What is the Gaussian Integral for Moments?

AI Thread Summary
The discussion focuses on calculating the mean of a probability density function (pdf) using moments, specifically for the pdf f(x|θ) = 2θ^{-2}x^3 exp(-x^2/θ). The user initially attempts to integrate by multiplying the pdf by x, but encounters incorrect results, prompting a review of integration limits to ensure f(x) > 0. A transformation is suggested using y = x^2/θ, leading to a new integral form. The importance of including the exponential term in the distribution is emphasized, as it is crucial for correctly evaluating the Gaussian integral. The conversation highlights the need for careful handling of the integral's components to achieve accurate results.
roadworx
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Hi,

I'm trying to use moments to find the mean of a pdf.

Here is the pdf:

f(x|\theta) = 2 \theta^{-2}x^3 exp(\frac{-x^2}{\theta})

I'm not really sure where to start. I can multiply the pdf by X and then integrate with respect to X, but it gives me the wrong answer.

Any ideas?

Thanks.
 
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A wrong answer is a reason to be alert. I'd check the limits of integration and make sure that they are correct in the sense that f(x) > 0 only between those limits.
 
EnumaElish said:
A wrong answer is a reason to be alert. I'd check the limits of integration and make sure that they are correct in the sense that f(x) > 0 only between those limits.

Basically this is what I've got.

\int_0^{inf} 2 \theta^{-2}x^{3+2m} dx

Using y=x^2 / \theta, if I rearrange this I get somehow:

\int_0^{inf} \theta^{m}y^{m+1} dy

Does anyone know where the final x in x^{3+2m} disappears to?
 
You've forgotten about the exponential term in your distribution function.
roadworx said:
Here is the pdf:

f(x|\theta) = 2 \theta^{-2}x^3 e^{{-x^2}/{\theta}}

I(k) = \int_0^{\infty} x^k f(x) dx = \int_0^{\infty} 2 \theta^{-2} x^{3+k} e^{{-x^2}/{\theta}} dx

This is a Gaussian integral. See this article down where it says "The general class of integrals of the form..." (equation 9).
 
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