pymc-devs / pymc-devs/pymc-examples
Blackbox likelihood
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 398
- Forks
- 325
- Avg merge
- 9d 15m
- Merged PRs (30d)
- 1
Description
Required changes
The notebook needs to be modified to use ArviZ+InferenceData at the very least, it's hard to know what extra work will be needed as it has not been executed for a while, cython usage is tricky to get right. Note that it uses DensityDist is strange ways, using dicts as observed that contain freeRVs and will need to use density_dist_obs=False as idata_kwargs (ref)
Changes to discuss
I don't think this notebook can be converted to v4 unless it undergoes a significant rewrite. Not sure if we should try to get it working with a custom distribution or with pm.Potential for v4.
⚠️ It requires cython to run.
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 with examples/case_studies/blackbox_external_likelihood.ipynb and run the notebook with Cython available to identify current failures. Read the ArviZ and InferenceData conversion guidance, then determine whether the notebook should use a custom distribution or pm.Potential for version 4. Done means the chosen approach runs successfully and uses ArviZ with InferenceData.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100