Speaker
Giulio d'Agostini
(Rome)
Description
This paper reviews the basic ideas behind
a Bayesian unfolding published some years ago
and improves their implementation. In particular,
uncertainties are now treated at all levels by
probability density functions and their propagation
is performed by Monte Carlo integration.
Thus, small numbers are better handled and
the final uncertainty does not rely on the assumption
of normality.
Theoretical and practical issues concerning the
iterative use of the algorithm are also treated.
The new program, implemented in the R language, is freely available,
together with sample scripts to play with toy models.
