ML-GPR user guide
=================

ML-GPR separates frequency-dependent 21-cm observations by assigning a
Gaussian-process covariance to each physical component. A typical model
contains smooth intrinsic foregrounds, less-smooth chromatic contamination, a
21-cm component, and heteroscedastic thermal noise. The fit produces a
posterior distribution of component reconstructions and power spectra rather
than one deterministic foreground subtraction.

This guide explains model construction, fitting, inference, and posterior
products. Package installation is covered in
:doc:`Getting started <../../getting_started>`. The
:doc:`API reference <../../api/ml_gpr/index>` documents individual Python
objects.

.. toctree::
   :maxdepth: 2

   migration_1_0
   fitting
   covariance_model
   inference
   posterior_products
   multi_epoch
   tutorials