Reintroducing Credal Networks under Epistemic Irrelevance

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Jasper De Bock ;
Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:123-135, 2016.

Abstract

A credal network under epistemic irrelevance is a generalised version of a Bayesian network that loosens its two main building blocks. On the one hand, the local probabilities do not have to be specified exactly. On the other hand, the assumptions of independence do not have to hold exactly. Conceptually, these credal networks are elegant and useful. However, in practice, they have long remained very hard to work with, both theoretically and computationally. This paper provides a general introduction to this type of credal networks and presents some promising new theoretical developments that were recently proved using sets of desirable gambles and lower previsions. We explain these developments in terms of probabilities and expectations, thereby making them more easily accessible to the Bayesian network community.

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