Results 31 to 40 of about 105,427 (308)
Analysis of Meditation vs. Sensory Engaged Brain States Using Shannon Entropy and Pearson's First Skewness Coefficient Extracted from EEG Data. [PDF]
Sensors (Basel), 2023 Davis JJJ, Kozma R, Schübeler F.
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Quantifying Information via Shannon Entropy in Spatially Structured Optical Beams. [PDF]
Research (Wash D C), 2021 Solyanik-Gorgone M +5 more
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Few generalized entropic relations related to Rydberg atoms
Scientific Reports, 2022 We calculate the analytical and numerical values of the position space Shannon entropy, momentum space Shannon entropy, and total Shannon entropy, $$S_\rho$$ S ρ , $$S_\gamma$$ S γ , and $$S_T$$ S T , respectively, of free and trapped Rydberg hydrogen ...
Kirtee Kumar, Vinod Prasad
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Estimating the variance of Shannon entropy [PDF]
Physical Review E, 2021 The statistical analysis of data stemming from dynamical systems, including, but not limited to, time series, routinely relies on the estimation of information theoretical quantities, most notably Shannon entropy. To this purpose, possibly the most widespread tool is provided by the so-called plug-in estimator, whose statistical properties in terms of ...
Ricci, Leonardo +2 more
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Analyzing boron oxide networks through Shannon entropy and Pearson correlation coefficient. [PDF]
Sci RepHuang R +4 more
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Adaptive estimation of Shannon entropy [PDF]
2015 IEEE International Symposium on Information Theory (ISIT), 2015 We consider estimating the Shannon entropy of a discrete distribution $P$ from $n$ i.i.d. samples. Recently, Jiao, Venkat, Han, and Weissman, and Wu and Yang constructed approximation theoretic estimators that achieve the minimax $L_2$ rates in estimating entropy.
Han, Yanjun +2 more
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On the Characterization of Shannon’s Entropy by Shannon’s Inequality [PDF]
Journal of the Australian Mathematical Society, 1973 1. In [2,5,6,7] a.o. several interpretations of the inequalityfor allsuch thatwere given and the following was proved.
Aczél, J., Ostrowski, A. M.
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Shannon Entropy Estimation for Linear Processes [PDF]
Journal of Risk and Financial Management, 2020 In this paper, we estimate the Shannon entropy S(f)=−E[log(f(x))] of a one-sided linear process with probability density function f(x). We employ the integral estimator Sn(f), which utilizes the standard kernel density estimator fn(x) of f(x). We show that Sn(f) converges to S(f) almost surely and in Ł2 under reasonable conditions.
Timothy Fortune, Hailin Sang
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Shannon Entropy Reinterpreted [PDF]
Reports on Mathematical Physics, 2018 In this paper we remark that Shannon entropy can be expressed as a function of the self-information (i.e. the logarithm) and the inverse of the Lambert $W$ function. It means that we consider that Shannon entropy has the trace form: $-k \sum_{i} W^{-1} \circ \mathsf{ln}(p_{i})$.
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