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• W2949673356 abstract "Monte Carlo methods represent the textit{de facto} standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performance of any such method is strictly related to the specification of the proposal distribution, such that unfortunate choices easily wreak havoc on the resulting estimators. In this work, we introduce a textit{layered} (i.e., hierarchical) procedure to generate samples employed within a Monte Carlo scheme. This approach ensures that an appropriate equivalent proposal density is always obtained automatically (thus eliminating the risk of a catastrophic performance), although at the expense of a moderate increase in the complexity. Furthermore, we provide a general unified importance sampling (IS) framework, where multiple proposal densities are employed and several IS schemes are introduced by applying the so-called deterministic mixture approach. Finally, given these schemes, we also propose a novel class of adaptive importance samplers using a population of proposals, where the adaptation is driven by independent parallel or interacting Markov Chain Monte Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits of both IS and MCMC methods." @default.
• W2949673356 created "2019-06-27" @default.
• W2949673356 creator A5013196979 @default.
• W2949673356 creator A5016290792 @default.
• W2949673356 creator A5084123747 @default.
• W2949673356 creator A5085691944 @default.
• W2949673356 date "2015-05-01" @default.
• W2949673356 modified "2023-10-18" @default.
• W2949673356 title "LAYERED ADAPTIVE IMPORTANCE SAMPLING" @default.
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• W2949673356 hasPublicationYear "2015" @default.
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