Skewed Student-T distribution

[fonnesbeck]

Description

Added skewed Student T distribution (Jones and Faddy implementation).

Checklist

• [x] Checked that the pre-commit linting/style checks pass
• [x] Included tests that prove the fix is effective or that the new feature works
• [x] Added necessary documentation (docstrings and/or example notebooks)
• [ ] If you are a pro: each commit corresponds to a relevant logical change

Type of change

• [x] New feature / enhancement
• [ ] Bug fix
• [ ] Documentation
• [ ] Maintenance
• [ ] Other (please specify):

---

📚 Documentation preview 📚: https://pymc--7252.org.readthedocs.build/en/7252/

[ricardoV94]

Add in pymc-experimental first?

[jessegrabowski]

If something like this goes in experimental, there should be a clear roadmap to go from there back to the main repo. This is a straight-forward distribution that (I guess?) can't be easily implemented as a generative graph using only existing distributions via CustomDist. If it's in pmx, what's the criteria to move it over? It's not something like statespace or marginalmodel that proposes a huge new functionality, or something like r2d2m2 that implements a distribution that doesn't fit neatly into a typical pymc model as we present them in books/tutorials.

[ricardoV94]

I don't think this one has a straightforward generative graph

[ricardoV94]

I assumed something "according to x and y" was a bit experimental, hence my suggestion

[fonnesbeck]

The "Following Jones and Faddy 2003" was to distinguish it from the range of available skewed Student T implementations (there are 3 or 4). I chose this one because it is available in SciPy and relatively straightforward to implement.

I think it will be a useful distribution because it allows one to specify a likelihood that is both skewed and overdispersed and converges to normal when a and b are both large and equal.

[fonnesbeck]

Jax failure does not seem to be related to this PR

[ricardoV94]

Jax failure does not seem to be related to this PR

It's not. Has been failing since the last scipy

[ricardoV94]

But this one is: https://github.com/pymc-devs/pymc/actions/runs/8682226852/job/23806440256?pr=7252#step:7:616

You should try running those logp/logcdf/icdf with n_samples=-1 or whatever it is, to test all combinations locally instead of only a random subset

[ricardoV94]

You're missing the tests for the RV itself. That's also where we cover alternative parametrizations. Something like these

https://github.com/pymc-devs/pymc/blob/34c2d31484143b7b5c6130927ed4efde93a27a0f/tests/distributions/test_continuous.py#L2179-L2200

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 92.35%. Comparing base (60a6314) to head (bcb1b5d). Report is 4 commits behind head on main.

Additional details and impacted files

[](https://app.codecov.io/gh/pymc-devs/pymc/pull/7252?src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pymc-devs) ```diff @@ Coverage Diff @@ ## main #7252 +/- ## ========================================== + Coverage 91.67% 92.35% +0.68% ========================================== Files 102 102 Lines 17017 17069 +52 ========================================== + Hits 15600 15764 +164 + Misses 1417 1305 -112 ``` | [Files](https://app.codecov.io/gh/pymc-devs/pymc/pull/7252?dropdown=coverage&src=pr&el=tree&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pymc-devs) | Coverage Δ | | |---|---|---| | [pymc/distributions/\_\_init\_\_.py](https://app.codecov.io/gh/pymc-devs/pymc/pull/7252?src=pr&el=tree&filepath=pymc%2Fdistributions%2F__init__.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pymc-devs#diff-cHltYy9kaXN0cmlidXRpb25zL19faW5pdF9fLnB5) | `100.00% <ø> (ø)` | | | [pymc/distributions/continuous.py](https://app.codecov.io/gh/pymc-devs/pymc/pull/7252?src=pr&el=tree&filepath=pymc%2Fdistributions%2Fcontinuous.py&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pymc-devs#diff-cHltYy9kaXN0cmlidXRpb25zL2NvbnRpbnVvdXMucHk=) | `97.93% <100.00%> (+0.09%)` | :arrow_up: | ... and [4 files with indirect coverage changes](https://app.codecov.io/gh/pymc-devs/pymc/pull/7252/indirect-changes?src=pr&el=tree-more&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=pymc-devs)

[michaelosthege]

Looks like test_skewstudentt_logp is failing on main, or is at least flaky?

[ricardoV94]

These tests run a random subset of 100 combinations. We should set n_samples=-1 locally and see if every combination passes (both float64 and float32), I guess it does not.

CC @fonnesbeck

[michaelosthege]

Running float64 this gives:

E           AssertionError: 
E           Arrays are not almost equal to 6 decimals
E           {'a': array(0.01), 'b': array(100.), 'mu': array(-2.1), 'sigma': array(0.01), 'value': array(-0.01)}
E           x and y -inf location mismatch:
E            x: array(-751.339449)
E            y: array(-inf)

[ricardoV94]

Which underflows? PyMC or Scipy?

[michaelosthege]

Which underflows? PyMC or Scipy?

SciPy produces the -inf.

What's the fix? I have it open and can push a branch real quick (if it's simple)

[ricardoV94]

Which underflows? PyMC or Scipy?

SciPy produces the -inf.

What's the fix? I have it open and can push a branch real quick (if it's simple)

Try slightly less extreme parameter domains