Comparison of Nml and Bayesian Scoring Criteria for Learning Parsimonious Markov Models

Parsimonious Markov models, a generalization of variable order Markov models, have been recently introduced for modeling biological sequences. Up to now, they have been learned by Bayesian approaches. However, there is not always sufficient prior knowledge available and a fully uninformative prior is difficult to define. In order to avoid cumbersome cross validation procedures for obtaining the optimal prior choice, we here adapt scoring criteria for Bayesian networks that approximate the Normalized Maximum Likelihood (NML) to parsimonious Markov models. We empirically compare their performance with the Bayesian approach by classifying splice sites, an important problem from computational biology.