All Outputs (3)

Proposer of the vote of thanks and contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’ (2023)
Journal Article
Wilkinson, D. (2024). Proposer of the vote of thanks and contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’. Journal of the Royal Statistical Society: Series B, 86(2), 302-304. https://doi.org/10.1093/jrsssb/qkad160

Computational statistics and machine learning (ML) are closely related, and there are many opportunities for cross-fertilization of ideas between the two fields. Both can benefit from greater interaction, and the two papers being discussed here highl... Read More about Proposer of the vote of thanks and contribution to the Discussion of ‘the Discussion Meeting on Probabilistic and statistical aspects of machine learning’.

Emulating long-term weather-driven transportation earthworks deterioration models to support asset management (2023)
Journal Article
Helm, P., Svalova, A., Morsy, A., Rouainia, M., Smith, A., El-Hamalawi, A., …Glendinning, S. (2024). Emulating long-term weather-driven transportation earthworks deterioration models to support asset management. Transportation Geotechnics, 44, Article 101155. https://doi.org/10.1016/j.trgeo.2023.101155

The deterioration of transport infrastructure earthworks is a global problem, with negative impacts for infrastructure resilience, becoming of increasing significance as existing infrastructure ages. Key mechanisms which affect this deterioration inc... Read More about Emulating long-term weather-driven transportation earthworks deterioration models to support asset management.

A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields (2023)
Journal Article
Johnson, S. R., Heaps, S. E., Wilson, K. J., & Wilkinson, D. J. (2023). A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields. Environmetrics, https://doi.org/10.1002/env.2824

With extreme weather events becoming more common, the risk posed by surface water flooding is ever increasing. In this work we propose a model, and associated Bayesian inference scheme, for generating short-term, probabilistic forecasts of localised... Read More about A Bayesian spatio‐temporal model for short‐term forecasting of precipitation fields.