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Alternative normalization for Normally distributed parameters? #250

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merged 3 commits into from Apr 28, 2022

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glwagner
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@glwagner glwagner commented Apr 24, 2022

@adelinehillier, should we subtract the mean and divide by variance? We're doing the converse now (subtracting variance and dividing by mean). It seems dividing by the mean has undesirable effects, like the variance in unconstrained space diverging as the constrained mean approaches zero. For example, we can't have unconstrained parameters with unit normal distributions using this method. But subtracting mean and dividing by variance is always valid, and implies that the unconstrained prior is unit Normal(0, 1).

@adelinehillier, should we subtract the mean and divide by variance? We're doing the converse now (subtracting variance and dividing by mean).
@glwagner glwagner changed the title Alternative normalization for Normally distributed parameters? Alternative normalization for Normally distributed parameters? Apr 24, 2022
@adelinehillier
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This is definitely the way it should be!

@glwagner
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Nice! I bumped the version, then I'll merge.

@glwagner glwagner merged commit 83d91aa into main Apr 28, 2022
@glwagner glwagner deleted the glw/converse branch April 28, 2022 01:05
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