Technical reports

HasGP: A Haskell library for Gaussian process inference

Sean B. Holden

April 2016, 6 pages

DOI: 10.48456/tr-885

Abstract

HasGP is a library providing supervised learning algorithms for Gaussian process (GP) regression and classification. While only one of many GP libraries available, it differs in that it represents an ongoing exploration of how machine learning research and deployment might benefit by moving away from the imperative/object-oriented style of implementation and instead employing the functional programming (FP) paradigm. HasGP is implemented in Haskell and is available under the GPL3 open source license.

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BibTeX record

@TechReport{UCAM-CL-TR-885,
  author =	 {Holden, Sean B.},
  title = 	 {{HasGP: A Haskell library for Gaussian process inference}},
  year = 	 2016,
  month = 	 apr,
  url = 	 {https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-885.pdf},
  institution =  {University of Cambridge, Computer Laboratory},
  doi = 	 {10.48456/tr-885},
  number = 	 {UCAM-CL-TR-885}
}