On Joint Modelling of Random Uncertainty and Fuzzy Imprecision

Olgierd Hryniewicz ()
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Olgierd Hryniewicz: Systems Research Institute

Chapter Chapter 2 in Coping with Uncertainty, 2010, pp 11-38 from Springer

Abstract: Abstract The paper deals with the problem of the mathematical description of uncertainties of different type. It has been demonstrated by many authors that the theory of probability is not always suitable for the description of uncertainty related to vagueness. We briefly present some of the most promising theories which have been recently proposed for coping with this problem. Then, we concentrate our attention on the application of fuzzy random variables which seem to be very useful for the joint modelling of random uncertainty and fuzzy imprecision, and for the statistical analysis of imprecise data. The application of the statistical methodology for fuzzy data, called fuzzy statistics, is illustrated with two practical examples, typical for the problems of systems analysis. First example is devoted to the problem of the estimation of greenhouse gases inventories. In the second example, typical for the problems of making decisions using small amount of available data, we show how fuzzy approach can be used for the improvement of sequential statistical tests.

Keywords: Membership Function; Fuzzy Number; Emission Factor; Classical Probability; Belief Function (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-642-03735-1_2

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DOI: 10.1007/978-3-642-03735-1_2

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