Uncertainty in Industrial Practice: A Guide to Quantitative by Etienne de Rocquigny, Nicolas Devictor, Stefano Tarantola

By Etienne de Rocquigny, Nicolas Devictor, Stefano Tarantola

Coping with uncertainties in commercial platforms is a regular problem to make sure more suitable layout, strong operation, in charge functionality and responsive probability keep an eye on. Authored via a number one ecu community of specialists representing a pass component of industries, Uncertainty in business perform goals to supply a reference for the dissemination of uncertainty therapy in any kind of undefined. it's fascinated with the quantification of uncertainties within the presence of knowledge, model(s) and data in regards to the process, and gives a technical contribution to decision-making methods when acknowledging business constraints. The strategy provided will be utilized to quite a number varied company contexts, from study or early layout via to certification or in-service approaches. The authors target to foster optimum trade-offs among literature-referenced methodologies and the simplified ways frequently inevitable in perform, as a result of information, time or funds obstacles of technical decision-makers.

Uncertainty in business perform:

  • Features fresh uncertainty case experiences performed within the nuclear, air & area, oil, mechanical and civil engineering industries set in a typical methodological framework.
  • offers equipment for organizing and treating uncertainties in a commonly used and prioritized point of view.

  • Illustrates useful problems and options encountered in line with the extent of complexity, details to be had and regulatory and monetary constraints.
  • Discusses most sensible perform in uncertainty modeling, propagation and sensitivity research via quite a few statistical and numerical tools.
  • Reviews fresh criteria, references and to be had software program, offering an important source for engineers and probability analysts in a large choice of industries.

This booklet presents a advisor to facing quantitative uncertainty in engineering and modelling and is aimed toward practitioners, together with risk-industry regulators and teachers wishing to advance industry-realistic methodologies.

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Extra info for Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management

Example text

I. that embeds the level-1 uncertainty. ; or within the single distribution of the variable of interest, called the predictive distribution, which also corresponds to the level-1 distribution averaged over the level-2 uncertainty of its parameters. For instance, one would not consider a credibility interval for a system failure rate1 , but merely its expected value, consolidating all components of uncertainty into a single probabilistic figure. g. ), provided it was specified along the same information basis on the inputs and definition of observable variables of interest.

That embeds the level-1 uncertainty. ; or within the single distribution of the variable of interest, called the predictive distribution, which also corresponds to the level-1 distribution averaged over the level-2 uncertainty of its parameters. For instance, one would not consider a credibility interval for a system failure rate1 , but merely its expected value, consolidating all components of uncertainty into a single probabilistic figure. g. ), provided it was specified along the same information basis on the inputs and definition of observable variables of interest.

The quantity of interest used for sensitivity analysis is the variance of confidence percentiles. The final goal is to rank the importance of the uncertain inputs. This is important for the feedback decision criterion in logistic and supply chain management.

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Uncertainty in Industrial Practice: A Guide to Quantitative by Etienne de Rocquigny, Nicolas Devictor, Stefano Tarantola
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