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21 changes: 10 additions & 11 deletions docs/_sources/index.rst.txt
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############################################################
Welcome to GeoBIPy: Geophysical Bayesian Inference in Python
############################################################
.. geobipy documentation master file, created by
sphinx-quickstart on Fri Oct 11 10:02:08 2024.
You can adapt this file completely to your liking, but it should at least
contain the root `toctree` directive.
This package uses a Bayesian formulation and Markov chain Monte Carlo sampling methods to
derive posterior distributions of subsurface and measured data properties.
The current implementation is applied to time and frequency domain electromagnetic data.
Application outside of these data types is in development.
geobipy documentation
=====================

Add your content using ``reStructuredText`` syntax. See the
`reStructuredText <https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html>`_
documentation for details.

Currently there are two types of data that we have implemented; frequency domain electromagnetic data,
and time domain electromagnetic data.
The package comes with a frequency domain forward modeller, but it does not come with a time domain forward modeller.

.. toctree::
:maxdepth: 2
:caption: Contents:
content/getting_started

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<meta charset="utf-8" />
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<title>Index &mdash; geobipy 2.3.1 documentation</title>
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<meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />

<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Welcome to GeoBIPy: Geophysical Bayesian Inference in Python &mdash; geobipy 2.3.1 documentation</title>
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<div class="local-toc"><ul>
<li><a class="reference internal" href="#">Welcome to GeoBIPy: Geophysical Bayesian Inference in Python</a></li>
<li><a class="reference internal" href="#">geobipy documentation</a></li>
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<section id="welcome-to-geobipy-geophysical-bayesian-inference-in-python">
<h1>Welcome to GeoBIPy: Geophysical Bayesian Inference in Python<a class="headerlink" href="#welcome-to-geobipy-geophysical-bayesian-inference-in-python" title="Link to this heading"></a></h1>
<p>This package uses a Bayesian formulation and Markov chain Monte Carlo sampling methods to
derive posterior distributions of subsurface and measured data properties.
The current implementation is applied to time and frequency domain electromagnetic data.
Application outside of these data types is in development.</p>
<p>Currently there are two types of data that we have implemented; frequency domain electromagnetic data,
and time domain electromagnetic data.
The package comes with a frequency domain forward modeller, but it does not come with a time domain forward modeller.</p>
<section id="geobipy-documentation">
<h1>geobipy documentation<a class="headerlink" href="#geobipy-documentation" title="Link to this heading"></a></h1>
<p>Add your content using <code class="docutils literal notranslate"><span class="pre">reStructuredText</span></code> syntax. See the
<a class="reference external" href="https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html">reStructuredText</a>
documentation for details.</p>
<div class="toctree-wrapper compound">
</div>
</section>


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