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ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration

Title: ESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration
Authors: Bottolo, L
Chadeau-Hyam, M
Hastie, DI
Langley, SR
Petretto, E
Tiret, L
Tregouet, D
Richardson, S
Item Type: Journal Article
Abstract: Summary:ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ‘large p, small n’ case. In the current version, ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on Evolutionary Monte Carlo. Our implementation is open source, allowing community-based alterations and improvements. Availability: C++ source code and documentation including compilation instructions are available under GNU licence at http://bgx.org.uk/software/ESS.html.
Issue Date: 13-Jan-2011
Date of Acceptance: 7-Dec-2010
URI: http://hdl.handle.net/10044/1/57206
DOI: https://dx.doi.org/10.1093/bioinformatics/btq684
ISSN: 1367-4803
Publisher: Oxford University Press (OUP)
Start Page: 587
End Page: 588
Journal / Book Title: Bioinformatics
Volume: 27
Issue: 4
Copyright Statement: © The Author(s) 2011. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Sponsor/Funder: Biotechnology and Biological Sciences Research Council (BBSRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Medical Research Council (MRC)
Funder's Grant Number: BB/C519670/1
G0600609
G0801056B
G1002319
Keywords: Science & Technology
Life Sciences & Biomedicine
Technology
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
Mathematics
Algorithms
Bayes Theorem
Gene Expression Regulation
Linear Models
Models, Statistical
Programming Languages
Software
Stochastic Processes
01 Mathematical Sciences
06 Biological Sciences
08 Information And Computing Sciences
Bioinformatics
Publication Status: Published
Appears in Collections:Clinical Sciences
Molecular Sciences
Faculty of Medicine
Faculty of Natural Sciences
Epidemiology, Public Health and Primary Care



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