[ENH] Bayesian Linear Regression using Normal Conjugate Prior#500
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January 3, 2025 23:34
fkiraly
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Jan 5, 2025
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Excellent work! My comments are mainly on documentation, PR structure, nothing content-wise.
- the notebooks should be reconciled with PR #480, otherwise I see some inconsistencies that could cause merge headaches. I would suggest to split off the notebooks in a separate PR, with dependency graph either 500, 480 -> new PR, or 500 -> 480 -> new PR (left side of arrow gets merged earlier).
- in the docstring, I think it is essential to say what exactly the model assumptions are, a little bit of math would be great there, and how these map onto python parameters.
- the changes in #480 are inconsistent with the directory structure here - I would recommend to make all submodues of
bayesianprivate, as long as we are not certain about the exact end state desired.
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Hi @fkiraly, Thanks for the review! I've made the following updates:
Let me know if this works! |
fkiraly
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Jan 9, 2025
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Hi @fkiraly, I've modified the API name in |
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What does this implement/fix? Explain your changes.
Implementing a new Bayesian linear regression estimator module using normal conjugate prior
with known noise variance.
Does your contribution introduce a new dependency? If yes, which one?
No
What should a reviewer concentrate their feedback on?
Correctness of implementation
Did you add any tests for the change?
No
Any other comments?
No
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docs/source/api_reference/taskname.rst, follow the pattern.Examplessection.(https://www.sktime.net/en/latest/developer_guide/dependencies.html#adding-a-soft-dependency).