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Superimposed are density plots for the normal distribution and using a kernel density estimate.
Densities are estimated using a kernel density estimator and Silverman's rule of thumb bandwidth.
We estimate the environment pdf from all identified clusters using a kernel density estimator (KDE) [34].
This scenario is demonstrated in the "Results" section, using a kernel density estimation (KDE) AD method.
First, crash clusters were investigated using a kernel density estimation (KDE) approach.
(1) We estimate the environment pdf from all identified clusters using a kernel density estimator (KDE) [34]. .
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PARalyzer [ 89] uses the number of T → C conversions that are diagnostic of PAR-CLIP binding sites to define the limits of the binding site using a kernel density-based classifier, but does not use transcript abundance data and so generates a set of RBP-bound sites rather than RBP target preferences.
We used a kernel density with 3 standard deviation to smooth the prediction.
We used a kernel density estimate (KDE) (using a Gaussian kernel with σ=5°) to approximate the geographic distribution of faces assigned to a particular cluster.
We used a kernel density estimator (Silverman 1986) with smoothing parameters estimated using likelihood based cross-validation to create a utilization distribution ("UD", Appendix A2).
Note that because we use a kernel density estimation, P values much smaller than the inverse of number of elements in the null distribution can be obtained.
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using a kernel method
using a force density
using a kernel regressor
using a kernel similarity
using a kernel image
using a charge density
using a kernel security
using a novel density
using a kernel smoothing
using a kernel kernel
using a probability density
using a kernel smoother
using a number density
using a kernel estimation
using a kernel regression
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