Examples of 'kernel density' in a sentence
Meaning of "kernel density"
kernel density - In statistics, kernel density refers to a non-parametric way to estimate the probability density function of a random variable. It is commonly used in data analysis and visualization to smooth out data points and create a continuous representation of the data distribution
How to use "kernel density" in a sentence
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kernel density
Classification and kernel density estimation.
Kernel density estimates are easily understood by reference to histograms.
Overlaid on this box plot is a kernel density estimation.
Kernel density estimation.
Wealso study standard kernel density estimates in both frames.
The distributions were smoothed using gaussian kernel density estimates.
Kernel density estimation provides better estimates of the density than histograms.
The kernels are summed to make the kernel density estimate solid blue curve.
Thus the kernel density estimator coincides with the characteristic function density estimator.
Using these data, we derived the density of study sites with a kernel density map.
The construction of a kernel density estimate finds interpretations in fields outside of density estimation.
In C++, libagf is a library for variable kernel density estimation.
The kernel density estimator was used to check hotspots and so set most affected areas.
The solid lines depict Gaussian kernel density estimates ( right axis ) of each distribution.
The Kernel Density calculates the density of point features around each output raster cell.
See also
One of the most widely used is the Kernel Density Estimation ( KDE ).
Kernel Density Estimation method has been chosen for executing the task.
This approach is known as kernel density estimation or the Parzen window technique.
In Julia, kernel density estimation is implemented in the KernelDensity . ji package.
This definition relies on kernel density estimates and is therefore non-parametric.
In Octave, kernel density estimation is implemented by the kernel density option econometrics package.
Figure 3 shows the Kernel density estimate of elderly deaths.
In MATLAB, kernel density estimation is implemented through the ksdensity function Statistics Toolbox.
For numerical variables, kernel density estimation was performed with a Gaussian kernel and variable bandwidth.
The kernel density 50 % identified two main areas of concentration of nests, ac i and ac ii.
For example, a kernel density estimation, or convolution is used for the smoothing.
In Haskell, kernel density is implemented in the statistics package.
This is a kernel density estimate ( KDE ) visualization of the interactive-user build time.
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Examples of using Kernel
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Booting the kernel on the target platform
This came from the system kernel
Nut in which the kernel has aborted
