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Thanks for sharing the good work. Is there any implementation for [kernel density estimation](https://en.wikipedia.org/wiki/Multivariate_kernel_density_estimation) available ([univariate](https://book…
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A Kernel density estimation , is a non-parametric method for estimating the **probability density function - PDF** of a Random Variable. Also as a generic EDA approach - the , kernel density plots ar…
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I think it would be nice to include this as a complement to the existing histogram out of the box, as a KDE sidesteps the issues with selecting a correct bucket size for the histogram. I'm not really …
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Hi Jonathan:
When I use the distribution generated by kernel density estimation for sampling, it takes a lot of time. And I use the distribution generated by sklearn's KDE for sampling, which is ve…
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Once we have a density estimate, identifying outliers can be done by picking points that are in regions with low density.
http://www.jmlr.org/papers/volume13/kim12b/kim12b.pdf
http://web.eecs.umich.ed…
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Hello,
I am trying to use Chaospy to perform advanced sampling of a multivariate KDE generated via sm.nonparametric.KDEMultivariate. Unfortunately, I am not able to defined the KDE as a custom dis…
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See https://hackage.haskell.org/package/statistics-0.16.0.1/docs/Statistics-Sample-KernelDensity.html .
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One thing that I think could potentially really clarify the the PCoA plots is to have some kernel density estimators. This could not only allow the user to visualize where the most points are cluster…
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Hello,
I was searching for a Kernel Density estimation in 3D. I think it could be nice to implement the following method from Zdravko Botev et al. in Julia.
Botev, Z. I.; Grotowski, J. F.; Kroes…
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### Describe the workflow you want to enable
Kernel density estimates for bounded data are biased near the boundary because probability mass "spills out of the domain". It would be great to add a bou…