  This paper studies the quantization of prior probabilities, drawn from an
ensemble, for distributed detection and data fusion. Design and performance
equivalences between a team of N agents tied by a fixed fusion rule and a more
powerful single agent are obtained. Effects of identical quantization and
diverse quantization are compared. Consideration of perceived common risk
enables agents using diverse quantizers to collaborate in hypothesis testing,
and it is proven that the minimum mean Bayes risk error is achieved by diverse
quantization. The comparison shows that optimal diverse quantization with K
cells per quantizer performs as well as optimal identical quantization with
N(K-1)+1 cells per quantizer. Similar results are obtained for maximum Bayes
risk error as the distortion criterion.
