Most surrogate models for computer experiments are interpolators, and the most common
interpolator is a Gaussian process (GP) that deliberately omits a small-scale (measurement) …

The Gaussian process regression model is a popular type of “emulator” used as a fast
surrogate for computationally expensive simulators (deterministic computer models). For …

One of the challenges with emulating the response of a multivariate function to its inputs is
the quantity of data that must be assimilated, which is the product of the number of model …

We consider the problem of emulating (approximating) computer models (simulators) that
produce massive output. The specific simulator we study is a computer model of volcanic …

In this paper we carry out a Bayesian calibration for uncertainty analysis in Computational
Fluid Dynamics modelling of urban flows. Taking the case of airflow in a regular street …

In a paper written some 25 years ago, I distinguished three contexts in which one might wish
to combine expert judgements of uncertainty: the expert problem, the group decision …

Probabilistic models for infectious disease dynamics are useful for understanding the
mechanism underlying the spread of infection. When the likelihood function for these models …

Uncertainty, its modelling and analysis have been discussed across many literatures
including statistics and operational research, knowledge management and philosophy:(i) …

This work focuses on substituting a computationally expensive simulator by a cheap
emulator to enable studying applications where running the simulator is prohibitively …

We provide a statistical interpretation of current practice in climate modeling. In this review,
we define weather and climate, clarify the relationship between simulator output and …