# r2_is n=5 nsamples=2000000 seed=11 imax=20; mean C = 1.59992 (2-2/n = 1.60000); walks absorbed before imax: 2000000
# 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 3 1.263886 7.08e-04 5.60e-04 0.969233 1.310214 0.961590
y=0.50 6 1.323996 9.23e-04 6.97e-04 0.882497 1.529062 0.841824
y=1.00 11 0.758225 1.62e-03 2.14e-03 0.606531 1.230260 0.497036
y=1.50 17 0.000000 0.00e+00 nan 0.324652 0.000000 0.100543
x=0.5 3 1.263886 7.08e-04 5.60e-04 0.964640 1.310214 0.961590
x=1.0 5 1.307691 8.33e-04 6.37e-04 0.904837 1.445222 0.890727
x=2.0 10 1.006223 1.62e-03 1.61e-03 0.670320 1.501108 0.574573
x=4.0 20 0.000000 0.00e+00 nan 0.201897 0.000000 0.017471
# sum_{i<=imax} E N_i = 4.000000 (n-1 = 4);  mean excursion length sum i E N_i/(n-1) = 5.0007 (n = 5; rel dev 1.31e-04)
# sum_{i>=1} |E N_i - e^{-i/n}| (i<=imax, plus e^{-i/n} tail 8.27e-02) = 1.9901  [upward-biased by MC noise]
