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Bayesian statistics is based on the subjective definition
of probability as *``degree of belief''* and on Bayes' theorem,
the basic tool for assigning probabilities to hypotheses combining
*a priori* judgements and experimental information. This
was the original
point of view
of Bayes, Bernoulli, Gauss, Laplace, etc.
and contrasts with later ``conventional''
(pseudo-)definitions of probabilities,
which implicitly presuppose the concept of probability.
These notes^{3.1} show that the Bayesian
approach is the natural one for data analysis in the most general sense,
and for assigning uncertainties
to the results of physical measurements
- while at the same time
resolving philosophical aspects of the problem.
The approach, although
little known and usually misunderstood
among the high energy physics community,
has become the standard way of reasoning in
several fields of research
and has recently been adopted by the
international metrology organizations
in their recommendations for
assessing measurement uncertainty.
These notes describe
a general model for treating
uncertainties originating from random
and systematic errors
in a consistent way and
include examples of
applications of the model in high energy physics, e.g.
``confidence intervals'' in different contexts, upper/lower
limits, treatment of ``systematic errors'',
hypothesis tests and unfolding.

** Next:** Introduction to the ``primer''
** Up:** Subjective probability and Bayes'
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Giulio D'Agostini
2003-05-15