pymc-devs / pymc-devs/pymc-examples
Combine Marginalized and Latent Gaussian Mixture Notebooks?
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- Dominant language
- Python
- Stars
- 398
- Forks
- 325
- Avg merge
- 9d 15m
- Merged PRs (30d)
- 1
Description
The two notebooks are covering exactly the same issue.
They seem short enough that we could use the same dataset and show one after the other. This way we also get a chance to nudge users to try the marginalized mixture, which usually works better.
https://docs.pymc.io/notebooks/gaussian_mixture_model.html
https://docs.pymc.io/notebooks/marginalized_gaussian_mixture_model.html
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
Compare the Gaussian mixture model and marginalized Gaussian mixture model notebooks at the two linked documentation URLs. Start by reviewing their datasets, narrative flow, and model demonstrations; done means the overlapping material is combined into one coherent notebook while still showing the marginalized mixture and its benefits.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- documentation, machine-learning
- Issue type
- Refactor
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100