# Simulating Monte Carlo with different standard deviations and interval confidence

I have a question regarding Monte Carlo simulation (direct simulation), applied to propagation of uncertainties.

From what I understand Monte Carlo accepts random numbers of each input variable of the model.

These random numbers are generated with the mean, standard deviation and type of PDF (normal, triangular, uniform, etc.)

But the question is: I have 2 input variables in my model:

x: mean=30.5 std=0.001 <- calculated with 95% confidence.
y: mean=2 std=0.1 <- calculated with 99% confidence

with z = x*y


What confidence interval will z be generated? How do I know which uncertainty (standard deviation) of z is correspondent to a 95% confidence level?

• I cannot make sense of this question for several reasons and hope you might be able to clarify them. What does it mean to "calculate" an input variable "with 95% confidence"? What do you mean by a "confidence interval" for x*y? What do you mean for a standard deviation to "correspond" to a confidence level?
– whuber
Commented Aug 30, 2013 at 15:51
• Ok. So i work with equipment's that have specifications or uncertainties. Those uncertainties are generally at 95% confidence level (some at 99%). To calculate the final uncertainty (ex: Power = Voltage*Current), i have to combine the uncertainty of the equipment generating voltage and current, so the final uncertainty of the power produced is a combination of both uncertainty of voltage and current. Generally the GUM method is applied but Monte Carlo is also accepted. Commented Aug 30, 2013 at 15:57
• The Gum strips the (expanded)uncertainties and work usually at sigma=1, only in the end we expand the uncertainty for the confidence level chosen. But here i don't know what implications i have mixing different uncertainties produced with different confidence levels. Commented Aug 30, 2013 at 16:00
• And since degrees of freedom are not calculated, i cannot expand the uncertainty. I don't really know if this is correct or not but that's why i'm here. Commented Aug 30, 2013 at 16:03
• Also. The distributions (PDF) are known. Commented Aug 30, 2013 at 16:05