

Active Information Fusion 
Efficient Computation of Mutual
Information
In this approach, a Markov synergy chain between sensors is defined. (Please refer to the paper for details). It represents an ideal synergy relation between sensors. Given a Markov synergy chain with a set of sensors, the mutual information for a Markov synergy chain can be computed as a sum of mutual information of pairwise sensors and singleton sensors. Also, the mutual information of the sensor set is upperbounded by the mutual information of its corresponding Markov synergy chains. Although its corresponding Markov synergy chains are not unique, it is experimentally shown that the minimum mutual information (called least upper bound) among all the Markov synergy chains is very close to the real mutual information of the sensors. Thus, the real mutual information can be approximated by the least upper bound. Presentation Slides

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