eldo_user

Monte Carlo Analysis
Primary Statistics of Uncertainty Analysis
The value of this upper bound in the above example is:
Importance Sampling Monte Carlo efficiently handles these cases of low failure probabilities.
Quantile Estimators using Standard Monte Carlo
The first MCBOUND extracts are modified as follows:
.EXTRACT MC LABEL=quantile_fosc_left_1em4 MCBOUND(fosc, 1e-1)
.EXTRACT MC LABEL=quantile_phn_right_1em4 MCBOUND(phase_noise_1meg,1.0 -
1e-1)
The target probabilities in the left tail of the frequency FOSC and the right tail of the phase
noise are shifted to the “normal events” domain. These specifications can be simulated more
easily using the standard Monte Carlo method. Running Eldo with SAMPLING=RAND and
5000 runs produces a .mcm file containing the following data:
Tail Probability Lower Quantile Upper Prec(%) Name
(Goal Value) Value of Meas
--------------------------------------------------------------------------------
Left 1.000e-01 1.80857e+09 1.80963e+09 1.81042e+09 0.10% FOSC
Right 9.000e-01 -1.17933e+02 -1.17911e+02 -1.17887e+02 0.04% PHASE_N
The lower and upper bounds are obtained by inverting the confidence interval:
where π is the target probability, and where h is the half-range of the confidence interval:
A theoretical result ensures that a central limit theorem exists for quantiles.
Probability and Quantiles using Importance Sampling Monte Carlo
Running Eldo with SAMPLING=ISMC and 500 runs for each batch produces a .mcm file
containing the following data:
Eldo® User's Manual, 15.3 483