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pro vyhledávání: '"Woods, David C"'
Optimization under uncertainty is important in many applications, particularly to inform policy and decision making in areas such as public health. A key source of uncertainty arises from the incorporation of environmental variables as inputs into co
Externí odkaz:
http://arxiv.org/abs/2401.12031
Autor:
Jackson, Samuel E., Woods, David C.
Statistical models typically capture uncertainties in our knowledge of the corresponding real-world processes, however, it is less common for this uncertainty specification to capture uncertainty surrounding the values of the inputs to the model, whi
Externí odkaz:
http://arxiv.org/abs/2305.05327
The aim of this work is to extend the usual optimal experimental design paradigm to experiments where the settings of one or more factors are functions. Such factors are known as profile factors, or as dynamic factors. For these new experiments, a de
Externí odkaz:
http://arxiv.org/abs/2110.09115
Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates
In sequential experiments, subjects become available for the study over a period of time, and covariates are often measured at the time of arrival. We consider the setting where the sample size is fixed but covariate values are unknown until subjects
Externí odkaz:
http://arxiv.org/abs/2005.13261
Autor:
Jackson, Samuel E., Woods, David C.
Computationally expensive simulators, implementing mathematical models in computer codes, are commonly approximated using statistical emulators. We develop and assess novel emulation methods for systems best modelled via a chain, series or network of
Externí odkaz:
http://arxiv.org/abs/1910.08003
In the assessment and selection of supersaturated designs, the aliasing structure of interaction effects is usually ignored by traditional criteria such as $E(s^2)$-optimality. We introduce the Summary of Effect Aliasing Structure (SEAS) for assessin
Externí odkaz:
http://arxiv.org/abs/1711.11488
Publikováno v:
Quality Engineering, 29, 91-103, 2017
The design of an experiment can be always be considered at least implicitly Bayesian, with prior knowledge used informally to aid decisions such as the variables to be studied and the choice of a plausible relationship between the explanatory variabl
Externí odkaz:
http://arxiv.org/abs/1606.05892
The first investigation is made of designs for screening experiments where the response variable is approximated by a generalised linear model. A Bayesian information capacity criterion is defined for the selection of designs that are robust to the f
Externí odkaz:
http://arxiv.org/abs/1601.08088
Autor:
Woods, David C.1 (AUTHOR) d.woods@soton.ac.uk, Biedermann, Stefanie2 (AUTHOR), Tommasi, Chiara3 (AUTHOR)
Publikováno v:
Statistical Papers. Aug2023, Vol. 64 Issue 4, p1015-1019. 5p.
Autor:
Bowman, Veronica E., Woods, David C.
Publikováno v:
SIAM/ASA Journal of Uncertainty Quantification, 4, 1323-1344, 2016
It is often desirable to build a statistical emulator of a complex computer simulator in order to perform analysis which would otherwise be computationally infeasible. We propose methodology to model multivariate output from a computer simulator taki
Externí odkaz:
http://arxiv.org/abs/1512.07451