POPS Regression#
Date: Mar 23, 2026 Version: 0.4.1
Useful links: Source Repository | Issues & Ideas
Bayesian regression for low-noise data with misspecification uncertainty estimation using the POPS (Pointwise Optimal Parameter Sets) algorithm.
popsregression provides POPSRegression, a
scikit-learn compatible estimator that extends
BayesianRidge to estimate weight uncertainties
accounting for model misspecification. This is particularly useful for
surrogate models fit to near-deterministic data, where standard Bayesian
regression significantly underestimates predictive uncertainty.
Installation and quick introduction to POPS Regression.
Background on the POPS algorithm and how to use it effectively.
Detailed API documentation for POPSRegression.
Gallery of examples demonstrating POPS Regression usage.