A credal network is a graphical tool for representation and manipulation of uncertainty, where probability values may be imprecise or indeterminate. A credal network associates a directed acyclic graph with a collection of sets of probability measures; in this context, inference is the computation of tight lower and upper bounds for conditional probabilities. In this paper we present new algorithms for inference in credal networks based on multilinear programming techniques. Experiments indicate that these new algorithms have better performance than existing ones, in the sense that they can produce more accurate results in larger networks.
|Title of host publication||Second Starting AI Researcher Symposium (STAIRS)|
|Place of Publication||Valencia|
|Number of pages||12|
|Publication status||Published - 2004|
Bibliographical note(oral presentation, blind peer reviewed by >3 reviewers)
- Uncertainty and reasoning, Sets of probability measures, Bayesian networks, Multilinear programming.