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Give UnivariateKDE distribution-like overloads #91

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ChrisRackauckas
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Upstreams SciML/SciMLExpectations.jl#41 . Allows for it to be "distribution-like". Still missing the random sampling.

Upstreams SciML/SciMLExpectations.jl#41 . Allows for it to be "distribution-like". Still missing the random sampling.
Base.eltype(K::UnivariateKDE) = eltype(K.density)
Base.minimum(K::UnivariateKDE) = minimum(K.x)
Base.maximum(K::UnivariateKDE) = maximum(K.x)

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What about Base.extrema?

@agerlach
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agerlach commented May 4, 2021

Any status update on getting this PR merged?

@tpapp
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tpapp commented May 5, 2021

Is it actually guaranteed that the the support is zero outside the extrema of x? I would have to look at the code, but I don't think it holds in general. Eg for a Normal kernel (which is the default), the support is the real line. x are just the points where it is precalculated.

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3 participants