Questions of 'how best to acquire data'are essential to modelling and prediction in the
natural and social sciences, engineering applications, and beyond. Optimal experimental …

The design of multiple experiments is commonly undertaken via suboptimal strategies, such
as batch (open-loop) design that omits feedback or greedy (myopic) design that does not …

I have enjoyed very much writing this book. This is very important from a personal point of
view, but I believe this is the key of writing something interesting for the readers. One of the …

Optimal experimental design (OED) seeks experiments expected to yield the most useful
data for some purpose. In practical circumstances where experiments are time-consuming or …

After a short historical introduction, the properties and numerical methods are the focal point
of discussion. Construction of discrete and adaptive designs demand more extended …

To maximize the chance for success in an experiment, good experimental design is needed.
However, the presence of unique constraints may prevent mapping the experimental …

Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …

We describe the R package acebayes and demonstrate its use to find Bayesian optimal
experimental designs. A decision-theoretic approach is adopted, with the optimal design …

We review simulation based approaches to optimal design, with an emphasis on problems
that cast optimal design as formal decision problems. Under this perspective we approach …

… dans ce meilleur des [modèles] possibles ߪ tout est au mieux. Candide (1759), Chapitre I,
VOLTAIRE The working title of the book was a bit long, Optimality Theory of Experimental …