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Efficient importance sampling in low dimensions using affine arithmetic
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Everitt, Richard G. (2018) Efficient importance sampling in low dimensions using affine arithmetic. Computational Statistics & Data Analysis, 33 (1). pp. 1-29. doi:10.1007/s00180-017-0729-z ISSN 0167-9473.
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Official URL: http://dx.doi.org/10.1007/s00180-017-0729-z
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.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||
Journal or Publication Title: | Computational Statistics & Data Analysis | ||||||||
Publisher: | Elsevier Science Ltd | ||||||||
ISSN: | 0167-9473 | ||||||||
Official Date: | March 2018 | ||||||||
Dates: |
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Volume: | 33 | ||||||||
Number: | 1 | ||||||||
Page Range: | pp. 1-29 | ||||||||
DOI: | 10.1007/s00180-017-0729-z | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
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