Abstract
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 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 problems. 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.
Users
Please
log in to take part in the discussion (add own reviews or comments).