This book provides a multi-level introduction to Bayesian reasoning
(as opposed to "conventional statistics") and its applications to
data analysis. The basic ideas of this "new" approach to the
quantification of uncertainty are presented using examples from
research and everyday life. Applications covered include:
parametric inference; combination of results; treatment of
uncertainty due to systematic errors and background; comparison of
hypotheses; unfolding of experimental distributions; upper/lower
bounds in frontier-type measurements. Approximate methods for
routine use are derived and are shown often to coincide - under
well-defined assumptions! - with "standard" methods, which can
therefore be seen as special cases of the more general Bayesian
methods. In dealing with uncertainty in measurements, modern
metrological ideas are utilized, including the ISO classification
of uncertainty into type A and type B. These are shown to fit well
into the Bayesian framework.
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