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### Describe the workflow you want to enable
Currently `KernelDensity.sample` only works for the `gaussian` and `tophat` kernels, but I'd like to add support for the `linear` and `exponential` kernel…
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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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Hi,
Is there a specific reason for why it is not implemented yet?
I tried to embedded it with a custom converter but it requires opset 20.
Thanks you for this precious package btw!
```
skl2on…
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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…
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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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Hi!
I've spent the morning porting and shaping the good ol' ESRI's **(weighted)Kernel Density** functionality into TURFjs.
I have made a new branch, but no PR because I'm not able to properly ru…
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Team,
I am working on a tool that requires as it's input an electron density on a grid. This was easy to achieve for the neutral and ionic states for given elements by using the numint.eval_rho fu…
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Currently the density, cdf, quantiles, etc. produced from a `dist_sample()` are not consistent with each other. That is ok as a design choice, and results in better estimates of each, even if they are…
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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…