We derive qualitative relationships about the informationalrelevance of variables in graphical decision models based on a consideration of the topology of the models. Speci cally,...
We propose a decision-analytical approach to comparing the flexibility of decision situations from the perspective of a decisionmaker who exhibits constant risk-aversion over a mo...
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be ...
Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a ...
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly ...