# r2_is n=1600 nsamples=50000 seed=1601 imax=192000; mean C = 2.00346 (2-2/n = 1.99875); 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 16000 0.972504 1.71e-04 1.76e-04 0.969233 1.003375 0.969106
y=0.50 32000 0.887263 1.69e-04 1.90e-04 0.882497 1.005401 0.881569
y=1.00 64000 0.608989 1.23e-04 2.02e-04 0.606531 1.004054 0.601401
y=1.50 96000 0.321070 8.05e-05 2.51e-04 0.324652 0.988965 0.315392
y=2.00 128000 0.129248 3.74e-05 2.89e-04 0.135335 0.955021 0.126278
y=3.00 192000 0.009042 3.57e-06 3.94e-04 0.011109 0.813900 0.008752
x=0.5 800 1.000835 1.50e-04 1.50e-04 0.999922 1.000913 0.999922
x=1.0 1600 1.000803 1.47e-04 1.47e-04 0.999688 1.001116 0.999687
x=2.0 3200 0.999797 1.71e-04 1.71e-04 0.998751 1.001047 0.998750
x=4.0 6400 0.996929 1.55e-04 1.56e-04 0.995012 1.001926 0.995004
x=8.0 12800 0.983148 1.61e-04 1.64e-04 0.980199 1.003009 0.980133
x=16.0 25600 0.927876 1.49e-04 1.60e-04 0.923116 1.005156 0.922620
# sum_{i<=imax} E N_i = 1599.000000 (n-1 = 1599);  mean excursion length sum i E N_i/(n-1) = 1600.0027 (n = 1600; rel dev 1.70e-06)
# sum_{i>=1} |E N_i - e^{-i/n}| (i<=imax, plus e^{-i/n} tail 1.23e-49) = 2.1811  [upward-biased by MC noise]
