JuliaGaussianProcesses / JuliaGaussianProcesses/AbstractGPs.jl
Hilbert space GPs?
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 287
- Forks
- 27
- PR merge metrics
- No merged PRs in 30d
Description
Hi,
I have recently had to work with data too large for standard GPs and therefore pointed Claude at the PyMC implementation of Hilbert space GPs.
The HSGP worked very well for my use case and I think it might be useful for others as well. I wonder if adding HSGPs is of interest to AbstractGPs. If so, I'm happy to open a PR with my code as a starting point.
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 by reviewing the linked PyMC Hilbert space GP example and the proposed implementation mentioned in the issue. Compare its scope with AbstractGPs.jl's existing Gaussian-process abstractions, then clarify the desired API and implementation boundaries with maintainers. Done means the feature scope is agreed and a PR can demonstrate working HSGPs for the intended large-data use case.
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
- Active
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100