  The generalized labeled multi-Bernoulli (GLMB) is a family of tractable
models that alleviates the limitations of the Poisson family in dynamic
Bayesian inference of point processes. In this paper, we derive closed form
expressions for the void probability functional and the Cauchy-Schwarz
divergence for GLMBs. The proposed analytic void probability functional is a
necessary and sufficient statistic that uniquely characterizes a GLMB, while
the proposed analytic Cauchy-Schwarz divergence provides a tractable measure of
similarity between GLMBs. We demonstrate the use of both results on a partially
observed Markov decision process for GLMBs, with Cauchy-Schwarz divergence
based reward, and void probability constraint.
