Discrete normal distributions are defined as the with prescribed means and covariance matrices which maximize entropy on integer lattice support. The set of discrete form an exponential family cumulant function related to Riemann theta function. In this paper, we present several formula for common statistical divergences between including Kullback-Leibler divergence. particular, describe effici...

With increasingly more databases becoming available on the Internet, there is a growing opportunity to globalise knowledge discovery and learn general patterns, rather than restricting learning to specific databases from which the rules may not be generalisable. Clustering of distributed databases facilitates learning of new concepts that characterise common features of, and differences between...

To facilitate the process of discovering brain structure-function associations from image and clinical data, we have developed classification tools for brain image data that are based on measures of dissimilarity between probability distributions. We propose statistical as well as non-statistical methods for classifying three dimensional probability distributions of regions of interest (ROIs) i...