implement rao-blackwellised particle filtering using dynamax and blackjax
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- Dominant language
- Python
- Stars
- 1k
- Forks
- 114
- Avg merge
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- Merged PRs (30d)
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Description
combine Kalman Filter from dynamax and sequential monte carlo from Blackjax to implement RBPF for switching linear Gaussian SSM. For details, see sec 13.4.1 of https://probml.github.io/pml-book/book2.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
Start by reading section 13.4.1 of the referenced ProbML book, then inspect Dynamax's Kalman Filter APIs and Blackjax's sequential Monte Carlo APIs. The work is done when these components implement Rao-Blackwellised particle filtering for a switching linear Gaussian state-space model, with behavior covered by appropriate project tests.
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
- 25/100