance sampling (MIS) approaches with overlapping (i.e., non-disjoint) sets of proposals. We derive a novel weighting scheme, based on the deterministic mixture methodology, that leads to unbiased estimators. The proposed framework can be seen as a generalization of other well-known MIS algorithms available in the literature. Furthermore, it allows us to achieve any desired trade-off between the variance of the estimators and the computational complexity through the definition of the sets of proposals. Simulations using a bimodal target density show the good performance of the proposed approach. | |
Internacional
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Si |
Nombre congreso
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2016 IEEE Statistical Signal Processing Workshop (SSP) |
Tipo de participación
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970 |
Lugar del congreso
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Palma de Mallorca (España) |
Revisores
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Si |
ISBN o ISSN
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978-1-4673-7802-4 |
DOI
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10.1109/SSP.2016.7551744 |
Fecha inicio congreso
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26/06/2016 |
Fecha fin congreso
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29/06/2016 |
Desde la página
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1 |
Hasta la página
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5 |
Título de las actas
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Proceedings of the 2016 IEEE Statistical Signal Processing Workshop (SSP) |