Using the Kullback-Leibler Distance
Kullback Leibler Distance between Probabilistic Context Free Grammars and Probabilistic Finite Automata
... the Kullback-Leibler distance, also called the relative entropy, between a probabilistic context-free grammar and a probabilistic fi- nite ...the Kullback-Leibler distance, ...
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New Estimates of the Kullback-Leibler Distance and Applications
... = 0 and p ln p 0 = ∞. It is well-known that relative entropy is always non-negative and equal to zero if and only if p = q. However, it is not a true distance between distributions since it is not symmetric and ...
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A New Upper Bound for the Kullback-Leibler Distance and Applications
... Dragomir and point out that it can be better than the first one in certain cases. 1. Introduction The relative entropy is a measure of the distance between two distributions. In statistics, it arises as an ...
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Features detection based blind handover using kullback leibler distance for 5G HetNets systems
... 7. CONCLUSION In this work, we studied a new method to manage the handovers between a number of users and Base Stations of Small Cells. Our idea has been based on analysing the probability density function of the ...
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Clustering Time Series Based on Forecast Distributions Using Kullback-Leibler Divergence
... the Kullback-Leibler divergence [9] for clustering the forecasts at a future ...the Kullback-Leibler distance can be computed directly from the forecast means and vari- ances provided ...
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Complex temporal topic evolution modelling using the Kullback Leibler divergence and the Bhattacharyya distance
... 3.3 Interactive exploration and visualization Our final contribution comprises a web application which allows users to upload and analyse their data sets using the proposed framework. A screenshot of the initial ...
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Markov-switching model selection using Kullback–Leibler divergence
... The objective of this paper is to develop a new information criterion for simultaneous selection of states and variables in Markov switching models. To accomplish this goal, we obtain an explicit approximation to the KL ...
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Markov-Switching Model Selection Using Kullback-Leibler Divergence
... The objective of this paper is to develop a new information criterion for simultaneous selection of states and variables in Markov switching models. To accomplish this goal, we obtain an explicit approximation to the KL ...
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Building Context-Dependent DNN Acoustic Models using Kullback-Leibler Divergence-Based State Tying
... natural distance function for such distribu- tions is the Kullback-Leibler divergence ...the Kullback-Leibler divergence instead of Gaussian ...of Kullback-Leibler ...
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Image Retrieval via Kullback-Leibler Divergence of Patches of Multiscale Coefficients in the KNN Framework
... mations. In order to select the most significant patches, (i.e. those containing the most important information of the im- age), we selected the largest ones (either in the sense of over- all energy or in the sense of ...
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Kullback Leibler simplex
... KULLBACK-LEIBLER SIMPLEX Abstract. This technical reference presents the functional structure and the algorithmic implemen- tation of KL (Kullback-Leibler) simplex. It details the simplex ...
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Kullback Leibler Simplex
... KULLBACK-LEIBLER SIMPLEX Abstract. This technical reference presents the functional structure and the algorithmic implemen- tation of KL (Kullback-Leibler) simplex. It details the simplex ...
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Bounds for Kullback-Leibler divergence
... The purpose of this paper is to present new bounds for relative entropy D ( p || q ) of two probability distributions and then to apply them to simple entropy and mutual information1. Th[r] ...
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How To Calculate The Kullback-Leibler Divergence And The Kulback-Kullback Divergence
... This distance can be used to define the covering radius to design an optimal (with smallest number of nodes) grid in the parameter ...the distance to determine the quality of the search templates - ...
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Kullback-Leibler Proximal Variational Inference
... We propose a new variational inference method based on a proximal framework that uses the Kullback-Leibler (KL) divergence as the proximal term. We make two contributions towards exploiting the geometry and ...
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Gaussian Kullback-Leibler Approximate Inference
... Section 5.1.1, and its derivative needs to be computed. Second, these algorithms use approximate methods to evaluate the marginal variances that are required to drive local variational bound opti- misation. Marginal ...
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Kullback-Leibler Divergence for the Normal-Gamma Distribution
... contrast using a parametric modulator (PM) that is given as PM = deg/90 ° with deg = (0 ° , 30 ° , 60 ° , 90 ° ), resulting in PM = (0, 1 / 3 , 2 / 3 , 1), such that the parametric modulator is proportional to ...
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A Kullback Leibler Divergence for Bayesian Model Diagnostics
... overcome the challenges associated with prior elicitation in calculating KLD under the Bayesian framework, one may consider the Bayesian estimate of the Kullback- Leibler projection by Goutis and Robert ...
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Analysis of Kullback-Leibler Divergence for Masquerade Detection
... Results from the PPI algorithms show that the padding-first PPI for distance D1, was able to identify 15% to 42% of the commands as padding during the training phase, whereas attack-first PPI identified 11% or ...
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A non symmetric divergence and kullback leibler divergence measure
... Leibler divergence measure have been studied. Numerical bounds of new divergence measures are also discussed. Copyright © 2015 Ram Naresh Saraswat. This is an open access article distributed under the Creative ...
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