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currently only kalman filters are implemented for the imm in filterpy lib (at least I think so)
- [x] check this!
- [x] implement pf in imm (as we and D Weidemann did in ASIM 2023)
- [ ] add KDE to…
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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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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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### 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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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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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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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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Taking the code sample below, the maximum sample is always below 5.0.
```
use kernel_density_estimation::prelude::*;
fn main() {
let observations: Vec = vec![4.99,5.0,5.01];
…