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We present a new approach to credal nets, which are graphical models thatgeneralise Bayesian nets to imprecise probability. Instead of applying thecommonly used notion of strong independence, we replace it by the weaker notionof epistemic irrelevance. We show how assessments of epistemic irrelevanceallow us to construct a global model out of given local uncertainty models andmention some useful properties. The main results and proofs are presented usingthe language of sets of desirable gambles, which provides a very general andexpressive way of representing imprecise probability models.

Credal nets under epistemic irrelevance