Abstract—Rényi divergence is related to Rényi entropy much like Kullback-Leibler divergence is related to Shannon’s entropy, and comes up in many settings. It was introduced by Rényi as a measure of information that satisfies almost the same axioms as Kullback-Leibler divergence, and depends

Abstract—In this work, we provide a computable expression for the Kullback–Leibler divergence rate lim ( ) between two time-invariant finite-alphabet Markov sources of arbitrary order and arbitrary initial distributions described by the probability distributions and, respectively. We illustrate

Abstract—This paper investigates the asymptotics of Kullback-Leibler divergence between two probability distributions satisfying a Central Limit Theorem prop-erty. The basic problem is as follows. Let Xi, i ∈ N, be a sequence of independent random variables such that the sum Sn = ∑n i=1Xi has

Multiresolution data arise when an object or a phenomenon is described at several levels of detail Multiresolution data is prevalent in many application areas F Examples include biology, computer vision Faster growth of multiresolution data is expected in future Over the years, data accumulates in multiple resolutions because

-pereieicoe " of the space X. The partition which best describes the clustering structure of the da.ta. is defined to be the one which maximises a certain criterion function. This criterion function is a weighted sum of Kullback-Leibler divergences.

Abstract not found

probabilities. The UCRL2 algorithm by Auer, Jaksch and Ortner (2009), which follows this strategy, has recently been shown to guarantee near-optimal regret bounds. In this paper, we strongly argue in favor of using the Kullback-Leibler (KL) divergence for this purpose. By studying the linear maximization

Abstract — In this paper, an objective function for training a fault tolerant neural network is derived based on the idea of Kullback-Leibler (KL) divergence. The new objective function is then applied to a radial basis function (RBF) network that is with multiplicative weight noise. Simulation

This paper considers a Kullback-Leibler distance (KLD) which is asymptotically equivalent to the KLD by Goutis and Robert [1] when the reference model (in comparison to a competing fitted model) is correctly specified and that certain regularity conditions hold true (ref. Akaike [2]). We derive

Abstract not found