these re-entry points and these fluxes only, we would then simulate in the cells pieces of trajectories that are statistically indistinguishable from the unbiased trajectory when it is assigned to $A$. We could then compute the total probability in the cells to get an estimate of $\rho_A$ (and hence $\rho_B=1-\rho_A$) as well as the total flux into $B$ to get $\nu_R$. We claim that there is a simple procedure to do these operations in practice. The key observation is that trajectories assigned to $A$ can be propagated like regular trajectories while they are outside of $A$ and $B$ – the only constraint imposed on them is via a boundary condition in their past: they need to have come from $A$ rather than $B$ last. But we know what this boundary condition entails, at least in a statistical sense: indeed the probability density $\varrho_A(\zz)$ that a trajectory be at $\zz$ and be assigned to $A$ is equal to the statistical steady state probability density $\varrho(\zz)$ for $\zz \in A$ while it is equal to 0 for $\zz \in B$. We also know that the probability flux out of $A$ is the statistical steady state one and the probability flux out of 