Probability theory has been the only well-founded theory of
uncertainty for a long time. It was viewed either as a powerful
tool for modelling random phenomena, or as a rational approach to
the notion of degree of belief. During the last thirty years, in
areas centered around decision theory, artificial intelligence and
information processing, numerous approaches extending or orthogonal
to the existing theory of probability and mathematical statistics
have come to the front. The common feature of those attempts is to
allow for softer or wider frameworks for taking into account the
incompleteness or imprecision of information. Many of these
approaches come down to blending interval or fuzzy interval
analysis with probabilistic methods.
This book gathers contributions to the 4th International
Conference on Soft methods in Probability and Statistics. Its aim
is to present recent results illustrating such new trends that
enlarge the statistical and uncertainty modeling traditions,
towards the handling of incomplete or subjective information. It
covers a broad scope ranging from philosophical and mathematical
underpinnings of new uncertainty theories, with a stress on their
impact in the area of statistics and data analysis, to numerical
methods and applications to environmental risk analysis and
mechanical engineering. A unique feature of this collection is to
establish a dialogue between fuzzy random variables and imprecise
probability theories.
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