Irrelevance and Independence Relations in quasi-Bayesian Networks
نویسنده
چکیده
This paper analyzes irrelevance and independence re lations in graphical models associated with convex sets of probability distributions (called Quasi-Bayesian networks) . The basic question in Quasi-Bayesian networks is, How can irrelevance/independence rela tions in Quasi-Bayesian networks be detected, enforced and exploited? This paper addresses these questions through Walley's definitions of irrelevance and inde pendence. Novel algorithms and results are presented for inferences with the so-called natural extensions us ing fractional linear programming, and the properties of the so-called type-1 extensions are clarified through a new generalization of d-separation.
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