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
Issue Date: 13-Jan-2011
Date of Acceptance: 7-Dec-2010
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 (, 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
Keywords: Science & Technology
Life Sciences & Biomedicine
Physical Sciences
Biochemical Research Methods
Biotechnology & Applied Microbiology
Computer Science, Interdisciplinary Applications
Mathematical & Computational Biology
Statistics & Probability
Biochemistry & Molecular Biology
Computer Science
Bayes Theorem
Gene Expression Regulation
Linear Models
Models, Statistical
Programming Languages
Stochastic Processes
01 Mathematical Sciences
06 Biological Sciences
08 Information And Computing Sciences
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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