Efficient importance sampling in low dimensions using affine arithmetic

Richard G. Everitt ()
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Richard G. Everitt: Whiteknights

Computational Statistics, 2018, vol. 33, issue 1, No 1, 29 pages

Abstract: Abstract Despite the development of sophisticated techniques such as sequential Monte Carlo (Del Moral et al. in J R Stat Soc Ser B 68(3):411–436, 2006), importance sampling (IS) remains an important Monte Carlo method for low dimensional target distributions (Chopin and Ridgway in Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation, 32:64–87, 2017). This paper describes a new technique for constructing proposal distributions for IS, using affine arithmetic (de Figueiredo and Stolfi in Numer Algorithms 37(1–4):147–158, 2004). This work builds on the Moore rejection sampler (Sainudiin in Machine interval experiments, Cornell University, Ithaca, 2005; Sainudiin and York in Algorithms Mol Biol 4(1):1, 2009) to which we provide a comparison.

Keywords: Importance sampling; Monte Carlo methods; Affine arithmetic (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s00180-017-0729-z

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