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Descripción
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| Monte Carlo (MC) methods are widely used for statistical inference in signal processing applications. A well-known class of MC methods is importance sampling (IS) and its adaptive extensions. In this work, we introduce an iterated importance sampler using a population of proposal densities, which are adapted according to an MCMC technique over the population of location parameters. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples weighted according to the so-called deterministic mixture scheme. Numerical results, on a multi-modal example and a localization problem in wireless sensor networks, show the advantages of the proposed schemes. | |
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Internacional
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Si |
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Nombre congreso
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European Signal Processing Conference (EUSIPCO) |
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Tipo de participación
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960 |
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Lugar del congreso
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Niza (Francia) |
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Revisores
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Si |
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ISBN o ISSN
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978-0-9928626-3-3 |
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DOI
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Fecha inicio congreso
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31/08/2015 |
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Fecha fin congreso
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04/09/2015 |
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Desde la página
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499 |
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Hasta la página
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503 |
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Título de las actas
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Proceedings of the 23rd European Signal Processing Conference (EUSIPCO) |