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 …

This book is a follow up of" Theory of Optimal Designs" by KR Shah and Bikas K. Sinha
published in this series. Unlike the previous book, this one covers a wide range of topics in …

This article explores numerical methods for stochastic optimization, with special attention to
Bayesian design problems. A common and challenging situation occurs when the objective …

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 …

We consider decision problems defined by a utility function and an underlying probability
model for all unknowns. The utility function quantifies the decision maker's preferences over …

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 …

This paper reviews the literature on Bayesian experimental design. A unified view of this
topic is presented, based on a decision-theoretic approach. This framework casts criteria …

We introduce a new class ofstandardized'optimality criteria which depend onstandardized'
covariances of the least squares estimators and provide an alternative to the commonly …

… 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 …

The experimental design problem concerns the selection of k points from a potentially large
design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed …