2016/084 - Bayesian Network Structure Learning with a Quotient Normalized Maximum Likelihood Criterion
- Tomi Silander
The Ninth Workshop on Information Theoretic Methods in Science and Engineering, Helsinki, Finland, 19-21 September 2016.
Learning the dependency structure of a multivariate distribution from the observational data is an important task since it allows us to speculate about the underlying causal mechanisms that induce dependencies. This learning task can be approached as a model selection problem, but recent studies have revealed that the popular Bayesian model selection criterion is not satisfactory. We review some of the information theoretic alternatives and introduce a new one called a quotient normalized maximum likelihood criterion