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—We present a method for estimating the KL divergence between continuous densities and we prove it converges almost surely. Divergence estimation is typically solved estimating the densities first. Our main result shows this intermediate step is unnecessary and that the divergence can

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

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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

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