A bayesian approach seems to be very efficient in the scan of the 24-dimesional
parameter space of a Minimal SuperSymmetry Model: in fact, differently from a
scan, experimental constrains and data can be easily introduce into the analysis.
example, a supposed detection of new particles at colliders will provide a
of measurements able to guide us to the region in the SUSY parameter space
fit the data.
This technique can easily be extended to include results from an assumed
an experiment of Dark Matter Direct Detection so that, in the hypothesis that
made of the lightest neutralino, from the combination of accelerator observables
measurements from DM searches, the SUSY parameters can be further
There results can be used to obtain informations on the DM candidate, testing
assumptions about, e.g., the local DM density and the average velocity.
I will briefly introduce the basis of the bayesian analysis and present some