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The probability density function (PDF) is estimated from measured data by using kernel density estimators [21].

The likelihood pdf is assumed unknown and is estimated by using kernel density estimator at the receiver.

In the procedure for validation, three statistical methods are used to compare measured and simulated noises: 1) The probability density function (PDF) is estimated from measured data by using kernel density estimators [21].

We calculated mutual information of interactions by using kernel density estimator (KDE) and discarded weak connections using proper thresholds.

The information theoretic approach is based on estimation of mutual information of interactions by using kernel density estimators.

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Figure 3a shows estimated posterior densities for the mass parameters, (obtained from the samples generated by our Gibbs sampler using kernel density estimation).

We have developed PeaKDEck, a peak calling program that distinguishes signal from noise by randomly sampling read densities and using kernel density estimation to generate a dataset-specific probability distribution of random background signal.

The densities obtained by means of the bootstrap will be depicted using kernel density estimators with bandwidth parameters of 0.2 to 0.5.

Using kernel density estimate (KDE) (Approximation of distribution.

Next, we determined the density of cells using Kernel density estimation.

The distributions were obtained using kernel density estimates [ 53] implemented in R [ 54].

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