On cautious probabilistic inference and default detachment

On cautious probabilistic inference and default detachment,10.1007/BF02031721,Annals of Operations Research,Helmut Thöne,Werner KielSling,Ulrich Gfint

On cautious probabilistic inference and default detachment  
BibTex | RIS | RefWorks Download
Conditional probabilities are one promising and widely used approach to model uncertainty in information systems. This paper discusses the DUCK-calculus, which is founded on the cautious approach to uncertain probabilistic inference. Based on a set of sound inference rules, derived probabilistic information is gained by local bounds propagation techniques. Precision being always a central point of criticism to such systems, we demonstrate that DUCK need not necessarily suffer from these problems. We can show that the popular Bayesian networks are subsumed by DUCK, implying that precise probabilities can be deduced by local propagation techniques, even in the multiply connected case. A comparative study with INFERNO and with inference techniques based on global operations-research techniques yields quite favorable results for our approach. Since conditional probabilities are also suited to model nonmonotonic situations by considering different contexts, we investigate the problems of maximal and relevant contexts, needed to draw default conclusions about individuals.
Journal: Annals of Operations Research - Annals OR , vol. 55, no. 1, pp. 195-224, 1995
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.