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A Bayesian Computer Model Analysis of Robust Bayesian Analyses (2022)
Journal Article
Vernon, I., & Gosling, J. (2023). A Bayesian Computer Model Analysis of Robust Bayesian Analyses. Bayesian Analysis, 18(4), 1367-1399. https://doi.org/10.1214/22-ba1340

We harness the power of Bayesian emulation techniques, designed to aid the analysis of complex computer models, to examine the structure of complex Bayesian analyses themselves. These techniques facilitate robust Bayesian analyses and/or sensitivity... Read More about A Bayesian Computer Model Analysis of Robust Bayesian Analyses.

Ab initio predictions link the neutron skin of 208Pb to nuclear forces (2022)
Journal Article
Hu, B., Jaing, W., Miyagi, T., Sun, Z., Ekström, A., Forssén, C., …Vernon, I. (2022). Ab initio predictions link the neutron skin of 208Pb to nuclear forces. Nature Physics, 18(10), 1196-1200. https://doi.org/10.1038/s41567-022-01715-8

Heavy atomic nuclei have an excess of neutrons over protons, which leads to the formation of a neutron skin whose thickness is sensitive to details of the nuclear force. This links atomic nuclei to properties of neutron stars, thereby relating object... Read More about Ab initio predictions link the neutron skin of 208Pb to nuclear forces.

Bayesian Emulation and History Matching of JUNE (2022)
Journal Article
Vernon, I., Owen, J., Aylett-Bullock, J., Cuestra-Lazaro, C., Frawley, J., Quera-Bofarull, A., …Krauss, F. (2022). Bayesian Emulation and History Matching of JUNE. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2233), Article 20220039. https://doi.org/10.1098/rsta.2022.0039

We analyse JUNE: a detailed model of Covid-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial computational resources to evaluate, making model calibration and general unc... Read More about Bayesian Emulation and History Matching of JUNE.

Complex model calibration through emulation, a worked example for a stochastic epidemic model (2022)
Journal Article
Dunne, M., Mohammadi, H., Challenor, P., Borgo, R., Porphyre, T., Vernon, I., …Swallow, B. (2022). Complex model calibration through emulation, a worked example for a stochastic epidemic model. Epidemics, 39, Article 100574. https://doi.org/10.1016/j.epidem.2022.100574

Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemio... Read More about Complex model calibration through emulation, a worked example for a stochastic epidemic model.

Efficient Emulation of Computer Models Utilising Multiple Known Boundaries of Differing Dimension (2022)
Journal Article
Jackson, S. E., & Vernon, I. (2023). Efficient Emulation of Computer Models Utilising Multiple Known Boundaries of Differing Dimension. Bayesian Analysis, 18(1), 165-191. https://doi.org/10.1214/22-ba1304

Emulation has been successfully applied across a wide variety of scientific disciplines for efficiently analysing computationally intensive models. We develop known boundary emulation strategies which utilise the fact that, for many computer models,... Read More about Efficient Emulation of Computer Models Utilising Multiple Known Boundaries of Differing Dimension.

Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling (2022)
Journal Article
Swallow, B., Birrell, P., Blake, J., Burgman, M., Challenor, P., Coffeng, L. E., …Vernon, I. (2022). Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling. Epidemics, 38, https://doi.org/10.1016/j.epidem.2022.100547

The estimation of parameters and model structure for informing infectious disease response has become a focal point of the recent pandemic. However, it has also highlighted a plethora of challenges remaining in the fast and robust extraction of infor... Read More about Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.