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Now we have a forward model $f(\vec{\theta})$ (an NN emulator) which could return the SED of given physical parameters. Then we need to build a neural density estimator to describe the $P(\vec{\theta}…
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MLPack for R is now on CRAN (https://cran.r-project.org/web/packages/mlpack/index.html) and contains an implementation of "density estimation trees" (paper: https://dl.acm.org/doi/abs/10.1145/2020408.…
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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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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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Started in 91eeaf096da884c3167416dea56ea1610f958a65, but needs some testing because I'm sure I've broken something unexpected.
## No `x`
```
ggplot(faithful, aes(y = waiting)) +
geom_hdr_boxp…
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Either by Monte Carlo simulations or by analyzing video data
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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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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 .