Results 61 to 70 of about 255,284 (162)
Kullback-Leibler divergence for the Fr\'echet extreme-value distribution [PDF]
, 2023 We derive a closed-form solution for the Kullback-Leibler divergence between two Fr\'echet extreme-value distributions.
Pain, Jean-Christophe
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Uniqueness and Optimality of Dynamical Extensions of Divergences
PRX Quantum, 2021 We introduce an axiomatic approach for channel divergences and channel relative entropies that is based on three information-theoretic axioms of monotonicity under superchannels, i.e., generalized data processing inequality, additivity under tensor ...
Gilad Gour
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Process monitoring based on Kullback Leibler divergence [PDF]
2013 European Control Conference (ECC), 2013 This article proposes to monitor industrial process faults using Kullback Leibler (KL) divergence. The main idea is to measure the difference between the distributions of normal and faulty data. Sensitivity analysis on the KL divergence under Gaussian distribution assumption is performed, which shows that the sensitivity of KL divergence increases with
Jiusun Zeng +5 more
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Kullback-Leibler divergence as a function of the contrast.
, 2013 Average Kullback-Leibler divergence ±95% confidence interval at the different contrast (N = 12). For each recorded cell the Kullback-Leibler divergence was estimated at each instance of time and was subsequently averaged across time giving a single ...
Anne-Kathrin Warzecha (57382) +3 more
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Balancing Reconstruction Error and Kullback-Leibler Divergence in Variational Autoencoders
IEEE Access, 2020 Likelihood-based generative frameworks are receiving increasing attention in the deep learning community, mostly on account of their strong probabilistic foundation.
Andrea Asperti, Matteo Trentin
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Physical Review ResearchWe demonstrate a model-free data analysis framework that leverages escort-weighted Shannon entropy and several divergence matrices to detect phase transitions in scattering and imaging datasets.
Jared Coles +11 more
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A Kullback–Leibler divergence method for input–system–state identification [PDF]
The capability of a novel Kullback–Leibler divergence method is examined herein within the Kalman filter framework to select the input–parameter–state estimation execution with the most plausible results.
Impraimakis, M. +2 more
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Notes on Kullback-Leibler Divergence and Likelihood
CoRR, 2014 The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that quantifies the proximity of two probability distributions. Although difficult to understand by examining the equation, an intuition and understanding of the KL divergence arises from its intimate relationship with likelihood theory.
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Vector Quantization by Minimizing Kullback-Leibler Divergence
CoRR, 2015 This paper proposes a new method for vector quantization by minimizing the Kullback-Leibler Divergence between the class label distributions over the quantization inputs, which are original vectors, and the output, which is the quantization subsets of the vector set.
Lan Yang +4 more
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The McMillan Theorem for Colored Branching Processes and Dimensions of Random Fractals
Entropy, 2014 For the simplest colored branching process, we prove an analog to the McMillan theorem and calculate the Hausdorff dimensions of random fractals defined in terms of the limit behavior of empirical measures generated by finite genetic lines.
Victor Bakhtin
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