Support symbolic eta parameter in LKJCholeskyCov.logp
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Description
Description
In pymc/distributions/multivariate.py, _LKJCholeksyCovRV_logp currently enforces eta to be a scalar constant via get_underlying_scalar_constant_value, raising a NotImplementedError if a symbolic random variable (e.g., pm.HalfNormal) or tensor is provided.
However, LKJCorr.logp already supports dynamic/symbolic eta using _lkj_normalizing_constant, and LKJCholeskyCov contains a TODO to lift this requirement.
Lifting this scalar restriction on eta (while keeping matrix dimension n constant) brings LKJCholeskyCov to parity with LKJCorr and allows hierarchical priors on eta.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start in pymc/distributions/multivariate.py with _LKJCholeksyCovRV_logp, then compare LKJCorr.logp and _lkj_normalizing_constant to understand the existing symbolic eta path. Keep the matrix dimension n constant; done means symbolic tensor and random-variable eta values are accepted without the current NotImplementedError and behave consistently with LKJCorr.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 70/100