Bayesian entropy network for fusion of different types of information

Abstract

A hybrid method for information fusion combining the maximum entropy (ME) method with the classical Bayesian network is proposed as the Bayesian-Entropy Network (BEN) in this paper. The key benefit of the proposed method is the capability to handle various types of information for classification and updating, such as classical point data, abstracted statistical information, and range data. The detailed derivation of the proposed is given and special focus is on the formulation of different types of information as constraints embedded in the entropy part. The Bayesian part is used to handle classical point observation data. Next, an adaptive algorithm is proposed to mitigate the impact of wrong information constraints on the final posterior distribution estimation. Following this, several examples are used to demonstrate the proposed methodology and application to engineering problems. It is shown that the proposed method is a generalized form of classical Bayesian method, and can take advantage of the extra information. This advantage is preferable in many engineering applications especially when the number of point observations is limited. Conclusions and future work are drawn based on the current study.