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Descripción
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| Monte Carlo (MC) methods are widely used in signal processing and machine learning. A well-known class of MC methods is composed of importance sampling and its adaptive extensions (e.g., population Monte Carlo). In this paper, we introduce an adaptive importance sampler using a population of proposal densities. The novel algorithm dynamically optimizes the cloud of proposals, adapting them using information about the gradient and Hessian matrix of the target distribution. Moreover, a new kind of interaction in the adaptation of the proposal densities is introduced, establishing a trade-off between attaining a good performance in terms of mean square error and robustness to initialization. | |
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Internacional
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Si |
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Nombre congreso
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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
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Tipo de participación
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960 |
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Lugar del congreso
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Brisbane (Australia) |
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Revisores
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Si |
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ISBN o ISSN
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978-1-4673-6997-8 |
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DOI
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Fecha inicio congreso
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19/04/2015 |
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Fecha fin congreso
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24/04/2015 |
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Desde la página
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4075 |
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Hasta la página
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4079 |
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Título de las actas
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Proceedings of the 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |