Results 301 to 310 of about 2,518,110 (330)
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Performance of Jensen Shannon Divergence in Incipient Fault Detection and Estimation
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019The diagnosis (Detection, Estimation and Isolation) of incipient faults, i.e. faults with severity variation
Xiaoxia Zhang, C. Delpha, D. Diallo
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Nonparametric Estimation of Conditional Information and Divergences
2018In this paper we propose new nonparametric estimators for a family of conditional mutual information and divergences. Our estimators are easy to compute; they only use simple k nearest neighbor based statistics. We prove that the proposed conditional information and divergence estimators are consistent under certain conditions, and demonstrate their ...
Barnabás Póczos, Jeff G. Schneider
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Estimates for the set of diverging points
Reports on Mathematical Physics, 1989Let \(F(x,y)=(f(x)+g(x),h(x))\) be a differentiable map from \(\mathbb{R}^ 2\) to \(\mathbb{R}^ 2\) and \((F^ n)^ \infty_{n=1}\) be the corresponding dynamical system. Here \(F^ n=F\circ F\circ \dots\circ F\) (\(n\) times). Suppose that the system is dissipative, i.e., \(\text{mes}_ 2F^ n(K)\to 0\) as \(n\to \infty\) for any compact \(K\subset\mathbb{R}
Sankat, K., Heinemann, V.
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Minimax estimation with divergence loss function
Information Sciences, 1974Abstract We propose a class of loss functions for parameter estimation obtained by considering the discrepancy between the probability distribution p.d. with the unknown parameter and the p.d. with the parameter replaced by the estimate, the discrepancy being measured by the divergence functional.
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Gene arrangement, phylogeny and divergence time estimation of mitogenomes in Thrips
Molecular Biology Reports, 2022Avas Pakrashi +4 more
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Divergence estimation based on deep neural networks and its use for language identification
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2016Yosuke Kashiwagi +3 more
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Estimating the Kullback–Leibler Divergence
2014We now investigate how the KLD rate can be estimated from a single empirical stationary trajectory, obtained from a stochastic stationary process whose dynamics is unknown. We assume that the empirical stationary trajectory contains \(n\) data of one or several random variables denoted by the letter \(X\).
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2015 Third International Conference on Digital Information, Networking, and Wireless Communications (DINWC), 2015
V. Korzhik, I. Fedyanin
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V. Korzhik, I. Fedyanin
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On the Minimum $\mathcal {K}$-Divergence Estimator
IEEE Transactions on Signal Processing, 2022Yair Sorek, Koby Todros
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Generalized EM Algorithms for Minimum Divergence Estimation
International Conference on Geometric Science of Information, 2015Diaa Al Mohamad, M. Broniatowski
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