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Consistency 🍏 🍎Expected output across functions could be more similarExpected output across functions could be more similar
Description
Generally speaking there are 3 types of ideas for obtaining credible intervals:
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Highest Density Interval(s) / Region(s) (HDI/HDR) : Interval or intervals that contain the values that have the
$(1-\alpha)$ % most probable values by their density. -
Quantile / Equal-Tailed Intervals (QI/ETI): Interval where each bound is found seperately: the lower/upper bound is that value that has
$\alpha/2$ % below/above it. -
Shortest Probability / Highest Density Continuous Interval (SPI/HDCI): The interval with the minimum width that contains
$(1-\alpha)$ % posterior mass.- When the posterior density is uni-modal, SPI/HDCI is equal to the HDI/HDR.
- When the posterior is symmetric, SPI/HDCI is equal to the QI/ETI.
We currently have 3 function for computing credible intervals1:
eti()- returns the QI/ETI based on the MCMC ecdf.hdi()- returns the SPI/HDCI based on the MCMC ecdf.spi()- returns the SPI/HDCI based on the density estimation from the MCMC samples.
This seems inconsistent and confusing. Personally I'm not a fan of the HDI/HDR that can return multiple intervals, nor of the density based SPI/HDCI (why add a step?), but something should probably be done regarding the naming of these functions? (scroll up and down here >>)
- Maybe merge
hdi()/spi()into one function? - Maybe also add one and two dimensional HDI/HDR?
Footnotes
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We also have
bci(), but if I'm not mistaken that is more appropriate for bootstrap based confidence intervals? ↩
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Consistency 🍏 🍎Expected output across functions could be more similarExpected output across functions could be more similar