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ThomasGesseyJones authored Jul 1, 2024
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Expand Up @@ -9,7 +9,7 @@ Overview
:Author: Thomas Gessey-Jones
:Version: 1.0.0
:Homepage: https://github.com/ThomasGesseyJones/FullyBayesianForecastsExample
:Letter: https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G
:Letter: https://ui.adsabs.harvard.edu/abs/2024PhRvD.109l3541G/abstract

.. image:: https://img.shields.io/badge/python-3.8-blue.svg
:target: https://www.python.org/downloads/
Expand All @@ -24,7 +24,7 @@ Overview

Example of a fully Bayesian forecast performed using an `Evidence Network <https://ui.adsabs.harvard.edu/abs/2023arXiv230511241J/abstract>`__.
This code also replicates the analysis of
`Gessey-Jones et al. (2023) <https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G>`__.
`Gessey-Jones et al. (2024) <https://ui.adsabs.harvard.edu/abs/2024PhRvD.109l3541G/abstract>`__.
This repository thus serves the dual purposes of providing an example code base others
can modify to perform their own fully Bayesian forecasts and also providing a
reproducible analysis pipeline for the letter.
Expand Down Expand Up @@ -113,7 +113,7 @@ scripts can be run from the terminal using the following commands:
python visualize_forecasts.py
to run with the default noise level of 15 mK and replicate the
analysis from `Gessey-Jones et al. (2023) <https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G>`__.
analysis from `Gessey-Jones et al. (2024) <https://ui.adsabs.harvard.edu/abs/2024PhRvD.109l3541G/abstract>`__.
Alternatively you can pass
the scripts a command line argument to specify the experiments noise level in K. For example
to run with a noise level of 100 mK you would run the following commands:
Expand All @@ -140,7 +140,7 @@ The various figures produced in the analysis are stored in the
figures_and_results directory alongside the timing_data to assess the
performance of the methodology and some summary statistics of the evidence
networks performance. The figures and data generated in the
analysis for `Gessey-Jones et al. (2023) <https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G>`__ are provided in this
analysis for `Gessey-Jones et al. (2024) <https://ui.adsabs.harvard.edu/abs/2024PhRvD.109l3541G/abstract>`__ are provided in this
repository for reference, alongside the figures generated for an earlier
version of the letter which did not model foregrounds.

Expand All @@ -149,31 +149,28 @@ Licence and Citation

The software is free to use on the MIT open source license.
If you use the software for academic purposes then we request that you cite
the `letter <https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G>`__ ::
the `letter <https://ui.adsabs.harvard.edu/abs/2024PhRvD.109l3541G/abstract>`__ ::

Gessey-Jones, T. and W. J. Handley. “Fully Bayesian Forecasts with Evidence
Networks.” (2023). arXiv:2309.06942
Gessey-Jones, T. and W. J. Handley. “Fully Bayesian forecasts with evidence
networks.” (June 2024). Physical Review D, Volume 109, Issue 12, 123541

If you are using Bibtex you can use the following to cite the letter

.. code:: bibtex
@ARTICLE{2023arXiv230906942G,
author = {{Gessey-Jones}, T. and {Handley}, W.~J.},
title = "{Fully Bayesian Forecasts with Evidence Networks}",
journal = {arXiv e-prints},
keywords = {Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, General Relativity and Quantum Cosmology},
year = 2023,
month = sep,
eid = {arXiv:2309.06942},
pages = {arXiv:2309.06942},
doi = {10.48550/arXiv.2309.06942},
archivePrefix = {arXiv},
eprint = {2309.06942},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2023arXiv230906942G},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{2024PhRvD.109l3541G,
author = {{Gessey-Jones}, T. and {Handley}, W.~J.},
title = "{Fully Bayesian forecasts with evidence networks}",
journal = {\prd},
year = 2024,
month = jun,
volume = {109},
number = {12},
eid = {123541},
pages = {123541},
doi = {10.1103/PhysRevD.109.123541},
adsurl = {https://ui.adsabs.harvard.edu/abs/2024PhRvD.109l3541G},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}}
Note some of the packages used (see below) in this code have their own licenses that
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