Hierarchical Bayesian Regression (HBR) and Analytic Gradient Calculation (GCGP)

Aderhold, A. and Husmeier, D. and Grzegorczyk, M. (2016) Hierarchical Bayesian Regression (HBR) and Analytic Gradient Calculation (GCGP). [Data Collection]

Original publication URL: http://dx.doi.org/10.1515/sagmb-2014-0041

Collection description

The software contains two components: The HBR code and the gradient calculation code (GCGP) that uses the Gaussian Process package GPstuff (http://research.cs.aalto.fi/pml/software/gpstuff/) by:
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, and Aki Vehtari (2013). GPstuff: Bayesian Modeling with Gaussian Processes. Journal of Machine Learning Research, 14(Apr):1175-1179
The HBR code was originally written by Marco Grzegorczyk, and adapted by Andrej Aderhold for the Biopepa data. It is located in the subfolder 'HBR/', see Section A. for details.
The GCGP code was written by Andrej Aderhold, and uses sub-routines from the GPstuff, version 4.4 software that is under the GNU GPL license. It is located in the subfolder GCGP, see Section B. for details.

College / School: College of Science and Engineering > School of Mathematics and Statistics
Date Deposited: 09 Sep 2016 08:13
Enlighten Publications URL: http://eprints.gla.ac.uk/120872/
Related resource URL: http://researchdata.gla.ac.uk/374/
Retention date: 9 September 2026
Funder's Name: Engineering & Physical Sciences Research Council (EPSRC), European Commission (EC)
URI: http://researchdata.gla.ac.uk/id/eprint/351

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Aderhold, A. and Husmeier, D. and Grzegorczyk, M. (2016); Hierarchical Bayesian Regression (HBR) and Analytic Gradient Calculation (GCGP)

University of Glasgow

10.5525/gla.researchdata.351

Retrieved: 2017-09-22

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