iDQ documentation
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`iDQ`, or inferential Data Quality, is a statistical inference framework for data quality.
It specifically focuses on the problem of non-Gaussian noise transients within gravitational-wave detectors, but the underlying formalism has broader applicability.

`iDQ` works with vectorized representations of the detector's *auxiliary state* and searches for correlations between that vectorized state and noise transients in :math:`h(t)` with the end goal of producing a calibrated estimate of the probability that there is a noise artifact in :math:`h(t)`, conditioned on the auxiliary state, as a function of time.
This is primarily done through supervised learning, and `iDQ` supports a variety of supervised learning techniques.
Furthermore, these concepts extend well beyond 1-dimensional data (i.e.: timeseries) and could be applied to any streaming classification problem.

`iDQ` not only supports classification through 2-class classification schemes, but also supports automatic retraining and calibration to deal with detector non-stationarity.
In this way, the algorithm automatically re-learns which correlations are important as they change over time and returns meaningful probabilistic statements that can be interpreted immediately without further processing.

.. _welcome-contents:

Contents
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.. toctree::
    :caption: Getting Started
    :maxdepth: 2

    installation
    quickstart
    tutorials/tutorials

.. toctree::
    :caption: Architecture
    :maxdepth: 2

    formalism
    abstractions
    data_products
    applications
    references/references

.. toctree::
    :caption: User Guide
    :maxdepth: 2

    workflow
    data_backends
    classifiers
    reporters
    configuration

.. toctree::
    :caption: API Reference
    :maxdepth: 2

    executables/executables
    api/api


.. _welcome-indices:

Indices and tables
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* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`