Test SVI for a model where we have NUTS results
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- Dominant language
- Python
- Stars
- 13
- Forks
- 3
- Avg merge
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Description
From f2f conversation there was some interest in exploring variational inference; mainly to resolve long compute times.
My understanding is that the infrastructure exists in the code to swap samplers and a variational inference sampler exists for numpyro https://num.pyro.ai/en/stable/svi.html .
So in principle, for situations where we already have NUTS generated posterior samples, it should be quite easy to compare the results.
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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 by locating the existing sampler-switching infrastructure and the workflow that produces NUTS posterior samples; no specific files or tests are named in the issue. Review NumPyro's SVI documentation, then define a model where NUTS results already exist and compare its SVI output with those results. Done means the comparison is implemented and its expected outcome is recorded.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning, testing-qa
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100