Add geovi paper

docs/source/user/citations.rst

@@ -48,3 +48,16 @@ NIFTy-related publications
       adsurl = {http://cdsads.u-strasbg.fr/abs/2017arXiv170801073S},
       adsnote = {Provided by the SAO/NASA Astrophysics Data System}
       }
+
+    @article{geovi,
+      author = {Frank, Philipp and Leike, Reimar and Enßlin, Torsten A.},
+      title = {Geometric Variational Inference},
+      journal = {Entropy},
+      volume = {23},
+      year = {2021},
+      number = {7},
+      article-number = {853},
+      url = {https://www.mdpi.com/1099-4300/23/7/853},
+      issn = {1099-4300},
+      doi = {10.3390/e23070853}
+    }

docs/source/user/ift.rst

@@ -258,4 +258,4 @@ Furthermore the KL is represented as a stochastic estimate using a set of sample
 
 A visual comparison of the MGVI and GeoVI algorithm can be found in `variational_inference_visualized.py <https://gitlab.mpcdf.mpg.de/ift/nifty/-/blob/NIFTy_7/demos/variational_inference_visualized.py>`_.
 
-.. [6] P. Frank, R. Leike, and T.A. Enßlin (2021), "Geometric Variational Inference"; `[arXiv:2105.10470] <https://arxiv.org/abs/2105.10470>`_
+.. [6] P. Frank, R. Leike, and T.A. Enßlin (2021), "Geometric Variational Inference"; `[arXiv:2105.10470] <https://arxiv.org/abs/2105.10470>`_ `[doi] <https://doi.org/10.3390/e23070853>`_

src/minimization/kl_energies.py

@@ -496,6 +496,7 @@ def GeoMetricKL(mean, hamiltonian, n_samples, minimizer_samp, mirror_samples,
     --------
     `Geometric Variational Inference`, Philipp Frank, Reimar Leike,
     Torsten A. Enßlin, `<https://arxiv.org/abs/2105.10470>`_
+    `<https://doi.org/10.3390/e23070853>`_
     """
     if not isinstance(hamiltonian, StandardHamiltonian):
         raise TypeError