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Estimating the entropy rate.
A k-nearest neighbor approach to estimating the entropy is appropriate in this case and we used the KSG algorithm to do the calculation.
We discuss common randomness requirements, techniques for estimating the entropy of physical sources, investigate specific nonrandom physical properties, estimate the autocorrelation, then mix reduce the data until all common randomness tests pass.
More precisely, starting from a recently introduced measure to determine the topological entropy of non-hierarchical networks, we provide bounds for estimating the entropy of hierarchical graphs.
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In order to compute this from data one is faced with the basic statistical problem of estimating the entropies: selecting the correct bin width in the histograms of the random variables.
However, there are some advantages to estimate the entropy directly.
An analytical procedure is also proposed to estimate the entropy generation and a comparison with the numerical results is accomplished.
The residual sub-band wavelet entropy (RSWE) can be directly used to estimate the entropy of bursts from the sub-bands as proposed by Paul et al.[47].[47]
An entropy-based encoder rate estimation in transform domain is also proposed [17], where the Laplacian correlation model is used to estimate the entropy.
The virtual-bond based low-resolution conformational model (Vfold) allows us to estimate the entropy and the full free energy landscape for RNA tertiary global folds.
We estimated the entropy of the set of pairs obtained on the test corpus after termination of the LT coding algorithm.
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