Hi,

P(Model) is usually related to the dimension of the model (number of parameters). The more parameters, the less likely the model (a form of the razor we all know and love).

See these:

http://en.wikipedia.org/wiki/Bayesian_information_criterion http://en.wikipedia.org/wiki/Akaike_information_criterion

There are other ways of learning causal structure, based on ruling out graphs not consistent with constraints found in the data. These do not rely on priors, but have their own problems.