Browse by author
Lookup NU author(s): Dr Zheyang Shen, Sam Power, Professor Chris OatesORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Copyright 2025 by the author(s).Deterministic mathematical models, such as those specified via differential equations, are a powerful tool to communicate scientific insight. However, such models are necessarily simplified descriptions of the real world. Generalised Bayesian methodologies have been proposed for inference with misspecified models, but these are typically associated with vanishing parameter uncertainty as more data are observed. In the context of a misspecified deterministic mathematical model, this has the undesirable consequence that posterior predictions become deterministic and certain, while being incorrect. Taking this observation as a starting point, we propose Prediction-Centric Uncertainty Quantification, where a mixture distribution based on the deterministic model confers improved uncertainty quantification in the predictive context. Computation of the mixing distribution is cast as a (regularised) gradient flow of the maximum mean discrepancy (MMD), enabling consistent numerical approximations to be obtained. Results are reported on both a toy model from population ecology and a real model of protein signalling in cell biology.
Author(s): Shen Z, Knoblauch J, Power S, Oates CJ
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: Proceedings of The 28th International Conference on Artificial Intelligence and Statistics
Year of Conference: 2025
Pages: 649-657
Online publication date: 03/05/2025
Acceptance date: 02/04/2018
ISSN: 2640-3498
Publisher: ML Research Press
URL: https://proceedings.mlr.press/v258/shen25a.html
Series Title: Proceedings of Machine Learning Research
