Implement specialized Hurdle distribution

[ricardoV94]

It indirectly addresses the issue reported in in pymc-devs/nutpie#163

The new objects have a logp that handles the discrete + continuous process correctly, without requiring the arbitrary truncation of the latter at epsilon. This provides a cheaper and more stable logp / logcdf.
For discrete variables we keep using a truncation

Also added special logic to truncate a Hurdle distribution which solves bambinos/bambi#768, this is not the desired behavior, reverted it

CC @zwelitunyiswa

---

📚 Documentation preview 📚: https://pymc--7810.org.readthedocs.build/en/7810/

[ricardoV94]

This was too restrictive, a subclass also inherits the dispatch function, and need not be in the registry explicitly

[codecov]

Codecov Report

❌ Patch coverage is 91.42857% with 6 lines in your changes missing coverage. Please review.
✅ Project coverage is 92.88%. Comparing base (3b62f82) to head (1e38719).
⚠️ Report is 48 commits behind head on main.

Files with missing lines	Patch %	Lines
pymc/distributions/mixture.py	91.17%	6 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #7810   +/-   ##
=======================================
  Coverage   92.88%   92.88%           
=======================================
  Files         107      107           
  Lines       18377    18389   +12     
=======================================
+ Hits        17069    17081   +12     
  Misses       1308     1308           

Files with missing lines	Coverage Δ
pymc/distributions/moments/means.py	100.00% <100.00%> (ø)
pymc/distributions/mixture.py	95.25% <91.17%> (+0.23%)	⬆️

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[zwelitunyiswa]

It indirectly addresses the issue reported in in pymc-devs/nutpie#163

The new objects have a logp that handles the discrete + continuous process correctly, without requiring the arbitrary truncation of the latter at epsilon. This provides a cheaper and more stable logp / logcdf. For discrete variables we keep using a truncation

Also added special logic to truncate a Hurdle distribution which solves bambinos/bambi#768

CC @zwelitunyiswa

📚 Documentation preview 📚: https://pymc--7810.org.readthedocs.build/en/7810/

@ricardoV94 This is amazing. Thank you so much for this!

[zaxtax]

LGTM! We should probably add a test similar to one that motivate this PR in the first class

[ricardoV94]

LGTM! We should probably add a test similar to one that motivate this PR in the first class

We have the pre-existing hurdlesl tests, in a sense this is just a refactor/optimization. Can't think of anything reasonable obvious to test here?

[zaxtax]

LGTM! We should probably add a test similar to one that motivate this PR in the first class

We have the pre-existing hurdlesl tests, in a sense this is just a refactor/optimization. Can't think of anything reasonable obvious to test here?

What caused the error originally reported here? pymc-devs/nutpie#163 Does that have a test already?

[ricardoV94]

LGTM! We should probably add a test similar to one that motivate this PR in the first class

We have the pre-existing hurdlesl tests, in a sense this is just a refactor/optimization. Can't think of anything reasonable obvious to test here?

What caused the error originally reported here? pymc-devs/nutpie#163 Does that have a test already?

That was fixed sometime ago in PyTensor: pymc-devs/pytensor#1137

The performance question when in numba is addressed by pymc-devs/pytensor#1445

Neither is PyMC specific

[zaxtax]

LGTM! We should probably add a test similar to one that motivate this PR in the first class

We have the pre-existing hurdlesl tests, in a sense this is just a refactor/optimization. Can't think of anything reasonable obvious to test here?

What caused the error originally reported here? pymc-devs/nutpie#163 Does that have a test already?

That was fixed sometime ago in PyTensor: pymc-devs/pytensor#1137

The performance question when in numba is addressed by pymc-devs/pytensor#1445

Neither is PyMC specific

Feel free to merge whenever you feel comfortable! I think it's good to go

[lucianopaz]

Looks good to me. I just left two very minor comments

[lucianopaz]

Why not use the logsumexp and have log scale weights here? Is it because the weights are already in the 0-1 range and taking the log won’t help with precision?

[ricardoV94]

We're not computing any log quantities nor starting with any log quantities so I don't think it would help. Also the initial point is not so critical?