3 research outputs found
Pivotal Pruning of Trade-offs in QPNs
Qualitative probabilistic networks have been designed for probabilistic
reasoning in a qualitative way. Due to their coarse level of representation
detail, qualitative probabilistic networks do not provide for resolving
trade-offs and typically yield ambiguous results upon inference. We present an
algorithm for computing more insightful results for unresolved trade-offs. The
algorithm builds upon the idea of using pivots to zoom in on the trade-offs and
identifying the information that would serve to resolve them.Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000
Positive dependence in qualitative probabilistic networks
Qualitative probabilistic networks (QPNs) combine the conditional
independence assumptions of Bayesian networks with the qualitative properties
of positive and negative dependence. They formalise various intuitive
properties of positive dependence to allow inferences over a large network of
variables. However, we will demonstrate in this paper that, due to an incorrect
symmetry property, many inferences obtained in non-binary QPNs are not
mathematically true. We will provide examples of such incorrect inferences and
briefly discuss possible resolutions.Comment: 10 pages, 3 figure
Pivotal pruning of trade-offs in QPNs
Qualitative probabilistic networks have been designed for probabilistic reasoning in a qualitative way. Due to their coarse level of representation detail, qualitative probabilistic networks do not provide for resolving trade-offs and typically yield ambiguous results upon inference. We present an algorithm for computing more insightful results for unresolved trade-offs. The algorithm builds upon the idea of using pivots to zoom in on the trade-offs and identifying the information that would serve to resolve them