MultiKDE.jl
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pizhn
Multivariate kernel density estimation
Hi, I am trying to apply multithreading approach to your codes It looks like your KDE codes are not compatible with multithreading. For some reasons, it even took more time...
Is there a way to generate samples from KDE approximation of the dataset? It would be especially nice in order to implement "kombine"-style monte carlo samplers. Thank you in advance!...
Better find a way to apply auto-derivative to sanity checks in test/kde.jl
Will be nice if we can choose to pre-initialize the whole KDE like [KernelDensity.jl](https://github.com/JuliaStats/KernelDensity.jl), using some optimized method like FFT or [AverageShiftedHistograms.jl](https://github.com/joshday/AverageShiftedHistograms.jl), which is needed in many situations.
Especially for categorical random variables
Will be good if add more kernels. An ideal way is link It to [Distributions.jl](https://github.com/JuliaStats/Distributions.jl) or [KernelFunctions.jl](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl).
Find a better function signature for get_dict_candidates
Will be good if find a more organized way to construct KDEMulti, especially the UnorderedCategoricalDim part.