add block gibbs sampling for HMM learning
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help wanted
- Dominant language
- Python
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Description
We currently support block gibbs sampling for LGSSM (see here).
We should do the same for HMMs, assuming conjugate priors.
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 by reading the block Gibbs sampling implementation for LGSSM in dynamax/linear_gaussian_ssm/models.py around the linked location. Trace the HMM learning and conjugate-prior code, then determine the equivalent HMM entry point and tests; done means HMMs support block Gibbs sampling under conjugate priors.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100