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D'Agostini G. Bayesian reasoning in high-energy physics

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D'Agostini G. Bayesian reasoning in high-energy physics
CERN, 1999. — 174 p.
Bayesian statistics is based on the intuitive idea that probability quantifies the degree of belief in the occurrence of an event. The choice of name is due to the key role played by Bayes' theorem, as a logical tool to update probability in the light of new pieces of information. This approach is very close to the intuitive reasoning of experienced physicists, and it allows all kinds of uncertainties to be handled in a consistent way. Many cases of evaluation of measurement uncertainty are considered in detail in this report, including uncertainty arising from systematic errors, upper/lower limits and unfolding. Approximate methods, very useful in routine applications, are provided and several standard methods are recovered for cases in which the (often hidden) assumptions on which they are based hold.
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