interpolated using, for example, a linear or piecewise constant process. An approximate Monte-Carlo approximation of (3) is then obtained by sampling for i = 1 , ..., N , and averaging An error analysis of (6) is available in Graham et al. (1996). For an approxi- mate Monte-Carlo approximation of (4) the simulation is stopped as soon as the path exits the domain. The estimate ˜ u$_{h}$ ( x x, t ) is biased for any h > 0, but E { ˜ u$_{h}$ ( x x, t ) } → u ( x x, t ) as h → 0. Traditionally, h and N would be selected so that the Monte Carlo error is comparable to the bias, optimising the conver- gence rate. This approach is limited by the computational expense, which is proportional to N ⧸ h . Alternatively, we can consider debiasing the estima- tor using such a numerical scheme, which is described in the remainder of this section. The reader is referred to Rhee and Glynn (2014) for debiasing expectations with respect to SDEs, and McLeish (2011) for a more general discussion of debiasing Monte Carlo estimators. 17 ˜ u$_{h}$ ( x x, t ) = 1 N N ∑ i =1 ˜ u ( i ) h . (6) ˜ u ( i ) h = f ( ˜ X x $_{Mh}$) exp ( − h$^{M}$ − 1 ∑ m =0 c ( ˜ X x $_{mh}$, t − mh ) ) ,