Function of gaussian random variable

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The discussion centers on proving the relation E(exp(z)) = exp(E(z^2)/2) for a zero-mean Gaussian random variable z. Participants suggest looking up the definition of expectation and performing the necessary integrals to demonstrate the equality. While actual integration isn't required, the focus is on showing that the two integrals are equal. The conversation emphasizes understanding the properties of Gaussian distributions and expectations. This mathematical relationship is significant in probability theory and statistics.
Jply
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I'm having trouble showing the following relation:

E(exp(z)) = exp(E(z^2)/2)

where z is a zero-mean gaussian variable and E() is the avg

anyone can help?
 
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Expectation. Look up the definition of the expectation of f(z), where z is some random variable, and do the integral it gives you. Although you don't have to actually do the integration as such, just show the two integrals are equal.
 
Thanks a lot
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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