Populations of models, experimental designs and coverage of parameter space by Latin Hypercube and Orthogonal sampling

Burrage, Kevin, Burrage, Pamela, Donovan, Diane and Thompson, Bevan (2015). Populations of models, experimental designs and coverage of parameter space by Latin Hypercube and Orthogonal sampling. In: International Conference On Computational Science, ICCS 2015 — Computational Science at the Gates of Nature. International Conference On Computational Science, ICCS 2015, Reykjavik, Iceland, (1762-1771). 1-3 June 2015. doi:10.1016/j.procs.2015.05.383


Author Burrage, Kevin
Burrage, Pamela
Donovan, Diane
Thompson, Bevan
Title of paper Populations of models, experimental designs and coverage of parameter space by Latin Hypercube and Orthogonal sampling
Conference name International Conference On Computational Science, ICCS 2015
Conference location Reykjavik, Iceland
Conference dates 1-3 June 2015
Convener Scientific Programme Committee of ICCS 2015
Proceedings title International Conference On Computational Science, ICCS 2015 — Computational Science at the Gates of Nature   Check publisher's open access policy
Journal name Procedia Computer Science   Check publisher's open access policy
Place of Publication Amsterdam, Netherlands
Publisher Elsevier
Publication Year 2015
Sub-type Fully published paper
DOI 10.1016/j.procs.2015.05.383
Open Access Status DOI
ISSN 1877-0509
Volume 51
Issue 1
Start page 1762
End page 1771
Total pages 10
Collection year 2016
Language eng
Formatted Abstract/Summary
In this paper we have used simulations to make a conjecture about the coverage of a t dimensional subspace of a d dimensional parameter space of size n when performing k trials of Latin Hypercube sampling. This takes the form P(k,n,d,t) = 1-e-k/n^t-1. We suggest that this coverage formula is independent of d and this allows us to make connections between building Populations of Models and Experimental Designs. We also show that Orthogonal sampling is superior to Latin Hypercube sampling in terms of allowing a more uniform coverage of the t dimensional subspace at the sub-block size level. These ideas have particular relevance when attempting to perform uncertainty quantification and sensitivity analyses.
Keyword Latin Hypercube sampling
Orthogonal sampling
Population of models
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Conference Paper
Collections: School of Mathematics and Physics
Official 2016 Collection
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 2 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 01 Sep 2015, 12:05:48 EST by System User on behalf of Scholarly Communication and Digitisation Service