JuliaGaussianProcesses / JuliaGaussianProcesses/AbstractGPs.jl
Celerite methods?
Nobody has claimed this yet.
- Dominant language
- Julia
- Stars
- 287
- Forks
- 27
- PR merge metrics
- No merged PRs in 30d
Description
Anyone here familiar with the methods used here? It gives a linear-time exact solution for special case of one-dimensional data with covariance given by mixtures of exponentials.
The main implementation is celerite. There a full-Julia version by @ericagol here, and an alternate Julia implementation @dfm wrote to walk me through the basic ideas here.
Just thought I'd mention this, since it seems relevant if AbstractGPs is intended as a best-of-everything GP package (which... is that the case?)
Contributor guide
No contributing guide indexed for this repository
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 the linked paper and comparing the Julia implementations in celerite.jl and Celerite2.jl. Then review AbstractGPs.jl's stated scope and existing Gaussian-process methods to determine whether this belongs in the package. Done means an agreed scope and implementation plan, not merely adding a method from the discussion.
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
- 20/100