λ = 1
Table 1. Exponential cumulative distribution.
| CDF(X) |
| X Input |
P Output |
| 0 |
0 |
| 0.5 |
0.39346934028737 |
| 1 |
0.63212055882856 |
| 1.5 |
0.77686983985157 |
| 2 |
0.86466471676339 |
| 2.5 |
0.9179150013761 |
| 3 |
0.95021293163214 |
| 3.5 |
0.96980261657768 |
Table 2. Exponential inverse cumulative distribution.
| ICDF(P) |
| P Input |
X Output |
| 0 |
0 |
| 0.39346934028737 |
0.5 |
| 0.63212055882856 |
1 |
| 0.77686983985157 |
1.5 |
| 0.86466471676339 |
2 |
| 0.9179150013761 |
2.5 |
| 0.95021293163214 |
3 |
| 0.96980261657768 |
3.5 |
Table 3. Exponential variates generated using inverse cdf method.
| RNG via ICDF(U) |
| Counter |
Rnd Vals |
| 1 |
1.3693776288225 |
| 2 |
0.52144637808107 |
| 3 |
0.47029288799416 |
| 4 |
0.58363495310885 |
| 5 |
0.50301044471886 |
| 6 |
1.6263742614731 |
| 7 |
0.081202794031811 |
| 8 |
0.63517233148915 |
Table 4. Exponential variates generated using RNG method.
| RNG via RNG(N) |
| Counter |
Rnd Vals |
| 1 |
0.15407725441241 |
| 2 |
2.1068313864131 |
| 3 |
0.65856198283296 |
| 4 |
0.91582716153622 |
| 5 |
3.5998003536231 |
| 6 |
0.44879775701359 |
| 7 |
1.5109561664815 |
| 8 |
0.35869760348962 |
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