minor edits to paper and references

ejhigson · Sep 16, 2018 · ca7bdc1 · ca7bdc1
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diff --git a/docs/paper/paper.bib b/docs/paper/paper.bib
@@ -165,21 +165,6 @@ @article{DESCollaboration2017
 volume = {98},
 year = {2018}
 }
-@article{Joudaki2016,
-archivePrefix = {arXiv},
-arxivId = {1601.05786},
-author = {Joudaki, Shahab and Blake, Chris and Heymans, Catherine and Choi, Ami and Harnois-Deraps, Joachim and Hildebrandt, Hendrik and Joachimi, Benjamin and Johnson, Andrew and Mead, Alexander and Parkinson, David and Viola, Massimo and van Waerbeke, Ludovic},
-doi = {10.1093/mnras/stw2665},
-eprint = {1601.05786},
-issn = {0035-8711},
-journal = {Monthly Notices of the Royal Astronomical Society},
-number = {May 2018},
-pages = {2033--2052},
-title = {{CFHTLenS revisited: assessing concordance with Planck including astrophysical systematics}},
-url = {http://dx.doi.org/10.1093/mnras/stw2665},
-volume = {2052},
-year = {2016}
-}
 @misc{zenododypolychord,
 author = {Higson, Edward},
 doi = {10.5281/zenodo.1328175},

diff --git a/docs/paper/paper.md b/docs/paper/paper.md
@@ -21,7 +21,7 @@ bibliography: paper.bib
 # Summary
 
 Nested sampling [@Skilling2006] is a popular numerical method for calculating Bayesian evidences and generating posterior samples given some likelihood and prior.
-The initial development of the algorithm was targeted at evidence calculation, but implementations such as ``MultiNest`` [@Feroz2008; @Feroz2009; @Feroz2013] and ``PolyChord`` [@Handley2015a; @Handley2015b] are now used extensively for parameter estimation in scientific research - see for example [@Joudaki2016; @DESCollaboration2017; @Chua2018].
+The initial development of the algorithm was targeted at evidence calculation, but implementations such as ``MultiNest`` [@Feroz2008; @Feroz2009; @Feroz2013] and ``PolyChord`` [@Handley2015a; @Handley2015b] are now used extensively for parameter estimation in scientific research (and in particular in astrophysics); see for example [@DESCollaboration2017; @Chua2018].
 Nested sampling performs well compared to Markov chain Monte Carlo (MCMC)-based alternatives at exploring multimodal and degenerate distributions, and the ``PolyChord`` software is well-suited to high-dimensional problems.
 
 Dynamic nested sampling [@Higson2017b] is a generalisation of the nested sampling algorithm which dynamically allocates samples to the regions of the posterior where they will have the greatest effect on calculation accuracy. This allows order-of-magnitude increases in computational efficiency, with the largest gains for high dimensional parameter estimation problems.