Can You Derive the MGF of Y=-X Using MGF of X Directly?

AI Thread Summary
The discussion revolves around deriving the moment generating function (MGF) of the random variable Y defined as Y = -X using the MGF of X, denoted as Mx(t). The participant is seeking a method to obtain My(t) directly from Mx(t) without first calculating the probability density function (pdf) of Y. They explore the relationship between the MGFs of scaled variables, specifically M_{cX}(t), and how simplifications can be made based on the functional form of X. A potential solution involves substituting a specific function into the MGF expression, indicating that the approach may depend on the characteristics of the original random variable. The conversation highlights the challenge of directly relating the MGFs of X and Y while seeking a more efficient calculation method.
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Say r.v. X, we have pdf f(x) and mgf Mx(t) defined.

Then define Y=-X, y is negative x.

Can we get mgf of Y, i.e. My(t) and how?

I know I can go the way to get pdf f(y) first then My(t). I want to know if Mx(t) is already in my hands, it should be easier to get My(t) other than do f(y) first.

I thought about this whole afternoon. Just don't get it. Please help and thanks in advance.

xoxo
 
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Remember that for any random variable

<br /> M_x(t) = E[e^{tx}]<br />

If you want the mgf for cX (any constant times X)

<br /> M_{cX}(t) = E[e^{t(cx)}]<br />

How can you simplify this expression, how does it relate to M_X(t),
and how do both observations relate to your problem?
 
I have found one way.

I have here x=log(1-x) and f(x) also known.

So we can see if I put x=log(1-x) into Mcx(t), it can be simplified easily. Here is my solution for this problem and it depends on the form of function of x.
 
How does your second post relate to your first question?
 
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