The Library
Towards automatic model comparison : an adaptive sequential Monte Carlo approach
Tools
Zhou, Yan , Johansen, Adam M. and Aston, John A. D. (2016) Towards automatic model comparison : an adaptive sequential Monte Carlo approach. Journal of Computational and Graphical Statistics, 25 (3). pp. 701-726. doi:10.1080/10618600.2015.1060885 ISSN 1061-8600.
|
PDF
WRAP_Toward Automatic Model Comparison An Adaptive Sequential Monte Carlo Approach.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (880Kb) | Preview |
Official URL: http://dx.doi.org/10.1080/10618600.2015.1060885
Abstract
Model comparison for the purposes of selection, averaging and validation is a problem found throughout statistics. Within the Bayesian paradigm, these problems all require the calculation of the posterior probabilities of models within a particular class. Substantial progress has been made in recent years, but difficulties remain in the implementation of existing schemes. This paper presents adaptive sequential Monte Carlo (SMC) sampling strategies to characterise the posterior distribution of a collection of models, as well as the parameters of those models. Both a simple product estimator and a combination of SMC and a path sampling estimator are considered and existing theoretical results are extended to include the path sampling variant. A novel approach to the automatic specification of distributions within SMC algorithms is presented and shown to outperform the state of the art in this area. The performance of the proposed strategies is demonstrated via an extensive empirical study. Comparisons with state of the art algorithms show that the proposed algorithms are always competitive, and often substantially superior to alternative techniques, at equal computational cost and considerably less application-specific implementation effort.
Item Type: | Journal Article | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics | ||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||||
Library of Congress Subject Headings (LCSH): | Monte Carlo method, Bayesian statistical decision theory, Algorithms | ||||||||||
Journal or Publication Title: | Journal of Computational and Graphical Statistics | ||||||||||
Publisher: | American Statistical Association | ||||||||||
ISSN: | 1061-8600 | ||||||||||
Official Date: | 5 August 2016 | ||||||||||
Dates: |
|
||||||||||
Volume: | 25 | ||||||||||
Number: | 3 | ||||||||||
Page Range: | pp. 701-726 | ||||||||||
DOI: | 10.1080/10618600.2015.1060885 | ||||||||||
Status: | Peer Reviewed | ||||||||||
Publication Status: | Published | ||||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||||
Date of first compliant deposit: | 31 March 2017 | ||||||||||
Date of first compliant Open Access: | 31 March 2017 | ||||||||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC) | ||||||||||
Grant number: | EP/I017984/1, EP/K021672/1, EPSRC/HEFCE CRiSM grant |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year