.. popsregression documentation master file

:notoc:

################
POPS Regression
################

**Date**: |today| **Version**: |version|

**Useful links**:
`Source Repository <https://github.com/tomswinburne/popsregression>`__ |
`Issues & Ideas <https://github.com/tomswinburne/popsregression/issues>`__

Bayesian regression for low-noise data with misspecification uncertainty
estimation using the POPS (Pointwise Optimal Parameter Sets) algorithm.

``popsregression`` provides :class:`~popsregression.POPSRegression`, a
scikit-learn compatible estimator that extends
:class:`~sklearn.linear_model.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.


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    .. grid-item-card:: Getting started
        :img-top: _static/img/index_getting_started.svg
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        Installation and quick introduction to POPS Regression.

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        .. button-ref:: quick_start
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            To the getting started guideline

    .. grid-item-card::  User guide
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        Background on the POPS algorithm and how to use it effectively.

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        .. button-ref:: user_guide
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            To the user guide

    .. grid-item-card::  API reference
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        Detailed API documentation for POPSRegression.

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        .. button-ref:: api
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            To the reference guide

    .. grid-item-card::  Examples
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        Gallery of examples demonstrating POPS Regression usage.

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        .. button-ref:: general_examples
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            To the gallery of examples


.. toctree::
    :maxdepth: 3
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    quick_start
    user_guide
    api
    auto_examples/index