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Meaning of Inverse CDF

  1. Nov 19, 2008 #1
    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.
     
  2. jcsd
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