Combining Soft Computing and Statistical Methods in Data Analysis

Combining Soft Computing and Statistical Methods in Data Analysis

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Over the last forty years there has been a growing interest to extend probability theory and statistics and to allow for more flexible modelling of imprecision, uncertainty, vagueness and ignorance. The fact that in many real-life situations data uncertainty is not only present in the form of randomness (stochastic uncertainty) but also in the form of imprecision/fuzziness is but one point underlining the need for a widening of statistical tools. Most such extensions originate in a qsofteningq of classical methods, allowing, in particular, to work with imprecise or vague data, considering imprecise or generalized probabilities and fuzzy events, etc. About ten years ago the idea of establishing a recurrent forum for discussing new trends in the before-mentioned context was born and resulted in the first International Conference on Soft Methods in Probability and Statistics (SMPS) that was held in Warsaw in 2002. In the following years the conference took place in Oviedo (2004), in Bristol (2006) and in Toulouse (2008). In the current edition the conference returns to Oviedo. This edited volume is a collection of papers presented at the SMPS 2010 conference held in Mieres and Oviedo. It gives a comprehensive overview of current research into the fusion of soft methods with probability and statistics.Alfons, A., Templ, M., Filzmoser, P.: simFrame: An object-oriented framework for statistical simulation. ... Theory, Vienna University of Technology (2009), http:// www. 5. Beirlant ... R Foundation for Statistical Computing, Vienna (2010) ISBN 3900051-07-0, http:// www.

Title:Combining Soft Computing and Statistical Methods in Data Analysis
Author:Christian Borgelt, Gil González Rodríguez, Wolfgang Trutschnig, María Asunción Lubiano, María Angeles Gil, Przemyslaw Grzegorzewski, Olgierd Hryniewicz
Publisher:Springer Science & Business Media - 2010-10-12


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