Fitting a gaussian distribution when given the mean and standard deviation

_what functions do we use if we want to fit a gaussian when given standard deviation and mean on pyroot?

Hi @SarahHamad, welcome to the ROOT forum!

When the standard deviation and the mean is given, what parameters are left for you to fit anyway?

Or did you mean fitting a Gaussian to determine the a priori unknown standard deviation and mean? In this case, you can get inspiration from the rf101_basics tutorial.

I hope this helps!

Thank you so much ! And I apologize for using the wrong term, I want to generate random numbers to draw the gaussian, so do I only define the gaussian function and generate variables to draw it ? Basically, I am working on this problem as a tutorial to get familiarized with root:
The “gestation period” for humans averages 40 weeks, with a standard deviation of 10 days. Using CERN’s root package, calculate and plot the probability that a woman will give birth tomorrow, given that she has made it all the way to today without giving birth yet, as a function of
the day. You can start about two weeks before the due date, and go until two weeks afterward.

I thought of modeling the problem before the mean as a gaussian probability distribution, but after the mean it can’t be as such as the probability of giving birth will not decrease. So, another way is trying an iterate method to get the probability of today as function of probability of yesterday. Is this a good way to think around the problem? And us the process on root different from other languages ?

Thank you so much for your help as I am new to this and my question might be really preliminary.