Open FriesischScott opened 2 weeks ago
Yes, sounds good. We could alternatively think about decomposing the joints into a copula + marginals if we know it has a closed form (e.g. Normal).
For sliced normals, we might be able to use transport maps from @jgrashorn
For sliced normals, we might be able to use transport maps from @jgrashorn
I'll believe that when I see it :D
Yes, sounds good. We could alternatively think about decomposing the joints into a copula + marginals if we know it has a closed form (e.g. Normal).
For the MvNormal
that should work. Not sure how well it generalizes for other mulivariate distributions.
For sliced normals, we might be able to use transport maps from @jgrashorn
I'll believe that when I see it :D
It should be possible, it's a polynomial mapping from SNS to some other distribution of the same dimensions. I think you fit them by minimising some divergence (KL-divergence?). So if we can compute the KL divergence between the sliced normal and some other distribution, should be possible.
But I think you can fit the TM directly to a dataset, so it might be better to just do that. And it can also be the case that the sliced normal is more flexible than the TMs.
We need a general interface for various joint distributions. I think a parameterized struct could work well here. In this we can wrap for example
SklarDist
from Copulas.jl,MvNormal
fromDistributions.jl
and Sliced Normals/Exponentials.This would allow us to define mappings for dispatch for distinct types.