A comparison of 95% confidence intervals for the solution of the 1D advection-diffusion model obtained using CLT via the EA and debiasing ap- proaches, over a range of parameter values at t = 5, x = 0 . 9, is given in Table 1, along with the average time taken to obtain each estimate. For the EA approach, the confidence interval is constructed from 1000 replicates of ˆ u ( x, t ), each obtained using 10 000 simulations, and for debiasing 1000 repli- cates of u ‡ ( x, t ), each obtained using N † = 10000. The halting value for the debiasing approach is drawn from a geometric distribution with parameter 0.45. Over the entire parameter range the EA approach has a lower work- variance product, showing that it is a more efficient approach than debiasing in this case. With regards to EA approaches, Brownian motion is a poor proposal when the P´ eclet number (proportional to b ⧸ a ) is large, leading to a large rejection rate. As can be seen in Table 1, as the P´ eclet number in- creases, the computational expense of obtaining the estimate increases, owing to the low acceptance rate of the proposed paths. This limitation is shared 22 Table 1: A comparison of 95% confidence intervals of the solution to the advection-diffusion example at t = 5, x = 0 . 9, for a range of drift values. a b True 95% Confidence Interval Average Time (s) EA Debiasing EA Debiasing 0.01 0.1 56.13 56 . 11 ± 2 . 3 × 10 − 2 55 . 90 ± 2 . 8 × 10 − 1 0.140 0.436 0.01 0.2 19.03 19 . 03 2 . 2 10 − 2 18 . 94 6 . 5 10 − 1 0.378 0.460 0.01 0.3 5.223 5 . 227 ± 1 . 3 × 10 − 2 5 . 330 ± 3 . 6 × 10 − 1 1.248 0.216 0.01 0.4 1.833 1 . 831 8 . 7 10 − 3 2 . 141 3 . 7 10 − 1 5.316 0.392