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...

We investigate Gaussian Kullback-Leibler (G-KL) variational approximate inference techniques for Bayesian generalised linear models and various extensions. In particular we make the following novel contributions: sufficient conditions for which the G-KL objective is differentiable and convex are described; constrained parameterisations of Gaussian covariance that make G-KL methods fast and scal...

In this paper, we investigate some inferential properties of the upper record Lomax distribution. Also, we will estimate the upper record of the Lomax distribution parameters using methods, Moment (MME), Maximum Likelihood (MLE), Kullback-Leibler Divergence of the Survival function (DLS) and Baysian. Finally, we will compare these methods using the Monte Carlo simulation.