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Sequential Empirical Coordination Under an Output Entropy Constraint [PDF]
IEEE Transactions on Information Theory, 2016 This paper considers the problem of sequential empirical coordination, where the objective is to achieve a given value of the expected uniform deviation between state-action empirical averages and statistical expectations under a given strategic probability measure, with respect to a given universal Glivenko-Cantelli class of test functions.
Ehsan Shafieepoorfard, Maxim Raginsky
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A High-Throughput Hardware Accelerator for Network Entropy Estimation Using Sketches
IEEE Access, 2021 Network traffic monitoring uses empirical entropy to detect anomalous events such as various types of attacks. However, the exact computation of the entropy in high-speed networks is a difficult process due to the limited memory resources available in ...
Javier E. Soto +4 more
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Entropy, 2014 A computational procedure is developed to determine initial instabilities within a three-dimensional laminar boundary layer and to follow these instabilities in the streamwise direction through to the resulting intermittency exponents within a fully ...
LaVar King Isaacson
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Thermodynamics and time-average [PDF]
, 2004 For a dynamical system far from equilibrium, one has to deal with empirical probabilities defined through time-averages, and the main problem is then how to formulate an appropriate statistical thermodynamics.
A. Carati +7 more
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Distinguishing between Clausius, Boltzmann and Pauling Entropies of Frozen Non-Equilibrium States
Entropy, 2019 In conventional textbook thermodynamics, entropy is a quantity that may be calculated by different methods, for example experimentally from heat capacities (following Clausius) or statistically from numbers of microscopic quantum states (following ...
Rainer Feistel
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Large deviations for empirical entropies ofg-measures [PDF]
Nonlinearity, 2005 Summary: The entropy of an ergodic finite-alphabet process can be computed from a single typical sample path \(x^n_1\) using the entropy of the \(k\)-block empirical probability and letting k grow with \(n\) roughly like \(\log n\). We further assume that the distribution of the process is a g-measure.
J. R. CHAZOTTES, GABRIELLI, DAVIDE
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An empirical exploration of entropy balancing in estimating treatment effects: Insights from simulation and two applied biomedical studies [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2021 We present entropy balancing – a relatively new technique for estimating treatment effects, which has been under-utilised in the applied biomedical literature.
Lateef Amusa +2 more
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Empirical Maximum Entropy Methods [PDF]
AIP Conference Proceedings, 2006 A method, which we suggest to call the Empirical Maximum Entropy method, is implicitly present at Maximum Entropy Empirical Likelihood method, as its special, non‐parametric case. From this vantage point the entropy‐based empirical approach to estimation is surveyed.
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Entropy conditions for L r -convergence of empirical processes [PDF]
Advances in Computational Mathematics, 2008 Let \(P_n\) be the empirical measure for a sample of \(n\) independent random elements with joint distribution \(P\). Furthermore, let \({\mathcal F}\) be a class of real-valued (bounded) functions defined on the sample space. Then it is known that \(\sup_{f\in {\mathcal F}}|P_nf - Pf| \to 0\) almost surely provided that \({\mathcal F}\) admits a ...
A. CAPONNETTO +2 more
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Entropy, 2020 The categorization of sleep stages helps to diagnose different sleep-related ailments. In this paper, an entropy-based information–theoretic approach is introduced for the automated categorization of sleep stages using multi-channel electroencephalogram (
R. K. Tripathy +3 more
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