Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models

By Andrea Saltelli, Stefano Tarantola, Francesca Campolongo, Marco Ratto. 2004

2,707 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6706076e77097c43c0676a7a4432b0a195b3b2a1

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Citations in the corpus

Fibroblast Heterogeneity and Immunosuppressive Environment in Human Breast Cancer. (2018, positive): “First, we applied Monte Carlo one-variable-at-a-time approach (Saltelli et al., 2004) with uniform random sampling (N=1000) of each variable for each data point.”
A survey on applications of the harmony search algorithm (2013, methodology): “It explores the joint use of Global Sensitivity Analysis (GSA Saltelli et al., 2005) and the HS algorithm for design optimization of shell and tube heat exchangers from the economic point of view.”
GEM: a generic ecological model for estuaries and coastal waters (2009, methodology): “A recent sensitivity analysis of GEM (Salacinska, 2008) using the Moris method (Saltelli et al., 2004), revealed that the Dutch coastal zone application of the GEM model is particularly sensitive to Chl:C ratios and light-related parameters (excl.”
The design of robust value-creating supply chain networks (2010, methodology): “An alternative is to select a few representative scenarios, to solve the resulting deterministic model for each of them, and to evaluate the designs obtained using Monte Carlo sensitivity analysis (Saltelli et al., 2004; Ridlehoover, 2004).”
Reconciling theory with observations: elements of a diagnostic approach to model evaluation (2008, background): “Evaluation might include a sensitivity analysis where the model response to perturbation of controlling factors would be expected to reflect similar behaviours observed in the data (Saltelli et al., 2004).”
Solutions for the diurnally forced advection‐diffusion equation to estimate bulk fluid velocity and diffusivity in streambeds from temperature time series (2012, methodology): “Consequently, the analysis of the propagation of the uncertainties associated with measurement errors and parameter estimations on hyporheic fluxes (magnitude and direction) relies on sensitivity analysis or methods such as Monte Carlo simulations [Gordon et al., 2012; Saltelli et al., 2004; Shanafi”
Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R' (2014, methodology): “All source codes, i.e., the NetLogo model implementation and the R/RNetLogo scripts, are available in the Supplementary Material. sensitivity analysis methods (e.g., Saltelli et al. 2000, 2004, 2008; Helton et al. 2006; Kleijnen 1995, 2008; Cournède et al. 2013; Gan et al. 2014).”
A Review on Global Sensitivity Analysis Methods (2014, positive): “Let us denote r the number of OAT designs (Saltelli et al. [78] propose to set parameter r between 4 and 10).”
Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments (2012, methodology): “The Morris (1991) method is a OAT “screening method” that is a computationally efficient means of identifying sensitive parameters, but is ultimately considered global because it attempts to explore the majority of the parameter space (Saltelli et al., 2004).”
A review of methods to match building energy simulation models to measured data (2014, neutral): “[64–68]”