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Soft Methods for Integrated Uncertainty Modelling

  • Book
  • © 2006

Overview

Editors:
  1. Jonathan Lawry

    1. AI Group Department of Engineering Mathematics, University of Bristol, Bristol, UK

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  2. Enrique Miranda

    1. Statistics and Operations Research, Rey Juan Carlos University, Spain

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  3. Alberto Bugarin

    1. Intelligent Systems Group Department of Electronics & Computer Science, University of Santiago de Compostela Santiago de Compostela, Spain

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  4. Shoumei Li

    1. Department of Applied Mathematics, Beijing University of Technology, Beijing, P.R. China

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  5. Maria Angeles Gil

    1. Dpto. Estadistica e I.O y D.M. Calle Calvo Sotelo s/n, Universiad de Oviedo Fac. Ciencias, Oviedo, Spain

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  6. Przemys aw Grzegorzewski

    1. Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

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  7. Olgierd Hyrniewicz

    1. Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

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  • Proceedings of the 3rd International Conference on Soft Methods in Probability and Statistics, Bristol UK Sept. 5.-7. 2006
  • Recent developments in Soft Computing and Statistics
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Intelligent and Soft Computing (AINSC, volume 37)

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Table of contents (47 chapters)

  1. Front Matter

    Pages I-X
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  3. Soft Methods in Statistics and Random Information Systems

    1. Front Matter

      Pages I-X
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    2. On Testing Fuzzy Independence

      • Olgierd Hryniewicz
      Pages 29-36

    3. Variance Decomposition of Fuzzy Random Variables

      • Andreas Wünsche, Wolfgang Näther
      Pages 37-44

    4. Fuzzy Histograms and Density Estimation

      • Kevin Loquin, Olivier Strauss
      Pages 45-52

    5. Graded Stochastic Dominance as a Tool for Ranking the Elements of a Poset

      • Karel De Loof, Hans De Meyer, Bernard De Baets
      Pages 53-60

    6. On Neyman-Pearson Lemma for Crisp, Random and Fuzzy Hypotheses

      • Adel Mohammadpour, Ali Mohammad-Djafari
      Pages 61-69

    7. Fuzzy Probability Distributions Induced by Fuzzy Random Vectors

      • Wolfgang Trutschnig
      Pages 71-78

    8. On the Identifiability of TSK Additive Fuzzy Rule-Based Models

      • José Luis Aznarte M., José Manuel Benítez
      Pages 79-86

    9. An Asymptotic Test for Symmetry of Random Variables Based on Fuzzy Tools

      • González-Rodríguez G., Colubi A., D’Urso P., Giordani P.
      Pages 87-94

    10. Exploratory Analysis of Random Variables Based on Fuzzifications

      • Colubi A., González-Rodríguez G., Lubiano M.A., Montenegro M.
      Pages 95-102

    11. A Method to Simulate Fuzzy Random Variables

      • González-Rodríguez G., Colubi A., Gil M.A., Coppi R.
      Pages 103-110

    12. Friedman’s Test for Ambiguous and Missing Data

      • Edyta Mrówka, PrzemysÅ‚aw Grzegorzewski
      Pages 111-118

  4. Probability of Imprecisely-Valued Random Elements with Applications

    1. Front Matter

      Pages I-X
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    2. Measure-Free Martingales with Application to Classical Martingales

      • S.F. Cullender, W.-C. Kuo, C.C.A. Labuschagne, B.A. Watson
      Pages 121-128
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Keywords

About this book

The idea of soft computing emerged in the early 1990s from the fuzzy systems c- munity, and refers to an understanding that the uncertainty, imprecision and ig- rance present in a problem should be explicitly represented and possibly even - ploited rather than either eliminated or ignored in computations. For instance, Zadeh de?ned ‘Soft Computing’ as follows: Soft computing differs from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty and partial truth. In effect, the role model for soft computing is the human mind. Recently soft computing has, to some extent, become synonymous with a hybrid approach combining AI techniques including fuzzy systems, neural networks, and biologically inspired methods such as genetic algorithms. Here, however, we adopt a more straightforward de?nition consistent with the original concept. Hence, soft methods are understood as those uncertainty formalisms not part of mainstream s- tistics and probability theory which have typically been developed within the AI and decisionanalysiscommunity.Thesearemathematicallysounduncertaintymodelling methodologies which are complementary to conventional statistics and probability theory.

Editors and Affiliations

  • AI Group Department of Engineering Mathematics, University of Bristol, Bristol, UK

    Jonathan Lawry

  • Statistics and Operations Research, Rey Juan Carlos University, Spain

    Enrique Miranda

  • Intelligent Systems Group Department of Electronics & Computer Science, University of Santiago de Compostela Santiago de Compostela, Spain

    Alberto Bugarin

  • Department of Applied Mathematics, Beijing University of Technology, Beijing, P.R. China

    Shoumei Li

  • Dpto. Estadistica e I.O y D.M. Calle Calvo Sotelo s/n, Universiad de Oviedo Fac. Ciencias, Oviedo, Spain

    Maria Angeles Gil

  • Systems Research Institute Polish Academy of Sciences, Warsaw, Poland

    Przemys aw Grzegorzewski, Olgierd Hyrniewicz

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