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

Leonardo Bottolo1, Marc Chadeau-Hyam, David I Hastie

  • 1Department of Epidemiology and Biostatistics, Imperial College London, UK. l.bottolo@imperial.ac.uk

Bioinformatics (Oxford, England)
|January 15, 2011
PubMed
Summary
This summary is machine-generated.

ESS++ is a C++ software for Bayesian variable selection in linear regression. This open-source tool efficiently handles large datasets and complex models, supporting community-driven enhancements.

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Area of Science:

  • Statistics
  • Computational Biology
  • Bioinformatics

Background:

  • ESS++ is a C++ software for Bayesian variable selection in linear regression.
  • The software is available under GNU license with documentation and compilation instructions.

Purpose of the Study:

  • To introduce ESS++, a novel C++ implementation for Bayesian variable selection in linear regression.
  • To present a robust tool for handling both standard and 'large p, small n' regression scenarios.

Main Methods:

  • Utilizes a fully Bayesian approach for variable selection.
  • Employs Evolutionary Monte Carlo as the core engine for predictor selection.
  • Designed in C++ for efficient computation and handling of large datasets.

Main Results:

  • ESS++ demonstrates effectiveness in variable selection for linear regression.
  • The software performs well across various data conditions, including 'large p, small n' cases.
  • Current version supports several hundred observations, thousands of predictors, and multiple responses.

Conclusions:

  • ESS++ provides a powerful, open-source solution for Bayesian variable selection.
  • The open-source nature facilitates community contributions and future improvements.
  • The software is suitable for complex regression analyses in various scientific fields.