Thoughts on belief and model revision with uncertain evidence

Bayesian networks and other graphical probabilistic models became a popular framework for reasoning with uncertainty. Efficient methods have been developed for revising beliefs with new evidence. However, when the evidence is uncertain, i.e. represented by a probability distribution, different methods have been proposed. In this paper we analyze and compare these methods. The goal is to show in what cases they can be used correctly