We present node level primitives for parallel exact inference on an arbitrary Bayesian network. We explore the probability representation on each node of Bayesian networks and eac...
Recent work in applying causal modeling (Bayesian networks) to software engineering has resulted in improved decision support systems for software project managers. Once the causa...
Peter Hearty, Norman E. Fenton, Martin Neil, Patri...
Empirical evidence shows that naive Bayesian classifiers perform quite well compared to more sophisticated network classifiers, even in view of inaccuracies in their parameters. I...
As postgenomic biology becomes more predictive, the ability to infer rate parameters of genetic and biochemical networks will become increasingly important. In this paper, we expl...
Software estimation models should support managerial decision making in software projects. We experience that most of current models do not achieve this goal to the extend manager...