update running title in docs

Author: CosmoMatt

docs/index.rst

@@ -11,8 +11,8 @@
 .. |ArXiv| image:: http://img.shields.io/badge/arXiv-2106.03646-orange.svg?style=flat
     :target: https://arxiv.org/abs/2106.03646
 
-ProxNest: Proximal nested sampling for high-dimensional Bayesian inference
-============================================================================
+ProxNest: Proximal nested sampling for high-dimensional Bayesian model selection
+=================================================================================
 
 ``ProxNest`` is an open source, well tested and documented Python implementation of the *proximal nested sampling* algorithm (`Cai et al. 2022 <https://arxiv.org/pdf/2106.03646.pdf>`_) which is uniquely suited for sampling from very high-dimensional posteriors that are log-concave and potentially not smooth (*e.g.* Laplace priors). This is achieved by exploiting tools from proximal calculus and Moreau-Yosida regularisation (`Moreau 1962 <https://hal.archives-ouvertes.fr/hal-01867195/file/Fonctions_convexes_duales_points_proximaux_Moreau_CRAS_1962.pdf>`_) to efficiently sample from the prior subject to the hard likelihood constraint. The resulting Markov chain iterations include a gradient step, approximating (with arbitrary precision) an overdamped Langevin SDE that can scale to very high-dimensional applications.