Fig. 6

Convergence of the Monte-Carlo method for a binary tree. The relative error ε is shown as a function of the number of random walk realisations generated, k. A: error generated on the binary tree in Fig. 4A with parameter set A. The red line shows a fit for $\epsilon \sim k^{-0.54}$. B: the convergence error on a binary tree of depth 16 (65,536 nodes). The red line demonstrates a fit for $\epsilon \sim k^{-0.5}$, the typical rate of convergence of a Monte-Carlo integration