Sequential design of computer experiments for the estimation of a probability of failure

This paper deals with the problem of estimating the volume of the excursion set of a function $f:\mathbb{R}^d \to \mathbb{R}$ above a given threshold, under a probability measure on $\mathbb{R}^d$ that is assumed to be known. In the industrial world, this corresponds to the problem of estimating a probability of failure of a system. When only an expensive-to-simulate model of the system is available, the budget for simulations is usually severely limited and therefore classical Monte Carlo methods ought to be avoided. One of the main contributions of this article is to derive SUR (stepwise uncertainty reduction) strategies from a Bayesian-theoretic formulation of the problem of estimating a probability of failure. These sequential strategies use a Gaussian process model of $f$ and aim at performing evaluations of $f$ as efficiently as possible to infer the value of the probability of failure. We compare these strategies to other strategies also based on a Gaussian process model for estimating a probability of failure.Comment: This is an author-generated postprint version. The published version is available at http://www.springerlink.co

Sources of “value for money” for museum visitors: Some survey evidence

This paper provides an economic analysis of the survey responses of visitors who were asked to make a “ value for money” (VFM) assessment of a museum visit. The paper first interprets the notion of VFM from an economic perspective, and distinguishes between evaluations made before and after a visit. It then analyses the survey responses of visitors to a major museum in the North of England, using appropriate statistical techniques to identify the economic determinants of VFM rankings by visitors. The final section discusses the implications of the methodology and results for museum management, and for the design of museum visitor surveys. Copyright Kluwer Academic Publishers 1996museums, value, surveys,