Computing for any general function whose variable is a gaussian

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Discussion Overview

The discussion centers on the computation of a function applied to a variable with a Gaussian distribution, specifically whether the resulting distribution of the function is also Gaussian. Participants explore the implications of different functions and the expected value of such transformations.

Discussion Character

  • Exploratory, Technical explanation, Debate/contested, Mathematical reasoning

Main Points Raised

  • One participant questions if applying a known function f to a Gaussian variable X results in a Gaussian distribution.
  • Another participant argues that the result will not generally be Gaussian, providing the example of f(x) = x² leading to a chi-squared distribution.
  • A later reply introduces an integral expression for the expected value of f(z) when z is a Gaussian random variable, but notes a correction is needed regarding the normalization factor.
  • Several participants discuss the correct form of the expected value expression, with one confirming the inclusion of the normalization factor 1/sqrt(2π).
  • One participant emphasizes that the expected value expression does not indicate the distribution of f(X), only its expected value.

Areas of Agreement / Disagreement

Participants generally agree on the need for a normalization factor in the expected value expression, but there remains disagreement on whether the resulting distribution of f(X) is Gaussian, with multiple competing views presented.

Contextual Notes

There are unresolved aspects regarding the specific conditions under which the distribution of f(X) may or may not be Gaussian, depending on the nature of the function f.

RRraskolnikov
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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
$$E(f(z))= \frac{1}{\sqrt{2\pi}}\int_{-\infty}^{\infty}{f(z)e^{-\frac{z^2}{2}}}dz $$
 
Last edited by a moderator:
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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