# r2_is n=100 nsamples=1000000 seed=101 imax=4000; mean C = 1.97992 (2-2/n = 1.98000); 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=0.25 250 0.991083 1.46e-04 1.47e-04 0.969233 1.022543 0.968719
y=0.50 500 0.909469 1.43e-04 1.58e-04 0.882497 1.030564 0.878684
y=1.00 1000 0.616813 1.19e-04 1.93e-04 0.606531 1.016952 0.585054
y=1.50 1500 0.304511 7.51e-05 2.47e-04 0.324652 0.937961 0.285963
y=2.00 2000 0.105303 3.33e-05 3.17e-04 0.135335 0.778090 0.098830
y=3.00 3000 0.003533 1.82e-06 5.16e-04 0.011109 0.318000 0.003457
x=0.5 50 1.011354 1.45e-04 1.43e-04 0.998751 1.012619 0.998747
x=1.0 100 1.009955 1.46e-04 1.45e-04 0.995012 1.015017 0.994979
x=2.0 200 1.000290 1.45e-04 1.45e-04 0.980199 1.020498 0.979933
x=4.0 400 0.949153 1.45e-04 1.53e-04 0.923116 1.028205 0.921088
x=8.0 800 0.746610 1.32e-04 1.77e-04 0.726149 1.028177 0.713081
x=16.0 1600 0.253723 6.58e-05 2.59e-04 0.278037 0.912549 0.238035
# sum_{i<=imax} E N_i = 99.000000 (n-1 = 99);  mean excursion length sum i E N_i/(n-1) = 100.0006 (n = 100; rel dev 5.53e-06)
# sum_{i>=1} |E N_i - e^{-i/n}| (i<=imax, plus e^{-i/n} tail 4.23e-16) = 2.1609  [upward-biased by MC noise]
