# r2_is n=6400 nsamples=5000 seed=6401 imax=1536000; mean C = 1.98860 (2-2/n = 1.99969); walks absorbed before imax: 0
# columns: label i  r=E N_i e^{i/n}  SE  relSE  e^{-y^2/2 or -i^2/(2n^3)}  E N_i e^{i/n+i^2/(2n^3)}  heuristic (1-i/n^2)^n e^{i/n}
y=0.25 128000 0.971079 4.60e-05 4.73e-05 0.969233 1.001905 0.969170
y=0.50 256000 0.884385 3.63e-04 4.10e-04 0.882497 1.002140 0.882035
y=1.00 512000 0.607627 2.04e-04 3.35e-04 0.606531 1.001808 0.603985
y=1.50 768000 0.322971 1.09e-04 3.36e-04 0.324652 0.994821 0.320055
y=2.00 1024000 0.132389 4.80e-05 3.62e-04 0.135335 0.978232 0.130815
y=3.00 1536000 0.010062 6.21e-06 6.17e-04 0.011109 0.905739 0.009895
x=0.5 3200 1.000288 2.00e-04 2.00e-04 0.999980 1.000307 0.999980
x=1.0 6400 0.999471 4.47e-04 4.47e-04 0.999922 0.999549 0.999922
x=2.0 12800 0.999313 4.47e-04 4.47e-04 0.999688 0.999625 0.999687
x=4.0 25600 0.999124 2.83e-04 2.83e-04 0.998751 1.000374 0.998750
x=8.0 51200 0.995677 2.82e-04 2.84e-04 0.995012 1.000668 0.995008
x=16.0 102400 0.981035 3.94e-04 4.02e-04 0.980199 1.000854 0.980166
# sum_{i<=imax} E N_i = 6399.000000 (n-1 = 6399);  mean excursion length sum i E N_i/(n-1) = 6400.0006 (n = 6400; rel dev 8.68e-08)
# sum_{i>=1} |E N_i - e^{-i/n}| (i<=imax, plus e^{-i/n} tail 3.76e-101) = 2.7986  [upward-biased by MC noise]
