train and validation data and then for a range of priors you can compute the maximum posterior e and then C on the validation data which estimate of your maximum a-posteriori parameter gives the best performance on the validation data and that tells you that the prior you use to estimate that was the better prior that's the same thing in standard neural net learning you'd say I put some like coefficient in front of weights square because I want to keep the weights small the coefficient in front of that is a choice it's a hyper