Title: Sensitivity analysis
Authors: SALTELLI AndreaANNONI Paola
Publisher: Springer
Publication Year: 2011
JRC Publication N°: JRC54701
ISBN: 978-3-642-04897-5
URI: http://dx.doi.org/10.1007/978-3-642-04898-2_509
http://publications.jrc.ec.europa.eu/repository/handle/JRC54701
DOI: 10.1007/978-3-642-04898-2_509
Type: Articles in books
Abstract: Existing guidelines for impact assessment recommend that mathematical modelling of real or man-made system be accompanied by a `sensitivity analysis' - SA (EC, 2009; EPA, 2009; OMB, 2006). The same recommendation can be found in textbooks for practitioners (e.g. Kennedy, 2007, Saltelli et al., 2008). Mathematical models can be seen as machines capable of mapping from a set of assumptions (data, parameters, scenarios) into an inference (model output). In this respect modellers should tackle: - Uncertainty. Characterize the empirical probability density function and the condence bounds for a model output. This can be viewed as the numerical equivalent of the measurement error for physical experiments. The question answered is "How uncertain is this inference?". - Sensitivity. Identify factors or groups of factors mostly responsible for the uncertainty in the prediction. The question answered is "Where is this uncertainty coming from?". The two terms are often used differently, with sensitivity analysis used for both challenges (e.g. Leamer, 1990). We focus on sensitivity analysis proper, i.e. the effect of individual factors or group of factors in driving the output and its uncertainty.
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