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The fuzzy set theory enables a formal description of uncertainty and imprecise statements.
Probabilistic collocation (PC) is a non-intrusive approach to compute the polynomial chaos description of uncertainty numerically.
Structural reliability analysis is conventionally based on a description of uncertainty via a joint probability density function (JPDF).
This also allows for a description of uncertainty, which would currently be challenging to define uniformly for InSAR measurements due to the diversity of data, processing and analysis methods widely used.
In this paper, the description of uncertainty begins from a historical perspective and concludes with a new perspective based upon making inferences; fuzzy logic can be most helpful in quantifying some inferences.
(See Methods section for description of uncertainty and sensitivity analyses).
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A description of uncertainties via bounds on the uncertainty variables is adopted.
It follows the risk assessment step (optimally incorporating the description of uncertainties in the risk assessment process) and involves consideration of political, social, economic, and engineering factors.
Experimental structure determination remains a perennial challenge as investigators tackle larger macromolecular systems, and begin to incorporate more complete descriptions of uncertainty, heterogeneity and dynamics into their models.
Probabilistic descriptions of uncertainty help avoid linguistic uncertainty, because mathematical statements avoid ambiguity and vagueness and the context is also usually explicit.
This approach then produces relative effect estimates (and associated descriptions of uncertainty) for all treatments connected by the network of evidence [ 3] – even where head-to-head trials for comparisons do not exist.
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