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A Gaussian Processes framework for the analysis of Experimental Data
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GPExp - A Gaussian Process framework for the analysis of Experimental Data

GPExp is a Matlab too useful to assess the quality of steady-state experimental results with regards to the provided inputs. It can help understanding which input variables are the most relevant, what is the repeatability of the experiments, which data points are likely to be outliers, etc.

GPExp is based on the GPML Matlab Library version 3.5 developped by Carl Edward Rasmussen and Christopher K. I. Williams.

It runs on Matlab® 7.x and later.

The code is released under the European Union Public Licence EUPL v1.1.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

Main developers:

  • Sylvain Quoilin (Joint Research Center, European Commission)
  • Jessica Schrouff (Stanford University)
  • Nicolas Huet (University of Liege)
  • Arnaud Legros (University of Liege)

This tool is part of the IWT SBO-110006 Project ‘‘The Next Generation Organic Rankine Cycles’’ (, funded by the Institute for the Promotion and Innovation by Science and Technology in Flanders (IWT).

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