In this paper, we study the problem of information preservation when decomposing a single Bayesian network into a set of smaller Bayesian networks. We present a method that lossle...
Obtaining a bayesian network from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper, we define an automatic learni...
Bayesian networks are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. We will analyze Bayesian network...
Bayesian Network structures with a maximum in-degree of k can be approximated with respect to a positive scoring metric up to an factor of 1/k. Key words: approximation algorithm,...