MDL is analogous to Bayesian inference, where the information cost of the model CODE(M ) acts as the prior distribution over models P (M ), and CODE(D|M ), the information cost of the data given the model, acts as the likelihood function P (D|M ).
bayesian word segmentation methods may be considered related as well.