Additional-Review-(marginalization-over-joint-population+EoS-uncertainty).md

@@ -32,7 +32,10 @@ The new version of the code relies on the user to specify the draw probability,
 
 **bad population reweighing**
 
-**WRITE ME**
+The previous version of the code implemented population priors by reweighing single-event PE samples. This was done within [this code block](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/c8b0d297d11b0a350c23ee44832d24b899729d8f/bin/mmax-model-selection#L227-270). Importantly, we see that the total weight for each PE sample was computed as the sum of population priors (implicit from [this block](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/c8b0d297d11b0a350c23ee44832d24b899729d8f/bin/mmax-model-selection#L263-270)). That sum was then normalized by the default PE prior [here](https://git.ligo.org/reed.essick/mmax-model-selection/-/blob/c8b0d297d11b0a350c23ee44832d24b899729d8f/bin/mmax-model-selection#L279).
+
+However, as is shown in the [technical note](#technical
+
 
   * bad population weights? did I have the normalizations messed up?
     - note that there is excellent agreement when we have a fixed population (all uncertainty is from the EoS), but there is bad agreement when we have uncertainty in the population and in the EoS. This suggests the issue is associated with the marginalization over the population.