Gaussian process regression in 1D

Use Gaussian processes to represent x-y data

Launch Tool

You must login before you can run this tool.

Version 1.0 - published on 04 Dec 2014

doi:10.4231/D3CZ3259R cite this

View All Supporting Documents

Category

Tools

Published on

Abstract

Perform Gaussian process regression in x-y data. The code makes use of the excellent GPy package.

References

  1. Rasmussen, C. E. and C. K. I. Williams (2006). Gaussian processes for machine learning. Cambridge, Mass., MIT Press.
  2. The GPy authors, (2012--2014). "GPy: A Gaussian process framework in Python." from http://github.com/SheffieldML/GPy.

Cite this work

Researchers should cite this work as follows:

  • Ilias Bilionis; Alejandro Strachan; Benjamin P Haley; Martin Hunt; Rohit Kaushal Tripathy; Sam Reeve (2014), "Gaussian process regression in 1D," https://nanohub.org/resources/gptool. (DOI: 10.4231/D3CZ3259R).

    BibTex | EndNote

Tags

  1. uncertainty quantification
  2. regression
  3. Gaussian process
  4. Bayesian statistics
  5. Machine Learning