Over the last thirty years there has been a growing interest in extending the theory of probability and statistics to allow for more flexible modelling of uncertainty, ignorance and fuzziness. Most such extensions result in a "softening" of the classical theory, to allow for imprecision in probability judgements and to incorporate fuzzy constraints and events. Many approaches utilise concepts, tools and techniques developed in theories such as fuzzy set theory, possibility theory, imprecise probability theory and Dempster-Shafer theory.
The need for soft extensions of probability theory is becoming apparent in a wide range of applications areas. For example, in data analysis and data mining it is becoming increasingly clear that integrating fuzzy sets and probability can lead to more robust and interpretable models that better capture both the inherent uncertainty and fuzziness of the underlying data. Also, in science and engineering the need to analyse and model the true uncertainty associated with complex systems requires a more sophisticated representation of ignorance than that provided by uninformative Bayesian priors.
Soft Methods in Probability and Statistics (SMPS) 2006 will be hosted by the Artificial Intelligence Group, Department of Engineering Mathematics at the University of Bristol, UK. This is the third of a series of biennial conferences organized in 2002 by the Systems Research Institute from the Polish Academy of Sciences in Warsaw (SMPS 2002) and in 2004 by the Department of Statistics and Operation Research at the University of Oviedo in Spain (SMPS 2004).
SMPS 2006 aims to provide a forum for researchers to present and discuss ideas, theories, and applications. The scope of conference is to bring together experts representing all existing and novel approaches to soft probability and statistics. In particular, we would welcome papers combining probability and statistics with fuzzy logic, applications of the Dempster-Shafer theory, possibility theory, generalized theories of uncertainty, generalized random elements, generalized probabilities and so on.
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