25 citations · 83 across the 14 of their papers we have counts for
14 papers · 1 filter
Bayes Linear Analysis for Statistical Modelling with Uncertain Inputs
Samuel E. Jackson, David C. Woods
Statistical models typically capture uncertainties in our knowledge of the corresponding real-world processes, however, it is less common for this uncertainty specification to capt…
Optimal design of dynamic experiments for scalar-on-function linear models with application to a biopharmaceutical study
Damianos Michaelides, Maria Adamou, David C. Woods +1
A Bayesian optimal experimental design framework is developed for experiments where settings of one or more variables, referred to as profile variables, can be functions. For this…
Nonmyopic and pseudo-nonmyopic approaches to optimal sequential design in the presence of covariates
Mia S. Tackney, David C. Woods, Ilya Shpitser
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 th…
Bayes Linear Emulation of Simulator Networks
Samuel E. Jackson, David C. Woods
Computationally expensive simulators, implementing mathematical models in computer codes, are commonly approximated using statistical emulators. We develop and assess novel emulati…
Copula-based robust optimal block designs
W. G. Mueller, A. Rappold, D. C. Woods
Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modelling assumption for such experiments…
Summary of effect aliasing structure (SEAS): new descriptive statistics for factorial and supersaturated designs
Frederick Kin Hing Phoa, Yi-Hua Liao, David C. Woods +1
In the assessment and selection of supersaturated designs, the aliasing structure of interaction effects is usually ignored by traditional criteria such as -optimality. We…
Bayesian design of experiments for generalised linear models and dimensional analysis with industrial and scientific application
David C. Woods, Antony M. Overstall, Maria Adamou +1
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…
Model selection via Bayesian information capacity designs for generalised linear models
David C. Woods, James M. McGree, Susan M. Lewis
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 crit…
Emulation of multivariate simulators using thin-plate splines with application to atmospheric dispersion
Veronica E. Bowman, David C. Woods
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 m…
Design of Experiments for Screening
David C. Woods, Susan M. Lewis
The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to ex…
Bayesian optimal design for ordinary differential equation models with application in biological science
Antony Overstall, David Woods, Ben Parker
Bayesian optimal design is considered for experiments where the response distribution depends on the solution to a system of non-linear ordinary differential equations. The motivat…
Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model
Antony Overstall, David Woods
We present a common framework for Bayesian emulation methodologies for multivariate-output simulators, or computer models, that employ either parametric linear models or nonparamet…
Bayesian Design of Experiments using Approximate Coordinate Exchange
Antony Overstall, David Woods
The construction of decision-theoretic Bayesian designs for realistically-complex nonlinear models is computationally challenging, as it requires the optimization of analytically i…
Designs for generalized linear models with random block effects via information matrix approximations
Timothy W. Waite, David C. Woods
The selection of optimal designs for generalized linear mixed models is complicated by the fact that the Fisher information matrix, on which most optimality criteria depend, is com…