General-Review-(post-GW190814).md

@@ -25,7 +25,7 @@ This review wiki covers the changes to the code based in order to incorporate po
   * [X] Monte-Carlo point-estimates and error estimates derived in [monte-carlo-derivations.pdf](uploads/06f0131a22f824afdf9acebd5460981a/monte-carlo-derivations.pdf) are correct
     * *Note!* Reed found small typos in the error estimates published on arXiv (v1 and v2) associated with covariance between "f" and "g", etc. The attached notes correct this (and it has been corrected in the code).
     * The attached notes also provide explicit formulas for the estimation of the bayes factor, which are only suggested in the arxiv versions. The code implements what is shown in the attached notes.
-    * Review statement: I've reviewed the notes and was able to reproduce all Monte-Carlo equations independently. The Bayes factor estimates are not yet reviewed but everything else in the attached notes is.
+    * Review statement: I've reviewed the notes and was able to reproduce all Monte-Carlo equations independently. The Bayes factor estimates are also reviewed.
   * [X] Monte-Carlo algorithms are correctly implemented within calls to [utils.prob_odds](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/master/mmax_model_selection/utils.py#L434) (or [utils.post_prior_bayes](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/master/mmax_model_selection/utils.py#L476) as appropriate).
     * Review statement: I have read over the code and am satisfied that it computes what is outlined in  [LIGO-P2000216](https://dcc.ligo.org/LIGO-P2000216) and [monte-carlo-derivations.pdf](uploads/06f0131a22f824afdf9acebd5460981a/monte-carlo-derivations.pdf). I checked all functions and found very minor typos that did not affect the final results - these have nevertheless been corrected in 1f035f47c04ddd4c7f69947b96ff0c759479b68d . The code is overall extremely clear and easy to follow.
   * [X] population priors are correctly implemented within calls to [utils.population_weights](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/master/mmax_model_selection/utils.py#L107)