Add flows paper

.pip_readme.rst

@@ -17,14 +17,16 @@ harmonic
     :target: https://arxiv.org/abs/2207.04037
 .. image:: http://img.shields.io/badge/arXiv-2307.00048-red.svg?style=flat
     :target: https://arxiv.org/abs/2307.00048
+.. image:: http://img.shields.io/badge/arXiv-2405.05969-red.svg?style=flat
+    :target: https://arxiv.org/abs/2405.05969
 
 
 **Python package to efficiently compute the learnt harmonic mean estimator of the Bayesian evidence**
 
 
 ``harmonic`` is an open source, well tested and documented Python implementation of the *learnt harmonic mean estimator* (`McEwen et al. 2021 <https://arxiv.org/abs/2111.12720>`_) to compute the marginal likelihood (Bayesian evidence), required for Bayesian model selection.
 
-While ``harmonic`` requires only posterior samples, and so is agnostic to the technique used to perform Markov chain Monte Carlo (MCMC) sampling, ``harmonic`` works exceptionally well with MCMC sampling techniques that naturally provide samples from multiple chains by their ensemble nature, such as affine invariant ensemble samplers.  We therefore advocate use of `harmonic` with the popular `emcee <https://github.com/dfm/emcee>`_ code implementing the affine invariant sampler of `Goodman & Weare (2010) <https://cims.nyu.edu/~weare/papers/d13.pdf>`_.
+While ``harmonic`` requires only posterior samples, and so is agnostic to the technique used to perform Markov chain Monte Carlo (MCMC) sampling, ``harmonic`` works well with MCMC sampling techniques that naturally provide samples from multiple chains by their ensemble nature, such as affine invariant ensemble samplers.  For instance, ``harmonic`` can be used with the popular `emcee <https://github.com/dfm/emcee>`_ code implementing the affine invariant sampler of `Goodman & Weare (2010) <https://cims.nyu.edu/~weare/papers/d13.pdf>`_, or the `NumPyro <https://github.com/pyro-ppl/numpyro>`_ code implementing various MCMC algorithms.
 
 Basic usage is highlighted in this `interactive demo <https://colab.research.google.com/github/astro-informatics/harmonic/blob/main/notebooks/basic_usage.ipynb>`_. 
 
@@ -50,6 +52,21 @@ A BibTeX entry for the paper is:
           year = 2021
      }
 
+Please *also* cite `Polanska et al. (2024) <https://arxiv.org/abs/2405.05969>`_ if using normalizing flow models.
+
+A BibTeX entry for the paper is:
+
+.. code-block::
+
+    @misc{polanska2024learned,
+        title={Learned harmonic mean estimation of the Bayesian evidence with normalizing flows}, 
+        author={Alicja Polanska and Matthew A. Price and Davide Piras and Alessio Spurio Mancini and Jason D. McEwen},
+        year={2024},
+        eprint={2405.05969},
+        archivePrefix={arXiv},
+        primaryClass={astro-ph.IM}
+    }
+
 Please *also* cite `Spurio Mancini et al. (2022) <https://arxiv.org/abs/2207.04037>`_ if this code has been of use in a simulation-based inference project.
 
 A BibTeX entry for the paper is:
@@ -65,15 +82,6 @@ A BibTeX entry for the paper is:
         year     = {2023}
      }
 
-.. code-block::
-
-     @inproceedings{polanska:harmonic_nfs_maxent,
-        author    = {A.~Polanska and M.~A.~Price and A.~Spurio Mancini and J.~D.~McEwen},
-        booktitle = {International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering},
-        eprint    = {arXiv:2307.00048},
-        title     = {Learned harmonic mean estimation of the marginal likelihood with normalizing flows},
-        year      = {2023}
-     }
 
 License
 -------

README.rst

@@ -16,10 +16,12 @@
     :target: https://arxiv.org/abs/2207.04037
 .. |arxiv3| image:: http://img.shields.io/badge/arXiv-2307.00048-orange.svg?style=flat
     :target: https://arxiv.org/abs/2307.00048
+.. |arxiv4| image:: http://img.shields.io/badge/arXiv-2405.05969-orange.svg?style=flat
+    :target: https://arxiv.org/abs/2405.05969
 .. .. image:: https://img.shields.io/pypi/pyversions/harmonic.svg
 ..     :target: https://pypi.python.org/pypi/harmonic/
 
-|github| |tests| |docs| |codecov| |pypi| |licence| |arxiv1| |arxiv2| |arxiv3|
+|github| |tests| |docs| |codecov| |pypi| |licence| |arxiv1| |arxiv2| |arxiv3| |arxiv4|
 
 
 .. |logo| image:: ./docs/assets/harm_badge_simple.svg
@@ -34,7 +36,7 @@
 
 For an accessible overview of the *learnt harmonic mean estimator* please see this `Towards Data Science article <https://towardsdatascience.com/learnt-harmonic-mean-estimator-for-bayesian-model-selection-47258bb0fc2e>`_.
 
-While ``harmonic`` requires only posterior samples, and so is agnostic to the technique used to perform Markov chain Monte Carlo (MCMC) sampling, ``harmonic`` works exceptionally well with MCMC sampling techniques that naturally provide samples from multiple chains by their ensemble nature, such as affine invariant ensemble samplers.  We therefore advocate use of `harmonic` with the popular `emcee <https://github.com/dfm/emcee>`_ code implementing the affine invariant sampler of `Goodman & Weare (2010) <https://cims.nyu.edu/~weare/papers/d13.pdf>`_.
+While ``harmonic`` requires only posterior samples, and so is agnostic to the technique used to perform Markov chain Monte Carlo (MCMC) sampling, ``harmonic`` works well with MCMC sampling techniques that naturally provide samples from multiple chains by their ensemble nature, such as affine invariant ensemble samplers.  For instance, ``harmonic`` can be used with the popular `emcee <https://github.com/dfm/emcee>`_ code implementing the affine invariant sampler of `Goodman & Weare (2010) <https://cims.nyu.edu/~weare/papers/d13.pdf>`_, or the `NumPyro <https://github.com/pyro-ppl/numpyro>`_ code implementing various MCMC algorithms.
 
 Basic usage is highlighted in this `interactive demo <https://colab.research.google.com/github/astro-informatics/harmonic/blob/main/notebooks/basic_usage.ipynb>`_. 
 
@@ -67,11 +69,11 @@ The ``harmonic`` package can also be installed from source by running
     git clone https://github.com/astro-informatics/harmonic
     cd harmonic
 
-and running the install script, within the root directory, with one command 
+and installing within the root directory, with one command 
 
 .. code-block:: bash
 
-    bash build_harmonic.sh
+    pip install .
 
 To check the install has worked correctly run the unit tests with 
 
@@ -115,6 +117,22 @@ A BibTeX entry for the paper is:
           year = 2021
      }
 
+
+Please *also* cite `Polanska et al. (2024) <https://arxiv.org/abs/2405.05969>`_ if using normalizing flow models.
+
+A BibTeX entry for the paper is:
+
+.. code-block::
+
+    @misc{polanska2024learned,
+        title={Learned harmonic mean estimation of the Bayesian evidence with normalizing flows}, 
+        author={Alicja Polanska and Matthew A. Price and Davide Piras and Alessio Spurio Mancini and Jason D. McEwen},
+        year={2024},
+        eprint={2405.05969},
+        archivePrefix={arXiv},
+        primaryClass={astro-ph.IM}
+    }
+
 Please *also* cite `Spurio Mancini et al. (2022) <https://arxiv.org/abs/2207.04037>`_ if this code has been of use in a simulation-based inference project.
 
 A BibTeX entry for the paper is:
@@ -130,19 +148,6 @@ A BibTeX entry for the paper is:
         year     = {2023}
      }
 
-Please *also* cite `Polanska et al. (2023) <https://arxiv.org/abs/2307.00048>`_ if using normalizing flow models.
-
-A BibTeX entry for the paper is:
-
-.. code-block::
-
-     @inproceedings{polanska:harmonic_nfs_maxent,
-        author    = {A.~Polanska and M.~A.~Price and A.~Spurio Mancini and J.~D.~McEwen},
-        booktitle = {International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering},
-        eprint    = {arXiv:2307.00048},
-        title     = {Learned harmonic mean estimation of the marginal likelihood with normalizing flows},
-        year      = {2023}
-     }
 
 License
 =======

docs/index.rst

@@ -1,4 +1,4 @@
-|GitHub| |Build Status| |Docs| |CodeCov| |PyPI| |GPL license| |ArXiv1| |ArXiv2| |ArXiv3|
+|GitHub| |Build Status| |Docs| |CodeCov| |PyPI| |GPL license| |ArXiv1| |ArXiv2| |ArXiv3| |ArXiv4|
 
 .. |GitHub| image:: https://img.shields.io/badge/GitHub-harmonic-brightgreen.svg?style=flat
    :target: https://github.com/astro-informatics/harmonic
@@ -18,6 +18,9 @@
 .. |ArXiv3| image:: http://img.shields.io/badge/arXiv-2307.00048-orange.svg?style=flat
    :target: https://arxiv.org/abs/2307.00048
 
+.. |ArXiv4| image:: http://img.shields.io/badge/arXiv-2405.05969-orange.svg?style=flat
+    :target: https://arxiv.org/abs/2405.05969
+
 .. |GPL license| image:: https://img.shields.io/badge/License-GPL-blue.svg
    :target: http://perso.crans.org/besson/LICENSE.html
 

docs/user_guide/install.rst

@@ -38,7 +38,7 @@ Once within a fresh environment **harmonic** may be installed by cloning the Git
     git clone https://github.com/astro-informatics/harmonic
     cd harmonic
 
-and running the install script, within the root directory, with one command 
+and installing within the root directory, with one command 
 
 .. code-block:: bash