1 More precisely , one should write for the evidence p(d|model ) , in order to show explicitly that it is conditional on the assumption that the model is the true theory . From there one can further employ Bayes ' theorem to obtain the posterior probability for the model 's parameters given the observed data , namely p(model|d ) . This is the subject of Bayesian model comparison ( see e.g. [ 21 ] for an illustration ) . Here we do not employ the evidence for this purpose ( see instead [ 10,16 ] for applications to the CMSSM ) , and therefore drop the explicit conditioning on the model under study , although in the following one should always interpret p(d ) ≡ p(d|model ) .