Add flows paper and update sampler recommendations.

.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

@@ -36,7 +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>`_.