# r2_is n=6 nsamples=20000000 seed=98 imax=30; mean C = 1.66661 (2-2/n = 1.66667); walks absorbed before imax: 20000000
# 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 4 1.217153 1.96e-04 1.61e-04 0.969233 1.263078 0.960759
y=0.50 7 1.226192 2.27e-04 1.85e-04 0.882497 1.373469 0.877506
y=1.00 15 0.676467 3.72e-04 5.49e-04 0.606531 1.138786 0.479995
y=1.50 22 0.071406 8.36e-04 1.17e-02 0.324652 0.218930 0.135321
y=2.00 29 0.000000 0.00e+00 nan 0.135335 0.000000 0.006790
x=0.5 3 1.202236 1.84e-04 1.53e-04 0.979382 1.227545 0.978173
x=1.0 6 1.242627 2.17e-04 1.74e-04 0.920044 1.350616 0.910347
x=2.0 12 0.994406 3.16e-04 3.18e-04 0.716531 1.387806 0.648696
x=4.0 24 0.000000 0.00e+00 nan 0.263597 0.000000 0.074895
# sum_{i<=imax} E N_i = 5.000000 (n-1 = 5);  mean excursion length sum i E N_i/(n-1) = 5.9999 (n = 6; rel dev -1.71e-05)
# sum_{i>=1} |E N_i - e^{-i/n}| (i<=imax, plus e^{-i/n} tail 3.72e-02) = 1.9986  [upward-biased by MC noise]
