138 citations · 298 across the 20 of their papers we have counts for
21 papers
Cross-Validation Based Adaptive Sampling for Multi-Level Gaussian Process Models
Louise Kimpton, James Salter, Tim Dodwell +2
Complex computer codes or models can often be run in a hierarchy of different levels of complexity ranging from the very basic to the sophisticated. The top levels in this hierarch…
Modeling Envisat RA-2 waveforms in the coastal zone: Case study of calm water contamination
J. Gomez Enri, S. Vignudelli, G. Quartly +4
This letter examines waveform data from the Envisat RA-2 as it passes regularly over Pianosa (a 10-km 2 island in the northwestern Mediterranean). Forty-six repeat passes were anal…
Using infinite server queues with partial information for occupancy prediction
Nikki Sonenberg, Victoria Volodina, Peter G. Challenor +1
Motivated by demand prediction for the custodial prison population in England and Wales, this paper describes an approach to the study of service systems using infinite server queu…
Modelling Correlated Bernoulli Data Part II: Inference
Louise Kimpton, Peter Challenor, Henry Wynn
Binary data are highly common in many applications, however it is usually modelled with the assumption that the data are independently and identically distributed. This is typicall…
Binary De Bruijn Processes
Louise Kimpton, Peter Challenor, Henry Wynn
Binary time series data are very common in many applications, and are typically modelled independently via a Bernoulli process with a single probability of success. However, the pr…
Variance-based global sensitivity analysis of numerical models using R
Hossein Mohammadi, Peter Challenor, Clémentine Prieur
Sensitivity analysis plays an important role in the development of computer models/simulators through identifying the contribution of each (uncertain) input factor to the model out…
Sequential adaptive design for emulating costly computer codes
Hossein Mohammadi, Peter Challenor
Gaussian processes (GPs) are generally regarded as the gold standard surrogate model for emulating computationally expensive computer-based simulators. However, the problem of trai…
A Bayesian Decision Support System in Energy Systems Planning
Victoria Volodina, Nikki Sonenberg, Peter Challenor +1
Gaussian Process (GP) emulators are widely used to approximate complex computer model behaviour across the input space. Motivated by the problem of coupling computer models, recent…
Propagating uncertainty in a network of energy models
Victoria Volodina, Nikki Sonenberg, Jim Q. Smith +3
Computer models are widely used in decision support for energy systems operation, planning and policy. A system of models is often employed, where model inputs themselves arise fro…
A microsimulation of spatial inequality in energy access: A Bayesian multi-level modelling approach for urban India
A. P. Neto-Bradley, R. Choudhary, P. Challenor
Access to sustained clean cooking in India is essential to addressing the health burden of indoor air pollution from biomass fuels, but spatial inequality in cities can adversely a…
Stochastic Downscaling to Chaotic Weather Regimes using Spatially Conditioned Gaussian Random Fields with Adaptive Covariance
Rachel Prudden, Niall Robinson, Peter Challenor +1
Downscaling aims to link the behaviour of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high resolution information at…
Emulating complex dynamical simulators with random Fourier features
Hossein Mohammadi, Peter Challenor, Marc Goodfellow
A Gaussian process (GP)-based methodology is proposed to emulate complex dynamical computer models (or simulators). The method relies on emulating the numerical flow map of the sys…
Key Questions for Modelling COVID-19 Exit Strategies
Robin N Thompson, T Deirdre Hollingsworth, Valerie Isham +40
Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to impl…
Cross-validation based adaptive sampling for Gaussian process models
Hossein Mohammadi, Peter Challenor, Daniel Williamson +1
In many real-world applications, we are interested in approximating black-box, costly functions as accurately as possible with the smallest number of function evaluations. A comple…
Classification of Computer Models with Labelled Outputs
Louise Kimpton, Peter Challenor, Daniel Williamson
Classification is a vital tool that is important for modelling many complex numerical models. A model or system may be such that, for certain areas of input space, the output eithe…
Future Proofing a Building Design Using History Matching Inspired Level-Set Techniques
Evan Baker, Peter Challenor, Matt Eames
History Matching is a technique used to calibrate complex computer models, that is, finding the input settings which lead to the simulated output matching up with real world observ…
Emulating computer models with step-discontinuous outputs using Gaussian processes
Hossein Mohammadi, Peter Challenor, Marc Goodfellow +1
In many real-world applications we are interested in approximating costly functions that are analytically unknown, e.g. complex computer codes. An emulator provides a fast approxim…
Predicting the Output From a Stochastic Computer Model When a Deterministic Approximation is Available
Evan Baker, Peter Challenor, Matt Eames
The analysis of computer models can be aided by the construction of surrogate models, or emulators, that statistically model the numerical computer model. Increasingly, computer mo…
Diagnostics for Stochastic Gaussian Process Emulators
Evan Baker, Peter Challenor, Matt Eames
Computer models, also known as simulators, can be computationally expensive to run, and for this reason statistical surrogates, known as emulators, are often used. Any statistical…
Modelling Numerical Systems with Two Distinct Labelled Output Classes
Louise Kimpton, Peter Challenor, Daniel Williamson
We present a new method of modelling numerical systems where there are two distinct output solution classes, for example tipping points or bifurcations. Gaussian process emulation…
Emulating dynamic non-linear simulators using Gaussian processes
Hossein Mohammadi, Peter Challenor, Marc Goodfellow
The dynamic emulation of non-linear deterministic computer codes where the output is a time series, possibly multivariate, is examined. Such computer models simulate the evolution…