Towards Quantifying the Uncertainty in In Silico Predictions using Bayesian Learning

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Authors
Allen, Timothy EH
Middleton, Alistair M
  Goodman, Jonathan
Russell, Paul J
Kukic, Predrag
Gutsell, Steve
Publication Date
2022-04
Journal Title
Computational Toxicology
ISSN
2468-1113
Publisher
Elsevier BV
Number
100228
Pages
100228-100228
Type
Article
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AM
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Citation
Allen, T. E., Middleton, A. M., Goodman, J., Russell, P. J., Kukic, P., & Gutsell, S. (2022). Towards Quantifying the Uncertainty in In Silico Predictions using Bayesian Learning. Computational Toxicology, (100228), 100228-100228. https://doi.org/10.1016/j.comtox.2022.100228
Embargo Lift Date
2023-04-30
Identifiers
External DOI: https://doi.org/10.1016/j.comtox.2022.100228
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336947
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/
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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's licence is described as Attribution-NonCommercial-NoDerivatives 4.0 International