implement unscented particle filter using dynamax and blackjax
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help wanted
- Dominant language
- Python
- Stars
- 1k
- Forks
- 114
- Avg merge
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- Merged PRs (30d)
- 1
Description
Use EKF and/or UKF from dynamax as proposal distributions for sequential monte carlo from Blackjax to implement approximate inference in nonlinear Gaussian SSMsM. For details, see sec 13.3.2 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 13.3.2 of the referenced Probabilistic Machine Learning book, then review the existing EKF and UKF implementations in Dynamax and BlackJAX's sequential Monte Carlo API. Done means implementing approximate inference for nonlinear Gaussian state-space models using Dynamax EKF and/or UKF proposal distributions with BlackJAX.
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
- 30/100