Handbook of Uncertainty Quantification

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Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices (2017, background): “Uncertainty analysis can include a broad range of applications relating to uncertainty—a very thorough reference can be found in (Ghanem et al., 2017).”
Stochastic Galerkin Methods for the Boltzmann-Poisson system (2019, positive): “Due to the lack of a known analytical solution to our time-dependent problem, we proceed to compare our solution with a reference solution obtained by means of a high-order (HO) stochastic collocation (SC) method [2,3,25].”
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results (2018, background): “See Ghanem et al. (2017), Berger et al. (1994), and Liu et al. (2013) for overviews of these ﬁelds, respectively.”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, neutral): “…as documented in the recent reviews by Norton (2015), Iooss and Lemaître (2015), Wei et al. (2015), Razavi and Gupta (2015), Borgonovo and Plischke (2016), Pianosi et al. (2016), Borgonovo et al. (2017), Borgonovo (2017), Ghanem et al. (2017), Gupta and Razavi (2017) and Saltelli et al. (2019).”