Gaussian distribution characteristic function

In summary, the conversation involved solving for the characteristic function of a normal distribution and reaching a point where a new term appeared due to "completing the square". This term had a mistake in it, causing confusion. The correct version should have a sigma squared term and a c squared term in the denominator.
  • #1
senobim
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Hello, guys. I am trying to solve for characteristic function of normal distribution and I've got to the point where some manipulation has been made with the term in integrands exponent. And a new term of t2σ2/2 has appeared. Could you be so kind and explain that to me, please.

[tex]=Ae^{it\mu}\int_{-\infty}^{\infty}e^{-\frac{1}{c^2}(\alpha^{2}-i2t\sigma ^{2}\alpha)}d\alpha=Ae^{(it\mu-\frac{t^{2}\sigma^{2}}{2})}\int_{-\infty}^{\infty}e^{-\frac{(\alpha-it\sigma^{2})^{2}}{c^{2}}} d\alpha [/tex]
 
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  • #2
What was done is called "completing the square". e.g. take ## x^2+6x ##. You divide the x coefficient by 2 and square that result to complete the square: ## x^2+6x=x^2+6x+9-9 =(x+3)^2-9 ##. ( ## (6/2)^2=9 ##). You add it and also subtract it from the expression. editing ... In the case you presented, ## \alpha=x ##. ## \\ ## Additional editing: On closer inspection, the term should have ## \sigma^4 ## and not ## \sigma^2 ##, and it should have a ## c^2 ## in the denominator instead of a 2. No wonder it puzzled you !
 
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  • #3
Nice! Thank you very much!
 
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1. What is a Gaussian distribution characteristic function?

The Gaussian distribution characteristic function is a mathematical function that fully describes the probability distribution of a Gaussian or normal random variable. It is defined as the expected value of the complex exponential function of the random variable.

2. How is the Gaussian distribution characteristic function related to the probability density function?

The Gaussian distribution characteristic function is the Fourier transform of the probability density function of a Gaussian distribution. This means that the characteristic function contains all the same information as the probability density function, but in a different form.

3. What are the properties of a Gaussian distribution characteristic function?

The Gaussian distribution characteristic function has several important properties, including being continuous, positive, and bounded. It is also infinitely differentiable and symmetric around the mean of the distribution.

4. How is the Gaussian distribution characteristic function used in statistical analysis?

The Gaussian distribution characteristic function is used in various statistical analyses to calculate probabilities and perform data transformations. It is also used in the Central Limit Theorem, which states that the sum of a large number of independent random variables will be approximately normally distributed.

5. Can the Gaussian distribution characteristic function be used for non-Gaussian distributions?

While the Gaussian distribution characteristic function is specifically defined for Gaussian distributions, it can also be used for other distributions under certain conditions. For example, the characteristic function of a sum of independent random variables can often be approximated by a Gaussian distribution, making it useful for non-Gaussian distributions as well.

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