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View article: On real-time calibrated prediction for complex model-based decision support in pandemics: Part 1
On real-time calibrated prediction for complex model-based decision support in pandemics: Part 1 Open
A bstract Infectious disease models are used to predict the spread and impact of outbreaks of a disease. Like other complex models, they have parameters that need to be calibrated, and structural discrepancies from the reality that they si…
View article: On real-time calibrated prediction for complex model-based decision support in pandemics: Part 2
On real-time calibrated prediction for complex model-based decision support in pandemics: Part 2 Open
Calibration of complex stochastic infectious disease models is challenging. These often have high-dimensional input and output spaces, with the models exhibiting complex, non-linear dynamics. Coupled with a paucity of necessary data, this …
View article: Emulating computer models with high-dimensional count output
Emulating computer models with high-dimensional count output Open
Computer models are used to study the real world, and often contain a large number of uncertain input parameters, produce a large number of outputs, may be expensive to run and need calibrating to real-world observations to be useful for d…
View article: Using the natural capital framework to integrate biodiversity into sustainable, efficient and equitable environmental-economic decision-making
Using the natural capital framework to integrate biodiversity into sustainable, efficient and equitable environmental-economic decision-making Open
One of Georgina Mace’s many transformational research contributions was to provide a universally applicable framework for incorporating any or all elements and connections of the natural environment within conventional economic decision-ma…
View article: Deep Gaussian Process Emulation and Uncertainty Quantification for Large Computer Experiments
Deep Gaussian Process Emulation and Uncertainty Quantification for Large Computer Experiments Open
Computer models are used as a way to explore complex physical systems. Stationary Gaussian process emulators, with their accompanying uncertainty quantification, are popular surrogates for computer models. However, many computer models are…
View article: How to make land use policy decisions: Integrating science and economics to deliver connected climate, biodiversity, and food objectives
How to make land use policy decisions: Integrating science and economics to deliver connected climate, biodiversity, and food objectives Open
Land use change is crucial to addressing the existential threats of climate change and biodiversity loss while enhancing food security [M. Zurek et al. , Science 376 , 1416–1421 (2022)]. The interconnected and spatially varying nature of t…
View article: Generalised Bayes Linear Inference
Generalised Bayes Linear Inference Open
Motivated by big data and the vast parameter spaces in modern machine learning models, optimisation approaches to Bayesian inference have seen a surge in popularity in recent years. In this paper, we address the connection between the popu…
View article: Coexchangeable Process Modeling for Uncertainty Quantification in Joint Climate Reconstruction
Coexchangeable Process Modeling for Uncertainty Quantification in Joint Climate Reconstruction Open
This is the final version. Available on open access from Taylor & Francis via the DOI in this record
View article: Feature calibration for computer models
Feature calibration for computer models Open
Computer model calibration involves using partial and imperfect observations of the real world to learn which values of a model's input parameters lead to outputs that are consistent with real-world observations. When calibrating models wi…
View article: On the meaning of uncertainty for ethical AI: philosophy and practice
On the meaning of uncertainty for ethical AI: philosophy and practice Open
Whether and how data scientists, statisticians and modellers should be accountable for the AI systems they develop remains a controversial and highly debated topic, especially given the complexity of AI systems and the difficulties in comp…
View article: Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections
Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections Open
Documenting the uncertainty of climate change projections is a fundamental objective of the inter-comparison exercises organized to feed into the Intergovernmental Panel on Climate Change (IPCC) reports. Usually, each modeling center contr…
View article: Engaging publics in the transition to smart mobilities
Engaging publics in the transition to smart mobilities Open
Commercial and public sector interests surrounding technological developments are promoting a widespread transition to autonomous vehicles, intelligent transportation systems and smart phone communications in everyday life, as part of the …
View article: Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation
Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation Open
Ice sheet models are used to study the deglaciation of North America at the\nend of the last ice age (past 21,000 years), so that we might understand\nwhether and how existing ice sheets may reduce or disappear under climate\nchange. Thoug…
View article: A review of planting principles to identify the right place for the right tree for ‘net zero plus’ woodlands: Applying a place‐based natural capital framework for sustainable, efficient and equitable (<scp>SEE</scp>) decisions
A review of planting principles to identify the right place for the right tree for ‘net zero plus’ woodlands: Applying a place‐based natural capital framework for sustainable, efficient and equitable (<span>SEE</span>) decisions Open
We outline the principles of the natural capital approach to decision making and apply these to the contemporary challenge of very significantly expanding woodlands as contribution to attaining net zero emissions of greenhouse gases. Drawi…
View article: De-tuning a coupled Climate Ice Sheet Model to simulate the North American Ice Sheet at the Last Glacial Maximum&#160;
De-tuning a coupled Climate Ice Sheet Model to simulate the North American Ice Sheet at the Last Glacial Maximum  Open
<p>Coupled climate-ice sheet models are crucial to evaluating climate-ice feedbacks' role in future ice sheet evolution. Such models are calibrated to reproduce modern-day ice sheets, but current observations alone are insufficient t…
View article: Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES
Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES Open
Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be u…
View article: Cross-Validation--based Adaptive Sampling for Gaussian Process Models
Cross-Validation--based Adaptive Sampling for Gaussian Process Models Open
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 complex computer code is an example of such a function. In this w…
View article: EFFICIENT CALIBRATION FOR HIGH-DIMENSIONAL COMPUTER MODEL OUTPUT USING BASIS METHODS
EFFICIENT CALIBRATION FOR HIGH-DIMENSIONAL COMPUTER MODEL OUTPUT USING BASIS METHODS Open
Calibration of expensive computer models with high-dimensional output fields can be approached via history matching. If the entire output field is matched, with patterns or correlations between locations or time points represented, calcula…
View article: Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases
Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases Open
The calibration of complex computer codes using uncertainty quantification (UQ) methods is a rich area of statistical methodological development. When applying these techniques to simulators with spatial output, it is now standard to use p…
View article: Deep Gaussian Process Emulation using Stochastic Imputation
Deep Gaussian Process Emulation using Stochastic Imputation Open
Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer …
View article: Comment on gmd-2021-205
Comment on gmd-2021-205 Open
Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models a…
View article: Comment on gmd-2021-205
Comment on gmd-2021-205 Open
Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models a…
View article: Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES
Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES Open
Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be u…
View article: Deep Gaussian Process Emulation using Stochastic Imputation
Deep Gaussian Process Emulation using Stochastic Imputation Open
Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose a novel inference method for DGPs for computer …
View article: Early epidemiological signatures of novel SARS-CoV-2 variants: establishment of B.1.617.2 in England
Early epidemiological signatures of novel SARS-CoV-2 variants: establishment of B.1.617.2 in England Open
The rapid emergence of SARS-CoV-2 mutants with new phenotypic properties is a critical challenge to the control of the ongoing pandemic. B.1.1.7 was monitored in the UK through routine testing and S-gene target failures (SGTF), comprising …
View article: Process‐Based Climate Model Development Harnessing Machine Learning: III. The Representation of Cumulus Geometry and Their 3D Radiative Effects
Process‐Based Climate Model Development Harnessing Machine Learning: III. The Representation of Cumulus Geometry and Their 3D Radiative Effects Open
Process‐scale development, evaluation, and calibration of physically based parameterizations of clouds and radiation are powerful levers for improving weather and climate models. In a series of papers, we propose a strategy for process‐bas…
View article: Exploring the complex uncertainties in coupled climate-ice simulations of the Last Glacial Maximum
Exploring the complex uncertainties in coupled climate-ice simulations of the Last Glacial Maximum Open
<p>Simulating the co-evolution of climate and ice-sheets during the Quaternary is key to understanding some of the major abrupt changes in climate, ice and sea level. Indeed, events such as the Meltwater pulse 1a rapid sea level rise…