.. _model_target:

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Model Description
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.. toctree::
  :hidden:
  :maxdepth: 2

This page (currently under construction) will provide an abbreviated overview of the statistical model.

Users should refer to `Edmonsond & Dyer (2025) <https://doi.org/10.5194/egusphere-2024-2579>`_ for an extended description of the model, guidance on choosing priors for key model parameters, synthetic proof-of-concept experiments, and advice on interpreting the results.

The statistical model is built using the probabilistic programming package `PyMC <https://www.pymc.io/welcome.html>`_. For users who are new to Bayesian modeling, the `PyMC documentation <https://www.pymc.io/projects/docs/en/stable/learn.html>`_ includes links to useful introductory resources.

.. Key features
.. =============
.. *
.. *

.. The statistical model is built using the probabilistic programming package `PyMC <https://www.pymc.io/welcome.html>`_. For users who are new to Bayesian modeling, the `PyMC documentation <https://www.pymc.io/projects/docs/en/stable/learn.html>`_ includes links to useful introductory resources.

.. Inference model structure
.. ===========================

.. Age constraints
.. ----------------

.. Sample ages
.. ------------

.. Proxy signal
.. ------------

.. Local influences on proxy records
.. ----------------------------------

.. Sampling the posterior
.. -----------------------