Use gaussian likelihood
Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 25/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- jupyter-notebook
- Domain
- data
Research direction
The issue names no files, tests, or entry points. Locate the number-count likelihood, then determine how a covariance matrix can be estimated from the data; the work is done when the likelihood uses that Gaussian covariance consistently.
Written by the indexing model from the issue text.
Description
Use gaussian likelihood for number counts, it will require a covariance matrix.
In a first approach, it can just be estimated from the data.
- Dominant language
- Jupyter Notebook
- Stars
- 9
- Forks
- 1
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
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.
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