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Call the scipy fit function using the loc and scale as starting guesses
After the fit is complete, get the values for a and b
The issue is that step 3 also returns new loc and scale parameters. The ones we input are just starting guesses. When we use the same loc and scale as step 1, they are out-of-sync with the a and b parameters.
Expected behavior
Stop using the initial guesses for loc and scale. Update them when setting a and b.
Problem Description
In the beta univariate fit function, we perform the following steps:
loc
andscale
parametersfit
function using theloc
andscale
as starting guessesa
andb
The issue is that step 3 also returns new
loc
andscale
parameters. The ones we input are just starting guesses. When we use the sameloc
andscale
as step 1, they are out-of-sync with thea
andb
parameters.Expected behavior
Stop using the initial guesses for
loc
andscale
. Update them when settinga
andb
.i.e. change line 30
Additional context
We verified this change by comparing our fit distribution to scipy. Scipy's fit is better because it's actually updating the
loc
andscale
parameters.Sometimes it's off by a little bit --
Sometimes, by a lot --
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