of trajectories, the databanks, $\nu^A_{\alpha,\beta}$ and $\pi^A_\alpha$ – different from their unbiased statistical steady state counterparts. By construction we then have $\pi^A_\alpha =
\int_{B_\alpha} \varrho_A(\zz) d\zz$ which, from , means that $$\label{eq:piA}
  \rho_A = \sum_{\alpha=1}^\Lambda \pi_\alpha^A\le 1, \qquad \rho_B = 1 -\rho_A.$$ Similarly we can compute $\nu_R$ from the total flux into $B$: $$\label{eq:nuRflux}
  \nu_R = \sum_{\substack{\text{$\alpha$ such that}\\ 
      \text{$B_\alpha$ not in $B$}}} \ \ 
  \sum_{\substack{\text{$\beta$ such that}\\ 
      \text{$B_\beta$ in $B$}}}
  \pi^A_\alpha \nu_{\alpha,\beta}^A.$$ Finally, we can get $k_{A,B}$ and $k_{B,A}$ from  and be done.

#### Comparison with TIS, FFS, and Markovian milestoning with Voronoi tessellations. {#comparison-with-tis-ffs-and-markovian-milestoning-with-voronoi-tessellations. .unnumbered}

Before illustrating our procedure to compute the reaction rate on a simple example, let us compare it to existing methods. First, let us point out that it is very similar in spirit to both TIS [@tis2; @pptis; @tis3] and FFS[@ffs; @valerianiFFS]. Like TIS and FFS, our method is based on selecting only those trajectories which come from the reactant state $A$. The difference is in the way this selection is achieved. In particular, unlike in TIS and FFS, we do not require that the interfaces be ordered monotonously, i.e. we do not need that a trajectory coming from $A$ crosses all the preceding 