JuliaGaussianProcesses / JuliaGaussianProcesses/LinearMixingModels.jl
Handle missing data (semi-heterotopic data)
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 5
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
How to handle missing data is covered in the paper introducing these models, and implemented in OILMMs.jl.
Similarily, it should be implemented here to handle the semi-heterotopic data case.
Contributor guide
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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 with the linked paper describing missing-data handling and compare it with OILMMs.jl's src/missing_data.jl implementation. Then inspect this repository's existing model and data-handling entry points to determine where semi-heterotopic support belongs. Done means the described missing-data behavior is implemented for this case and verified with appropriate tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- julia
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100