Not yet. There are a few different ways of specifying the distribution, but we don’t yet have options for doing from the 25th&75th percentiles. It would be nice to do eventually. (Might be very doable to add in a PR, for a fairly motivated person). https://www.squiggle-language.com/docs/Api/Dist#normal
You can type in, normal({p5: 10, p95:30}). It should later be possible to say normal({p25: 10, p75:30}).
Separately; when you say “25, 50, 75 percentiles”; do you mean all at once? This would be an overspecification; you only need two points. Also; would you want this to work for normal/lognormal distributions, or anything else?
I’m thinking, just like how to can infer whether it’s normal or lognormal, we can use one of the bell curve shaped distribution that gives a sort of closest approximation.
More generally, it’d be awesome if there a way to get the max entropy distribution given a bunch of statistics like quantiles or nsamples with min and max.
For
to(a,b), is there a way to specify other confidence intervals?E.g. let’s say I have the 25, 50, 75 percentiles, but not the 5 and 95 percentiles?
Not yet. There are a few different ways of specifying the distribution, but we don’t yet have options for doing from the 25th&75th percentiles. It would be nice to do eventually. (Might be very doable to add in a PR, for a fairly motivated person).
https://www.squiggle-language.com/docs/Api/Dist#normal
You can type in,
normal({p5: 10, p95:30}). It should later be possible to saynormal({p25: 10, p75:30}).Separately; when you say “25, 50, 75 percentiles”; do you mean all at once? This would be an overspecification; you only need two points. Also; would you want this to work for normal/lognormal distributions, or anything else?
25,50,75:
I’m thinking, just like how
tocan infer whether it’s normal or lognormal, we can use one of the bell curve shaped distribution that gives a sort of closest approximation.More generally, it’d be awesome if there a way to get the max entropy distribution given a bunch of statistics like quantiles or nsamples with min and max.