Improving Bayesian Network parameter learning using constraints

This paper describes a new approach to unify constraints on parameters with training data to perform parameter estimation in Bayesian networks of known structure. The method is general in the sense that any convex constraint is allowed, which includes many proposals in the literature. Driven by a maximum entropy criterion and the imprecise Dirichlet model, we present a constrained convex optimization formulation to combine priors, constraints and data. Experiments indicate benefits of this framework.