scothoward
Nov19-08, 03:41 PM
1. The problem statement, all variables and given/known data
Hi,
There is one theorm in my Applied Probability course that I am having trouble understanding. It has to do with how to derive various types of random variables from the transformation X= g(U)
It says, Let U be a uniform (0,1) random variable and let F(x) denote a cumulative distribution function with an inverse F^-1(u) defined for 0<u<1. The random variable X = F^-1(U) has a CDF FX(x) = F(x).
I think my lack of understand of what F^-1(u) is hindering my understanding of this theorm. Would you be able to explain?
2. Relevant equations
X= g(U)
3. The attempt at a solution
From what I understand and the examples looked at, this theorm allows us
derive random variables of different types, using the uniform random
variable.
Hi,
There is one theorm in my Applied Probability course that I am having trouble understanding. It has to do with how to derive various types of random variables from the transformation X= g(U)
It says, Let U be a uniform (0,1) random variable and let F(x) denote a cumulative distribution function with an inverse F^-1(u) defined for 0<u<1. The random variable X = F^-1(U) has a CDF FX(x) = F(x).
I think my lack of understand of what F^-1(u) is hindering my understanding of this theorm. Would you be able to explain?
2. Relevant equations
X= g(U)
3. The attempt at a solution
From what I understand and the examples looked at, this theorm allows us
derive random variables of different types, using the uniform random
variable.