Accessibility navigation

Efficient importance sampling in low dimensions using affine arithmetic

Lists
Tools

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

[img]
Preview
 Text (Open Access) - Published Version
· Available under License Creative Commons Attribution.
· Please see our End User Agreement before downloading.
1MB
[img] Text - Accepted Version
· Restricted to Repository staff only
2MB

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

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: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
ID Code:70160
Publisher:Springer
Download Statistics

Downloads

Downloads per month over past year

Altmetric
Deposit Details

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation

See also