In this section
@inproceedings{Olofsson:2018,
author = {Olofsson, S and Deisenroth, M and Misener, R},
publisher = {ICML},
title = {Design of experiments for model discrimination hybridising analytical and data-driven approaches},
url = {http://hdl.handle.net/10044/1/57157},
year = {2018}
}
TY - CPAPER
AB - Healthcare companies must submit pharmaceuti-cal drugs or medical devices to regulatory bodiesbefore marketing new technology. Regulatorybodies frequently require transparent and inter-pretable computational modelling to justify a newhealthcare technology, but researchers may haveseveral competing models for a biological sys-tem and too little data to discriminate betweenthe models. In design of experiments for modeldiscrimination, the goal is to design maximallyinformative physical experiments in order to dis-criminate between rival predictive models. Priorwork has focused either on analytical approaches,which cannot manage all functions, or on data-driven approaches, which may have computa-tional difficulties or lack interpretable marginalpredictive distributions. We develop a method-ology introducing Gaussian process surrogatesin lieu of the original mechanistic models. Wethereby extend existing design and model discrim-ination methods developed for analytical modelsto cases of non-analytical models in a computa-tionally efficient manner.
AU - Olofsson,S
AU - Deisenroth,M
AU - Misener,R
PB - ICML
PY - 2018///
TI - Design of experiments for model discrimination hybridising analytical and data-driven approaches
UR - http://hdl.handle.net/10044/1/57157
ER -