Parallel-Bilby-Phase-Marginalisation-Review.md

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 | PP-test | ![pp_test_phase_marginalisation_on](uploads/fdc426f0efa813babc9598dab8cdc09b/pp_test_phase_marginalisation_on.png) | ![pp_test_phase_marginalisation_off](uploads/3fe842bbdbbba663e6cf82f569f8c01a/pp_test_phase_marginalisation_off.png) |
 | Meta | ![phase_marginalisation_on_meta](uploads/3476450a96b1924f155139e89cbcd888/phase_marginalisation_on_meta.png) | ![phase_marginalisation_off_meta](uploads/330cdb3c4d57f0a5f2bca270404ac580/phase_marginalisation_off_meta.png) |
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 These demonstrate that parallel bilby is unbaised when analysing with/without phase marginalisation. 
 
 ## PP-test setup
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 ## Histogramming the Evidence Residuals
 Below is a histogram of the evidence residuals (logZ_on - logZ_off) from the above runs. 
 ![Evidence Residual](uploads/fd9915b107dc3da5178824e681d2ea0f/test.png)
-This demonstrates that the LogZ are relatively the same, with a slight difference, most likely due to sampling. Note that the sigma of the Gaussian fit is close to the typical evidence uncertainty reported by dynesty
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+This demonstrates that the LogZ are relatively the same for the two PE run sets. The slight difference is most likely due to sampling (the sigma of the Gaussian fit of the residuals is close to the typical evidence uncertainty reported by dynesty)
 
 ## Example Corner
 The following corner plot is from an injection with SNRs of {H1: 12.89, L1: 10.48}. Note that although the posteriors look a bit messy, this has been made with one run, rather than combining the results from multiple runs.