Efficient importance sampling in low dimensions using affine arithmetic

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

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Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/70160
Identification Number/DOI	10.1007/s00180-017-0729-z
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics > Applied Statistics
Publisher	Springer
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Date Deposited:	02 May 2017 11:12	Date item deposited into CentAUR
Last Modified:	23 Mar 2025 01:49	Date item last modified

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