JuliaGaussianProcesses / JuliaGaussianProcesses/AbstractGPs.jl
What is `noise_var` in documentation?
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Description
https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev/examples/0-intro-1d/
mentions:
We create a finite dimensional projection at the inputs of the training dataset observed under Gaussian noise with variance noise_var=0.1
Is it the same thing as alpha in https://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.GaussianProcessRegressor.html#sklearn.gaussian_process.GaussianProcessRegressor
which is the σ_n^2 in literature? https://gaussianprocess.org/gpml/chapters/RW.pdf#page=37
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- Read the whole issue, then the project's contributing guide.
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Research direction
Start with the linked AbstractGPs.jl introductory example and compare its noise_var wording with scikit-learn's GaussianProcessRegressor documentation and the cited GPML passage. Done means the documentation clearly explains whether noise_var, alpha, and σ_n² refer to the same quantity.
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Assessment
- Tech stack
- julia, scikit-learn
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100