By Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, Stefano Tarantola
Complicated mathematical and computational versions are utilized in all parts of society and expertise and but version established technology is more and more contested or refuted, specifically whilst versions are utilized to arguable topics in domain names akin to well-being, the surroundings or the economic climate. extra stringent criteria of proofs are demanded from model-based numbers, particularly while those numbers symbolize capability monetary losses, threats to human future health or the country of our environment. Quantitative sensitivity research is usually agreed to be one such standard.
Mathematical versions are sturdy at mapping assumptions into inferences. A modeller makes assumptions approximately legislation referring to the approach, approximately its prestige and a plethora of different, usually arcane, procedure variables and inner version settings. To what quantity will we depend on the model-based inference while almost all these assumptions are fraught with uncertainties? international Sensitivity research deals an available therapy of such difficulties through quantitative sensitivity research, starting with the 1st ideas and guiding the reader throughout the complete variety of suggested practices with a wealthy set of solved workouts. The textual content explains the incentive for sensitivity research, reports the necessary statistical suggestions, and offers a consultant to strength applications.
The book:
- Provides a self-contained therapy of the topic, permitting readers to benefit and perform worldwide sensitivity research with out additional materials.
- Presents how one can body the research, interpret its effects, and stay away from strength pitfalls.
- Features a number of routines and solved difficulties to assist illustrate the applications.
- Is authored by means of major sensitivity research practitioners, combining a number of disciplinary backgrounds.
Postgraduate scholars and practitioners in quite a lot of matters, together with records, arithmetic, engineering, physics, chemistry, environmental sciences, biology, toxicology, actuarial sciences, and econometrics will locate a lot of use right here. This e-book will end up both precious to engineers engaged on danger research and to monetary analysts enthusiastic about pricing and hedging.
Read or Download Global Sensitivity Analysis: The Primer PDF
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Extra info for Global Sensitivity Analysis: The Primer
Sample text
Is the model overly dependent on fragile assumptions? METHODS AND SETTINGS FOR SENSITIVITY ANALYSIS 35 • Research prioritization. Which factor is most deserving of further analysis or measurement? ⇒ factor prioritization setting. • Model simplification. Can some factors or compartments of the model be fixed or simplified? ⇒ factor fixing setting. • Identifying critical or otherwise interesting regions in the space of the input factors. Identifying factors which interact and which may thus generate extreme values.
Remember that we assume all factors independent, so each factor is sampled from its marginal distribution with no consideration of where the other factors are sampled. How the Monte Carlo sample is used to produce estimates Sˆ i of the first-order sensitivity measures Si is explained later in this book (see Chapter 4). 27), where r = 4, c = 0 5, = 1 2 3 4 for both i and Zi , and N = 40 000 for the regression analysis. 3, where the squared standardized regression estimates ˆ 2 are also reported for comparison.
45) we have apportioned variance between data and experts and an interaction between the two. 47) i in which uncertainty is divided among ‘themes’, each theme comprising an indicator and its weight. It is easy to imagine similar applications. For example, one could divide uncertainty among observational data, estimation, model assumptions, model resolution and so on. 16 Further Methods So far we have discussed the following tools for sensitivity analysis: • • • • derivatives and sigma-normalized derivatives; regression coefficients (standardized); variance-based measures; scatterplots.