Defining Classes of Influences for the Acquisition of Probability Constraints for Bayesian Networks

The task of eliciting all probabilities required for a Bayesian network can be supported by first acquiring qualitative constraints on the numerical quantities to be obtained. Building upon the concept of qualitative influence, we analyse such constraints and define a small number of influence classes. Based upon these classes, we present a method for efficiently acquiring the qualitative constraints that should be satisfied by the network's probabilities.

• Type: Conference
• Year: 2004
• Where: ECAI
• Authors: Linda C. van der Gaag, Eveline M. Helsper
• Keywords: Artificial Intelligence, Bayesian Network, ECAI 2004, Qualitative Constraints, Qualitative Influence