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Implement multimodal HDI #7

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sethaxen opened this issue Aug 7, 2023 · 1 comment
Open

Implement multimodal HDI #7

sethaxen opened this issue Aug 7, 2023 · 1 comment
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enhancement New feature or request

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sethaxen commented Aug 7, 2023

Our current HDI estimator hdi assumes the distribution is unimodal. To support multimodal distributions, we would first fit a KDE (or histogram for discrete points) to the draws and then find the HDI from that. This is mostly useful for plotting.

Once #6 is complete, this could probably be easily done with https://github.com/tpapp/HighestDensityRegions.jl. See also Python ArviZ's implementation.
@sethaxen sethaxen added the enhancement New feature or request label Aug 7, 2023
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sethaxen commented Aug 8, 2023

We should also look into estimating the HDI from average shifted histograms (see e.g. https://github.com/joshday/AverageShiftedHistograms.jl), as these are much faster to compute than the KDE. Probably a simulation study is necessary to compare both performance and accuracy of estimating known HDIs from KDE vs ASH.

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