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| if lower is not None and mu is not None and sigma is not None: | ||
| a = (lower - mu) / sigma | ||
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| if upper is not None and mu is not None and sigma is not None: |
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@ev-br sorry to bother you. any thoughts on how to deal with missing parameters when fitting? this method seems almost useless if you need 3 parameters just to fix one.
In other words, scipy.stats.truncnorm.fit(fa=<guess>) will work, but paramnormal.truncated_normal.fit(lower=<guess>) won't since you also need mu and sigma to estimate a.
On the otherhand, if you're fitting truncnorm distributions and you have a guess for a, does it follow that you implicitly have a guess for sigma and mu?
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Geez, that's confusing! A quick thought, likely rubbish --- how about say, fixing upper and lower from the extent of the data and then calling truncnorm.fit_loc_scale to estimate mu and sigma?
| References | ||
| ---------- | ||
| http://scipy.github.io/devdocs/generated/scipy.stats.truncnorm | ||
| https://en.wikipedia.org/wiki/Rice_distribution |
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Good catch and I didn't know about fit_loc_scale! Will check it out.
On Fri, Jul 29, 2016 at 5:03 AM Evgeni Burovski notifications@github.com
wrote:
In paramnormal/dist.py
#36 (comment):
- and
- .. math::
b = \frac{x_{\mathrm{upper bound}} - \mu}{\sigma}
- Since
aandbare directly linked to the location and scale- of the distribution as well as the lower and upper limits,
- respectively, it's difficult to use the
fitmethod of this- distirbution without either knowing a lot about it
a priorior- assuming just as much.
- References
Rice?
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3 participants
closes #35