most citedElicitation of Probabilities for Belief Networks: Combining Qualitative and Quantitative Information

176 citations · 625 across the 15 of their papers we have counts for

collaborators

15 papers

cs.AI2013176 cited

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…

cs.AI2013101 cited

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…

cs.AI201328 cited

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…

cs.AI201312 cited

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…

cs.AI2013166 cited

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…

cs.AI201341 cited

Analysing Sensitivity Data from Probabilistic Networks

Linda C. van der Gaag, Silja Renooij

With the advance of efficient analytical methods for sensitivity analysis ofprobabilistic networks, the interest in the sensitivities revealed by real-life networks is rekindled. A…