[ENH] Add BaseBayesianRegressor class for Bayesian model interface#802
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arnavk23 wants to merge 3 commits intosktime:mainfrom
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[ENH] Add BaseBayesianRegressor class for Bayesian model interface#802arnavk23 wants to merge 3 commits intosktime:mainfrom
BaseBayesianRegressor class for Bayesian model interface#802arnavk23 wants to merge 3 commits intosktime:mainfrom
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- Create BaseBayesianRegressor that encapsulates PyMC backend logic - Refactor BayesianLinearRegressor to inherit from BaseBayesianRegressor - Implement standardized MCMC sampling, prediction, and parameter retrieval - Add get_posterior_summary method for posterior analysis - Update dependencies and imports Closes sktime#389
- Add arviz to all_extras in pyproject.toml for Bayesian functionality - Required for BaseBayesianRegressor and Bayesian estimators
arnavk23
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BaseBayesianRegressor class for Bayesian model interface
- Remove unused imports from BaseBayesianRegressor - Remove unused training_data variable from BayesianLinearRegressor - Fix code formatting and linting issues
fkiraly
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Mar 6, 2026
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fkiraly
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Thanks!
This seems to be primarily MC-based. I think a base Bayesian regressor should be able to accommodate non-MC-paradigms too.
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Reference Issues/PRs
Towards #389
What does this implement/fix? Explain your changes.
This PR implements the Bayesian model interface design proposed in issue #389. It introduces a
BaseBayesianRegressorclass that encapsulates PyMC backend logic for MCMC sampling and posterior predictive inference, making it easier to create new Bayesian estimators.The base class handles all PyMC complexity, allowing subclasses to focus only on defining their probabilistic models via the
_build_model()method.Does your contribution introduce a new dependency? If yes, which one?
Yes, adds
arvizas an optional dependency for Bayesian posterior analysis and diagnostics.What should a reviewer concentrate their feedback on?
Did you add any tests for the change?
The existing tests for
BayesianLinearRegressorshould continue to work with the refactored implementation. Additional tests for the base class could be added in a follow-up PR.Any other comments?
This implementation follows the detailed design specification from the issue #389. The base class provides a clean abstraction that will make it easier to implement additional Bayesian estimators in the future.
PR checklist
For all contributions
How to: add yourself to the all-contributors file in the
skproroot directory (not theCONTRIBUTORS.md). Common badges:code- fixing a bug, or adding code logic.doc- writing or improving documentation or docstrings.bug- reporting or diagnosing a bug (get this pluscodeif you also fixed the bug in the PR).maintenance- CI, test framework, release.See here for full badge reference
For new estimators
docs/source/api_reference/taskname.rst, follow the pattern.Examplessection.python_dependenciestag and ensured dependency isolation, see the estimator dependencies guide.