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Efficient importance sampling in low dimensions using affine arithmetic

Everitt, R. G. (2018) Efficient importance sampling in low dimensions using affine arithmetic. Computational Statistics, 33 (1). pp. 1-29. ISSN 1613-9658

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To link to this item DOI: 10.1007/s00180-017-0729-z

Abstract/Summary

Despite the development of sophisticated techniques such as sequential Monte Carlo, importance sampling (IS) remains an important Monte Carlo method for low dimensional target distributions. This paper describes a new technique for constructing proposal distributions for IS, using affine arithmetic. This work builds on the Moore rejection sampler to which we provide a comparison.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Applied Statistics
ID Code:70160
Publisher:Springer

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