176 citations · 672 across the 18 of their papers we have counts for
18 papers
Computing Probability Intervals Under Independency Constraints
Linda C. van der Gaag
Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a full specification of a joint probability distribution is availa…
Elicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information
Marek J. Druzdzel, Linda C. van der Gaag
Although the usefulness of belief networks for reasoning under uncertainty is widely accepted, obtaining numerical probabilities that they require is still perceived a major obstac…
How to Elicit Many Probabilities
Linda C. van der Gaag, Silja Renooij, Cilia L. M. Witteman +2
In building Bayesian belief networks, the elicitation of all probabilities required can be a major obstacle. We learned the extent of this often-cited observation in the constructi…
Enhancing QPNs for Trade-off Resolution
Silja Renooij, Linda C. van der Gaag
Qualitative probabilistic networks have been introduced as qualitative abstractions of Bayesian belief networks. One of the major drawbacks of these qualitative networks is their c…
Pivotal Pruning of Trade-offs in QPNs
Silja Renooij, Linda C. van der Gaag, Simon Parsons +1
Qualitative probabilistic networks have been designed for probabilistic reasoning in a qualitative way. Due to their coarse level of representation detail, qualitative probabilisti…
Making Sensitivity Analysis Computationally Efficient
Uffe Kjærulff, Linda C. van der Gaag
To investigate the robustness of the output probabilities of a Bayesian network, a sensitivity analysis can be performed. A one-way sensitivity analysis establishes, for each of th…