  We perform a forecast of the CMSSM for the LHC based in an improved Bayesian
analysis taking into account the present theoretical and experimental wisdom
about the model. In this way we obtain a map of the preferred regions of the
CMSSM parameter space and show that fine-tuning penalization arises from the
Bayesian analysis itself when the experimental value of Mz is considered. The
results are remarkable stable when using different priors
