Environmental Engineering Reference
In-Depth Information
References
Kennedy M.C. and O'Hagan A.: Bayesian calibration of computer models. J. R. Stat. Soc. Ser. B
Stat. Methodology, 63, 425-464 (2001)
Rasmussen C.E. and Williams C.K.L.: Gaussian Processes for Machine Learning. MIT Press,
ISBN 026218253X www.GaussianProcess.org/gpml (1996)
Yu Y., Sokhi R.S., Kitwiroon N., Middleton D.R., Fisher B.: Performance characteristics of
MM5-SMOKE-CMAQ for a summer photochemical episode in southeast England, United
Kingdom. Atmospheric Environment, 42, 4870-4883 (2008)
3. Questions and Answers
Ferd Aauter (RIVM):
Is it possible to use this type of analysis when you are
dealing with spatially distributed uncertain parameters and complex, time-
consuming models?
Answer:
The purpose of an emulator is to make an uncertainty analysis possible
and practical when one is dealing with a complex model containing many
distributed uncertain parameters. In this case a full Monte-Carlo analysis is not
possible and alternative efficient methods need to be tried.
