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Data processing has become essential to modern civilization. The
original data for this processing comes from measurements or from
experts, and both sources are subject to uncertainty.
Traditionally, probabilistic methods have been used to process
uncertainty. However, in many practical situations, we do not know
the corresponding probabilities: in measurements, we often only
know the upper bound on the measurement errors; this is known as
interval uncertainty. In turn, expert estimates often include
imprecise (fuzzy) words from natural language such as "small"; this
is known as fuzzy uncertainty. In this book, leading specialists on
interval, fuzzy, probabilistic uncertainty and their combination
describe state-of-the-art developments in their research areas.
Accordingly, the book offers a valuable guide for researchers and
practitioners interested in data processing under uncertainty, and
an introduction to the latest trends and techniques in this area,
suitable for graduate students.
Data processing has become essential to modern civilization. The
original data for this processing comes from measurements or from
experts, and both sources are subject to uncertainty.
Traditionally, probabilistic methods have been used to process
uncertainty. However, in many practical situations, we do not know
the corresponding probabilities: in measurements, we often only
know the upper bound on the measurement errors; this is known as
interval uncertainty. In turn, expert estimates often include
imprecise (fuzzy) words from natural language such as "small"; this
is known as fuzzy uncertainty. In this book, leading specialists on
interval, fuzzy, probabilistic uncertainty and their combination
describe state-of-the-art developments in their research areas.
Accordingly, the book offers a valuable guide for researchers and
practitioners interested in data processing under uncertainty, and
an introduction to the latest trends and techniques in this area,
suitable for graduate students.
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