Add DiscreteWeibull moment

[zoj613]

Adds the median of the discrete weibull distribution as mentioned in #5078 (comment)

Closes #5078

[zoj613]

I think it is worth noting that according to the parametrization used for the distribution, the q parameter must be in the interval (0, 1), and so it makes sense to check this when instantiating the DisreteWeibull class. currently, this parameter validation is not done at all.

cc @ricardoV94

[codecov]

Codecov Report

Merging #5407 (ac99306) into main (71b18e6) will increase coverage by 0.02%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main    #5407      +/-   ##
==========================================
+ Coverage   80.45%   80.48%   +0.02%     
==========================================
  Files          82       82              
  Lines       14151    14157       +6     
==========================================
+ Hits        11385    11394       +9     
+ Misses       2766     2763       -3     

Impacted Files	Coverage Δ
pymc/distributions/discrete.py	98.81% <100.00%> (+0.01%)	⬆️
pymc/__init__.py	100.00% <0.00%> (ø)
pymc/parallel_sampling.py	87.70% <0.00%> (+0.99%)	⬆️

[ricardoV94]

I think it is worth noting that according to the parametrization used for the distribution, the q parameter must be in the interval (0, 1), and so it makes sense to check this when instantiating the DisreteWeibull class. currently, this parameter validation is not done at all.

cc @ricardoV94

@zoj613 We only to those type of checks when drawing random numbers or evaluating the logp / logcdf, as the values may be symbolic when the distribution is initialized. The moments should not have to worry about invalid parameters

pymc/pymc/distributions/discrete.py

Line 520 in ac99306

return check_parameters(res, 0 < q, q < 1, 0 < beta, msg="0 < q < 1, beta > 0")

[ricardoV94]

Failing test seems unrelated: #5409

[ricardoV94]

Thanks @zoj613 this was the last one!