Giordon Stark

> There shouldn't be a substantial change apart from minor numerical rounding errors. The factor 2 should only affect the uncertainties and even that only if you didn't compensate by...

> Note: the accuracy of MINUIT is typically way lower than machine precision. Deviations of 1e-6 can be expected, also see https://iminuit.readthedocs.io/en/stable/benchmark.html. Interesting, I didn't see this page before! @matthewfeickert...

I might end up putting this on my github and hosting a JSON file that updates nightly instead... that might be better than putting it in `pyhf`.

> Yeah, I can understand the idea of wanting to have this be something that is outside of the main repo so that maintenance of it isn't tied to the...

Closing this (and deleting branch) in favor of https://github.com/pyhf/public-probability-models

hi, i appreciate the discussion here. we do generally have limited time and I do think this is prioritized enough that we want to make sure it gets into the...

In the current HEAD of `pyhf` (pre-0.7.0), this works correctly ```python import pyhf required_parset = pyhf.modifiers.staterror.required_parset import json ws = pyhf.Workspace(json.load(open('3b_tag21.2.27-1_RW_ExpSyst_79800_multibin_excl_Gtt_2400_5000_800.json'))) pdf = ws.model() print(pdf.config.param_set('staterror_SR1L_Inj_Lmeff_cuts')) def to_poisson(func): def wrapper(*args, **kwargs):...

For now, current solution is to raise a `RuntimeError`.

I think `nan` will happen when your CLs is expected to be super high. But also I wanted to point out that your `min_pyhf.py` script is just repeating functionality that...

How would you suggest making it read-only? The only way I see this being possible is if we hand copies in return e.g. ``` @property def channels(self): return [*self.channels] ```