Bayesian Network Learning with Parameter Constraints

The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of training data. This paper considers a variety of types of domain knowledge for constraining parameter estimates when learning Bayesian networks. In particular, we consider domain knowledge that constrains the values or relationships among subsets of parameters in a Bayesian network with known structure. We incorporate a wide variety of parameter constraints into learning procedures for Bayesian networks, by formulating this task as a constrained optimization problem. The assumptions made in module networks, dynamic Bayes nets and context specific independence models can be viewed as particular cases of such parameter constraints. We present closed form solutions or fast iterative algorithms for estimating parameters subject to several specific classes of parameter constraints, including equalities and inequali...

• Type: Journal
• Year: 2006
• Where: JMLR
• Authors: Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat Rao
• Keywords: Bayesian Network, JMLR 2006, Parameter Constraints, Parameters