Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
This work addresses a specific computational bottleneck in probabilistic reasoning for researchers and practitioners using Bayesian networks, though it appears incremental in nature.
The paper tackles the problem of ambiguous qualitative relationships (tradeoffs) in Bayesian networks by presenting two incremental techniques that combine qualitative and numeric probabilistic reasoning to resolve these tradeoffs. The result is systematic methods that potentially offer lower computational cost compared to purely numeric approaches.
Qualitative probabilistic reasoning in a Bayesian network often reveals tradeoffs: relationships that are ambiguous due to competing qualitative influences. We present two techniques that combine qualitative and numeric probabilistic reasoning to resolve such tradeoffs, inferring the qualitative relationship between nodes in a Bayesian network. The first approach incrementally marginalizes nodes that contribute to the ambiguous qualitative relationships. The second approach evaluates approximate Bayesian networks for bounds of probability distributions, and uses these bounds to determinate qualitative relationships in question. This approach is also incremental in that the algorithm refines the state spaces of random variables for tighter bounds until the qualitative relationships are resolved. Both approaches provide systematic methods for tradeoff resolution at potentially lower computational cost than application of purely numeric methods.