  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 {\em
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 $\sqrt{\chi^2/\nu}$ scaling prescription to
'apparently' discrepant results, found here, will be discussed in more detail
in a separate paper.)
