We study cross-validation as a scoring criterion for learning dynamic Bayesian network models that generalize well. We argue that cross-validation is more suitable than the Bayesian scoring criterion for one of the most common interpretations of generalization. We confirm this by carrying out an experimental comparison of cross-validation and the Bayesian scoring criterion, as implemented by the Bayesian Dirichlet metric and the Bayesian information criterion. The results show that cross-validation leads to models that generalize better for a wide range of sample sizes.
"KAUST shall be a beacon for peace, hope and reconciliation, and shall serve the people of the Kingdom and the world."
King Abdullah bin Abdulaziz Al Saud, 1924 – 2015
Thuwal 23955-6900, Kingdom of Saudi Arabia