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Bayesian Analysis in Cosmology

Project description

*Cobaya*, a code for Bayesian analysis in Cosmology

:Author: `Jesus Torrado`_ and `Antony Lewis`_

:Source: `Source code at GitHub <>`_

:Documentation: `Documentation at Readthedocs <>`_

:Licence: `LGPL <>`_ + mandatory bug reporting asap + mandatory `arXiv'ing <>`_ of publications using it (see `LICENCE.txt <>`_ for exceptions). The documentation is licensed under the `GFDL <>`_.

:E-mail list: – **sign up for important bugs and release announcements!**

:Support: For general support, CosmoCoffee_; for bugs and issues, use the `issue tracker <>`_.

:Installation: ``pip install cobaya --upgrade --user`` (see the `installation instructions <>`_; in general do *not* clone)

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**Cobaya** (**co**\ de for **bay**\ esian **a**\ nalysis, and Spanish for *Guinea Pig*) is a framework for sampling and statistical modelling: it allows you to explore an arbitrary prior or posterior using a range of Monte Carlo samplers (including the advanced MCMC sampler from CosmoMC_, and the advanced nested sampler PolyChord_). The results of the sampling can be analysed with GetDist_. It supports MPI parallelization (and very soon HPC containerization with Docker/Shifter and Singularity).

Its authors are `Jesus Torrado`_ and `Antony Lewis`_. Some ideas and pieces of code have been adapted from other codes (e.g CosmoMC_ by `Antony Lewis`_ and contributors, and `Monte Python`_, by `Julien Lesgourgues`_ and `Benjamin Audren`_).

**Cobaya** has been conceived from the beginning to be highly and effortlessly extensible: without touching **cobaya**'s source code, you can define your own priors and likelihoods, create new parameters as functions of other parameters...

Though **cobaya** is a general purpose statistical framework, it includes interfaces to cosmological *theory codes* (CAMB_ and CLASS_) and *likelihoods of cosmological experiments* (Planck, Bicep-Keck, SDSS... and more coming soon). Automatic installers are included for all those external modules. You can also use **cobaya** simply as a wrapper for cosmological models and likelihoods, and integrate it in your own sampler/pipeline.

The interfaces to most cosmological likelihoods are agnostic as to which theory code is used to compute the observables, which facilitates comparison between those codes. Those interfaces are also parameter-agnostic, so using your own modified versions of theory codes and likelihoods requires no additional editing of **cobaya**'s source.

The overhead per posterior evaluation is ``< 0.1 ms x dimension`` per posterior evaluation (mostly due to evaluating ``scipy.stats`` logpdf's in the prior), which makes it suitable for most cosmological applications (CAMB_ and CLASS_ take seconds to run), but not necessarily for more general statistical applications, if the evaluation time per pdf involved is of that order or smaller.

How to cite us

As of this version, there is no scientific publication yet associated to this software, so simply mention its `GitHub repository <>`_.

To appropriately cite the modules (samplers, theory codes, likelihoods) that you have used, simply run the script `cobaya-citation` with your input file(s) as argument(s), and you will get *bibtex* references and a short suggested text snippet for each module mentioned in your input file. You can find a usage example :ref:`here <citations>`.


Thanks to `Julien Lesgourgues`_ for support on interfacing CLASS_, and to `Guadalupe Cañas Herrera`_ for extensive and somewhat painful testing.

.. _`Jesus Torrado`:
.. _`Antony Lewis`:
.. _CosmoMC:
.. _CosmoCoffee:
.. _`Monte Python`:
.. _`Julien Lesgourgues`:
.. _`Benjamin Audren`:
.. _Camb:
.. _Class:
.. _GetDist:
.. _PolyChord:
.. _`Guadalupe Cañas Herrera`:

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