Titlebook: Data Modeling for Metrology and Testing in Measurement Science; Franco Pavese,Alistair B. Forbes Book 2009 Birkhäuser Boston 2009 Internet

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The Cognitive and Behavioural Sciencesaccording to models that are different according to the underpinning assumptions, which must adequately match the characteristic of the observed phenomena or process. This chapter deals with the different types of description of the uncertainty components, with a wide selection of citations from ref

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Communicating for Social Changerement error is located on the interval [-Δ,Δ]. The traditional engineering approach to such situations is to assume that Δ . is uniformly distributed on [-Δ,Δ], and to use the corresponding statistical techniques. In some situations, however, this approach underestimates the error of indirect measu

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https://doi.org/10.1007/978-3-319-76017-9one that weighs each alternative pros, cons, and risks. The support for decision-making is data that come basically from experience, either previously acquired or gathered for the specific decision-making. The data usually come from different sources and thus have to be fused for a single decision.

Sleep-Paralysis

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From Monologism to Dialogicality; it is always reduced to relevant influences, system parameters, and behaviour. Therefore, in uncertainty evaluation, it is important to consider the effects of imperfect modelling. Derived from the classical theory of signals and systems, this contribution explains the basic approaches to systemat

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Daniel C. O’Connell,Sabine Kowal been expressed (either explicitly or implicitly) as probability density functions, Monte Carlo techniques can propagate uncertainties through any measurement equation or algorithm – including statistical aggregates. These techniques are introduced in this chapter, and applied to examples drawn from