A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks

This work provides a theoretical foundation for prior selection in Bayesian network learning, but it is incremental as it builds on existing assumptions.

The paper presents a new characterization of the Dirichlet distribution, showing that under certain assumptions used in learning Bayesian networks, a Dirichlet prior on parameters is unavoidable.