Related work¶
We are drawing on related work throughout.
Software¶
Feinberg, J., and Langtangen, H. P. (2015). Chaospy: An open source tool for designing methods of uncertainty quantification. Journal of Computational Science, 11, 46-57.
Herman, J., and Usher, W. (2017). SALib: An open-source Python library for sensitivity analysis. Journal of Open Source Software, 2 (9).
Tennoe S., Halnes G., and Einevoll G.T. (2018). Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience. Frontiers in Neuroinformatics, 12, 49.
Books¶
Ghanem, R., Higdon, D., and Owhadi, H. (2017). Handbook of uncertainty quantification. Cham, Switzerland: Springer International Publishing.
Saltelli et al. (2008). Global sensitivity analysis: The primer. Chichester, UK: John Wiley & Sons Ltd.
Saltelli, A., Tarantola, S., Campolongo, F., and Ratto, M. (2004). Sensitivity analysis in practice: A guide to assessing scientific models. Chichester, UK: John Wiley & Sons Ltd.
Smith, R.C. (2014). Uncertainty quantification: Theory, implementation, and applications. Philadelphia, PA: Society for Industrial and Applied Mathematics.
Sullivan, T.J. (2015). Introduction to uncertainty quantification. Cham, Switzerland: Springer International Publishing.
Popular science¶
King, M., and Kay, J. (2020). Radical uncertainty: Decision-making for an unknowable future. London, UK: The Bridge Street Press.