Approximately optimal experimental design for heteroscedastic Gaussian process models

Alexis Boukouvalas, Dan Cornford, Milan Stehlik

Research output: Preprint or Working paperTechnical report


This paper presents a greedy Bayesian experimental design criterion for heteroscedastic Gaussian process models. The criterion is based on the Fisher information and is optimal in the sense of minimizing parameter uncertainty for likelihood based estimators. We demonstrate the validity of the criterion under different noise regimes and present experimental results from a rabies simulator to demonstrate the effectiveness of the resulting approximately optimal designs.
Original languageEnglish
Place of PublicationBirmingham
PublisherAston University
ISBN (Print)14
Publication statusUnpublished - 10 Nov 2009


  • Gaussian process
  • emulation
  • experimental design


Dive into the research topics of 'Approximately optimal experimental design for heteroscedastic Gaussian process models'. Together they form a unique fingerprint.

Cite this