Results 131 to 140 of about 255,284 (162)

The fractional Kullback–Leibler divergence

Journal of Physics A: Mathematical and Theoretical, 2021
Abstract The Kullback–Leibler divergence or relative entropy is generalised by deriving its fractional form. The conventional Kullback–Leibler divergence as well as other formulations emerge as special cases. It is shown that the fractional divergence encapsulates different relative entropy states via the manipulation of the ...
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Kullback-Leibler Divergence Revisited

Proceedings of the ACM SIGIR International Conference on Theory of Information Retrieval, 2017
Thee KL divergence is the most commonly used measure for comparing query and document language models in the language modeling framework to ad hoc retrieval. Since KL is rank equivalent to a specific weighted geometric mean, we examine alternative weighted means for language-model comparison, as well as alternative divergence measures.
Fiana Raiber, Oren Kurland
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Distributions of the Kullback–Leibler divergence with applications

British Journal of Mathematical and Statistical Psychology, 2011
The Kullback–Leibler divergence (KLD) is a widely used method for measuring the fit of two distributions. In general, the distribution of the KLD is unknown. Under reasonable assumptions, common in psychometrics, the distribution of the KLD is shown to be asymptotically distributed as a scaled (non‐central) chi‐square with one ...
Belov, Dmitry I., Armstrong, Ronald D.
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Bivariate Kullback–Leibler divergence

Communications in Statistics - Theory and Methods
N Unnikrishnan Nair, P G Sankaran
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Kullback–Leibler divergence for evaluating bioequivalence

Statistics in Medicine, 2003
AbstractIn this paper we propose a methodology for evaluating the bioequivalence of two formulations of a drug that encompasses not only average bioequivalence (ABE), but also the more recently introduced measures of population bioequivalence (PBE) and individual bioequivalence (IBE). The latter two measures are concerned with prescribability (PBE) and
Vladimir, Dragalin, Valerii, Fedorov, Scott, Patterson, Byron, Jones   +3 more
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Kullback–Leibler divergence: A quantile approach

Statistics & Probability Letters, 2016
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P.G. Sankaran, S.M. Sunoj, N. Unnikrishnan Nair   +2 more
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Kullback-Leibler Divergence-Based Visual Servoing

2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2021
This paper proposes a Kullback-Leibler (K-L) divergence-based visual servoing scheme. K-L divergence, also known as relative entropy, is a measure of the difference between two probability distributions. By employing the K-L divergence as a new error metric to evaluate the similarity between the actual and desired images, and then formulating the ...
Xiangfei Li, Huan Zhao 0001, Han Ding 0001   +2 more
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The Kullback-Leibler Divergence and Nonnegative Matrices

IEEE Transactions on Information Theory, 2006
This correspondence establishes an interesting connection between the Kullback-Leibler divergence and the Perron root of nonnegative irreducible matrices. In the second part of the correspondence, we apply these results to the power control problem in wireless communications networks to show a fundamental tradeoff between fairness and efficiency.
Holger Boche, Slawomir Stanczak
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Use of Kullback–Leibler divergence for forgetting

International Journal of Adaptive Control and Signal Processing, 2008
AbstractNon‐symmetric Kullback–Leibler divergence (KLD) measures proximity of probability density functions (pdfs). Bernardo (Ann. Stat.1979;7(3):686–690) had shown its unique role in approximation of pdfs. The order of the KLD arguments is also implied by his methodological result.
Kárný, Miroslav, Andrýsek, Josef
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Complex NMF with the generalized Kullback-Leibler divergence

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
We previously introduced a phase-aware variant of the non-negative matrix factorization (NMF) approach for audio source separation, which we call the “Complex NMF (CNMF).” This approach makes it possible to realize NMF-like signal decompositions in the complex time-frequency domain. One limitation of the CNMF framework is that the divergence measure is
Hirokazu Kameoka, Hideaki Kagami, Masahiro Yukawa   +2 more
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