Computing for any general function whose variable is a gaussian

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When computing a function f(X) where X follows a Gaussian distribution, the resulting distribution is generally not Gaussian. For instance, if f(x) = x², the output is a chi-squared distribution, which is always positive and not Gaussian. The expected value of f(z) for a Gaussian random variable can be expressed as E(f(z)) = (1/√(2π)) ∫ f(z)e^(-z²/2) dz, correcting the initial omission of the 1/√(2π) factor. This expression accurately represents the expected value but does not indicate the distribution of f(X). Overall, transformations of Gaussian variables often lead to non-Gaussian distributions.
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If I have a variable X whose gaussian distribution is known and let f be a known function, is there a way to compute f(X) (i.e) the resulting gaussian distribution from this? Is the result actually a gaussian distribution?
 
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It won't be a gaussian distribution in general. For example if f(x) = x2 then you get what's called the chi-squared distribution with one degree of freedom (k degrees of freedom is adding the square of k gaussians). These are not gaussian (in fact it always has to give a positive number)
 
Office_Shredder said:
It won't be a gaussian distribution in general. For example if f(x) = x2 then you get what's called the chi-squared distribution with one degree of freedom (k degrees of freedom is adding the square of k gaussians). These are not gaussian (in fact it always has to give a positive number)

E(f(z))= \int_{-\inf}^{\inf}{f(z)e^{-\frac{z^2}{2}}}dz

What about this above relation? Found it somewhere and it said this is for finding the expected value of f(z) when z is a random variable with gaussian distro.
 
That's correct except there should be a 1/sqrt(2pi) in there. In general if you have a probability density function p(x) for a random variable X, then
E(f(X)) = \int_{-\infty}^{\infty} f(x) p(x) dx
In this case your p(x) is the Gaussian density.
 
Office_Shredder said:
That's correct except there should be a 1/sqrt(2pi) in there. In general if you have a probability density function p(x) for a random variable X, then
E(f(X)) = \int_{-\infty}^{\infty} f(x) p(x) dx
In this case your p(x) is the Gaussian density.

If you don't mind, can you write down the correct expression with the pi?
 
Office_Shredder said:
That's correct except there should be a 1/sqrt(2pi) in there. In general if you have a probability density function p(x) for a random variable X, then
E(f(X)) = \int_{-\infty}^{\infty} f(x) p(x) dx
In this case your p(x) is the Gaussian density.

E(f(z))= \frac{1}{sqrt(2\pi)}\int_{-\inf}^{\inf}{f(z)e^{-\frac{z^2}{2}}}dz


Is this the right expression?

Mod note: Fixed it for you. The LaTeX for infinity is \infty, not \inf. And for the square root, it's \sqrt
$$E(f(z))= \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}{f(z)e^{-\frac{z^2}{2}}}dz $$
 
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That looks correct. That doesn't say anything about what the distribution of f is, all you are being told is what the expected value is
 

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