  The aim of this paper, triggered by some discussions in the astrophysics
community raised by astro-ph/0508529, is to introduce the issue of `fits' from
a probabilistic perspective (also known as Bayesian), with special attention to
the construction of model that describes the `network of dependences' (a
Bayesian network) that connects experimental observations to model parameters
and upon which the probabilistic inference relies. The particular case of
linear fit with errors on both axes and extra variance of the data points
around the straight line (i.e. not accounted by the experimental errors) is
shown in detail. Some questions related to the use of linear fit formulas to
log-linearized exponential and power laws are also sketched, as well as the
issue of systematic errors.
