BAYESIAN REASONING IN DATA ANALYSIS
A Critical Introduction
by Giulio D'Agostini (University of Rome “La Sapienza”, Italy)
Giulio D'Agostini is an experimental particle physicist and an associate professor at the University of Rome “La Sapienza”. He has collaborated in large frontier-type experiments at the international laboratories of CERN and DESY, working on various aspects of the construction and the operation of detectors, and analysing the resulting data. He has also performed several re-analyses of data produced by other experiments. Physics topics to which he has contributed include: study of the force between quarks and gluons; quark fragmentation; heavy quark decay; proton and photon structure functions; new particle searches (dibaryons, excited quarks, supersymmetric particles, electron compositeness, Higgs particle).
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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