Inference Meta Models: A New Perspective On Belief Propagation With Bayesian Net- works

We investigate properties of Bayesian networks (BNs) in the context of robust state estimation. We focus on problems where state estimation can be viewed as a classification of the possible states, which in turn is based on the fusion of heterogeneous and noisy information. We introduce a coarse perspective of the inference processes and show that classification with BNs can be very robust, even if we use models and evidence associated with significant uncertainties. By making coarse and realistic assumptions we can (i) formulate asymptotic properties of the classification performance, (ii) identify situations in which Bayesian fusion supports robust inference and (iii) introduce techniques that support detection of potentially misleading inference results at runtime. The presented coarse grained analysis from the runtime perspective is relevant for an important class of real world fusion problems where it is difficult to obtain domain models that precisely describe the true probability distributions over different states.