Inference Meta Models: Towards Robust Information Fusion with Bayesian Networks

Abstract

This paper discusses the properties of Bayesian networks (BNs) in the context of accurate state estimation. We focus on a relevant class of problems where state estimation can be viewed as a classification of possible states based on the fusion of heterogeneous and noisy information. We introduce the inference meta model (IMM), a coarse runtime perspective on the inference processes which facilitates the analysis of the state estimation with BNs. By making coarse and realistic assumptions, we show that such inference can be very robust and has asymptotic properties regarding the fusion accuracy, even if we use models and evidence associated with significant uncertainties. Moreover, the IMM provides guidance for the development of (i) robust fusion systems and (ii) methods for runtime detection of potentially misleading fusion results