almost changed nothing

latex/main paper/main.tex

@@ -1638,8 +1638,9 @@ \section{Limitations and Future Work}
 and the recursive estimator for $e^{E[X]}$ (as demonstrated in example \ref{ex:exp int}).
 These discoveries subsequently led us to other relevant literature. \\
 
-The section on RRMC was composed prior to our discovery of algorithm \ref{jl:main poisson},
-and as such are not integrated. Our application of
+The section on RRMC was written before we discovered algorithm \ref{jl:main poisson}.
+It may be possible to circumvent recursion in RRMC similar to algorithm \ref{jl:main poisson}.
+Our application of
 recursion in recursion in RRMC for IVPs is primarily aimed at achieving convergence behavior
 akin to classical solvers, while maintaining unbiasedness. This recursive approach allows us to
 'freeze' terms in the outer recursion without bias. The next logical step would be
@@ -1696,13 +1697,11 @@ \section{Limitations and Future Work}
 % $A(s)$, which is necessary
 % for optimal IBC.
 
-One of the elements lacking in our findings is rigor. We believe
-that RMC is an informal approach to an unbiased estimate of
-the Von Neumann series to the corresponding integral equation.
+One of the elements lacking in our findings is rigor.
 \cite{ermakov_monte_2021} presents Theorems $1$ and $2$ to
-show that their estimates have finite variance and provide an expression for it.
+show that their estimators have finite variance and provide an expression for it.
 Before becoming aware of \cite{ermakov_monte_2021}, we
-previously derived a similar expression (with errors)
+previously attempted to derive an expression for the variance
 by employing the law of total variance, similar to (16) in \cite{rath_ears_2022}. \\
 We believe that proving the optimality of IBC in Example \ref{ex:CV RRMC IVP} is feasible
 but tedious.