Well you could draw samples of the parameters with a Bayesian setup via MCMC or get a distribution over them via a variational approximation, rather than getting some sort of maximum likelihood (MAP whatever) value for the parameters of the model via solving an optimization problem. This seems much more general (and practically useful). So I think it is the other way around regularizers are just priors (that you arrived at somehow).
