a re-entry point) and $T_\alpha$ is the total simulation time in cell $B_\alpha$ (i.e. the total time the trajectory in cell $B_\alpha$ was running). If we then denote by $\pi_\alpha$ the probability to find the unbiased trajectory inside cell $B_\alpha$ at statistical steady state, i.e. $$\label{eq:rhocells}
  \pi_{\alpha} = \int_{B_\alpha} \varrho(\zz) d\zz,$$ we see that $\pi_\alpha$ and $\nu_{\alpha,\beta}$ are related as $$\label{eq:peq}
  \sum_{\substack{\beta=1\\ \beta \not=\alpha}
  }^{\Lambda} \pi_{\beta} \nu_{\beta,\alpha} = 
  \sum_{\substack{\beta=1\\ \beta \not=\alpha}}^{\Lambda} 
  \pi_{\alpha} \nu_{\alpha,\beta},\qquad 
  \sum_{\alpha=1}^{\Lambda} \pi_\alpha = 1.$$ The first equation in  simply expresses that, at statistical steady state, the total probability flux into $B_\alpha$ (which is the term at the left hand-side of the first equation in ) must be equal to the total flux out of $B_\alpha$ (which is the term at the right hand-side of the first equation in ). The second equation in  is simply a normalization condition for the probability which follows from $\sum_{\alpha} \pi_\alpha = \sum_{\alpha} \int_{B_\alpha} \varrho(\zz)
d\zz = \int_\Omega \varrho(\zz) d\zz = 1$. We stress that does not require detailed balance (i.e. $\pi_{\beta}
\nu_{\beta,\alpha} \not = \pi_{\alpha} \nu_{\alpha,\beta}$, possibly) and so it holds even for nonequilibrium processes provided only that they are at statistical steady state. Having calculated $\nu_{\alpha,\beta}$ from  and $\pi_\alpha$ from 