can immediately pick a re-entry point into that cell from the appropriate databank and continue the trajectory from that point without having to put it on hold.

The only remaining issue we need to take care of in order to make complete the procedure outlined above is how to pick the re-entry point into $B_\alpha$ among the databanks on the various edges leading into $B_\alpha$. By construction, the re-entry points in the databanks on each edge are unbiased samples of re-entry points conditional on the trajectory entering by that edge. But there are several edges by which the trajectory can enter a given cell, and to introduce no bias we need to pick the edges with the proper probability of re-entrance by that edge. To see how this can be done, imagine that, as we run the simulation in each cell $B_\alpha$, we compute an estimate of the effective rate of exit out that cell and into $B_\beta$ via $$\label{eq:fluxdef}
  \nu_{\alpha,\beta} = \frac{N_{\alpha,\beta}}{T_\alpha}$$ where $N_{\alpha,\beta}$ is the total number of times the trajectory hit the boundary between $B_\alpha$ and $B_\beta$ (which is also the number of times the trajectory in cell $B_\alpha$ had to be re-injected into that cell from 