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Bayesian networks (BNs) represent joint space probabilities compactly and enable one to carry out efficient inferencing. Although the Dempster-Shafer (DS) belief theoretic framework captures a wider class of imperfections, its utility in such graphical models is limited. This is mainly due to the requirement of having to maintain a basic probability assignment (BPA) for the whole power set of propositions...
This panel position paper discusses advantages and challenges of multi agent fusion systems (MAS) with respect to the modeling flexibility and fusion reliability. We argue that the MAS paradigm in combination with rigorous modeling and inference methods can facilitate design of theoretically and technically sound fusion systems. This is illustrated with the help of a MAS approach to Bayesian fusion...
Bayesian networks are useful for predicting future activities on the battlefield. Bayesian mathematics provides the most benefit in JDL fusion levels 2+, i.e. situation, threat, and performance assessment. However, these networks are exceedingly difficult for the average person to develop, much less a soldier in the middle of a war. We are in the process of developing a Bayesian modeling aid that...
The traditional message passing algorithm developed by Pearl in 1980s provides exact inference for discrete poly-tree Bayesian networks. When there are multiple paths (loops) in the network, we can still apply Pearl's algorithm to provide approximate solutions and it is so-called "loopy propagation". However, when mixed random variables (continuous and discrete variables) are present in...
In this paper we show that causal probabilistic models can facilitate the design of robust and flexible fusion systems. Observed events resulting from stochastic causal processes can be modeled with the help of causal Bayesian networks, mathematically rigorous and compact probabilistic causal models. Bayesian networks explicitly represent conditional independence which facilitates decentralized modeling...
The construction of belief networks is a widely used methodology for high level fusion modeling. While some of the components of a belief network deal with ambiguous (probabilistic) data, others may deal with vague (possibilistic) data. Given the need to represent both probabilistic and possibilistic components in a single belief network, a framework and toolset for building Hybrid networks, utilizing...
This paper considers the accuracy of state estimation based on classification using Bayesian networks. It presents a method to localize network fragments that (i) are in a particular (rare) case responsible for a potential misclassification, or (ii) contain modeling errors that consistently cause misclassifications, even in common cases. We derive an algorithm that, within such fragments, can localize...
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