The question of how to combine experimental results
that `appear' to be in mutual disagreement, treated
in detail years ago in a previous paper, is revisited.
The first novelty of the present note
is the explicit use of graphical models, in order to make
the deterministic and probabilistic links between
the variables of interest more evident.
Then, instead of
aiming for results in closed formulae,
the integrals of interest are evaluated
by
Markov Chain Monte Carlo (MCMC) sampling, with
the algorithms (typically
Gibbs Sampler)
implemented in the package
JAGS
(“Just Another Gibbs Sampler”).
For convenience, the
JAGS functions are
called from
R scripts, thus gaining
the advantage given by the rich collection of mathematical, statistical
and graphical functions included in the
R installation.
The results of the previous paper are thus easily re-obtained
and the method is applied to the determination
of the charged kaon mass. This note, based on lectures
to PhD students and young researchers has been written
with a didactic touch, and the relevant
JAGS/rjags
code is provided. (A
curious bias arising from the sequential
application of the
scaling prescription to
`apparently' discrepant results, found
here, will be discussed in more detail in a separate paper.)
mass determination
