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Lecture Notes in Computer Science
Conditional independence provides an essential framework to deal with knowledge and uncertainty in Artificial Intelligence, and is fundamental in probability and multivariate statistics. Its associated implication problem is paramount for building Bayesian networks. Unfortunately, the problem does not enjoy a finite ground axiomatization and is already coNP-complete to decide for restricted subclasses. Saturated conditional independencies form an important subclass of conditional independenciesdoi:10.1007/978-3-642-32621-9_19 fatcat:6guu6v3g2nbife6txjprjodpau