SciML / SciML/DiffEqNoiseProcess.jl
BrownianInterval type
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- Dominant language
- Julia
- Stars
- 62
- Forks
- 32
- Avg merge
- 8h 38m
- Merged PRs (30d)
- 16
Description
https://openreview.net/pdf?id=padYzanQNbg this paper is something we might want to add @frankschae .
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 the linked paper to understand the proposed BrownianInterval type, then inspect how this repository represents existing noise processes. Determine the API and behavior needed for the type, and confirm that the implementation is integrated consistently with the library's stochastic-process use cases.
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