Is there a formula for this gaussian integral

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Discussion Overview

The discussion revolves around finding a formula for the Gaussian integral of the form $$\int_{-\infty}^{\infty}{x^4}{e^{-a(x-b)^2}}dx$$. Participants explore various approaches to derive or confirm the integral's value, touching on related concepts in probability and moments of the normal distribution.

Discussion Character

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

Main Points Raised

  • One participant inquires about the formula for the integral, noting difficulty in finding it in common resources.
  • Another participant presents a derivative approach based on the known Gaussian integral $$\int_{-\infty}^\infty e^{-bx^2}dx=\sqrt{\frac{\pi}{b}}$$, suggesting it may apply to the integral in question.
  • A different participant challenges the applicability of the derivative method for this specific integral.
  • One participant asserts that the integral relates to moments of the normal distribution, providing a formula for even moments and suggesting that it leads to $$\frac{3\sqrt{\pi}}{4 a^{5/2}}$$ for the case of $$p=4$$.
  • Another participant expresses skepticism about the correctness of the previous claims, referencing known results for lower moments and noting a pattern that remains unclear for the fourth moment.
  • A participant acknowledges confusion regarding the central and non-central moments, attempting to clarify the correct expressions for moments up to the fourth order.
  • One participant expresses gratitude for a derived formula that resolves their query after significant effort.
  • Another participant suggests an integration by parts method to approach the integral, noting that it simplifies the polynomial degree.
  • A different approach is proposed using the characteristic function of a Gaussian distribution, which can yield moments through expansion and matching terms.

Areas of Agreement / Disagreement

The discussion features multiple competing views and approaches to the integral, with no clear consensus on the correct method or formula. Participants express differing opinions on the validity of certain approaches and results.

Contextual Notes

Participants reference various mathematical techniques and properties of Gaussian integrals, but there are unresolved aspects regarding the application of these methods to the specific integral in question. Some assumptions about the parameters and their roles in the integrals are not fully explored.

skate_nerd
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Is there a formula for this gaussian integral:

$$int_{-\infty}^{\infty}{x^4}{e^{-a(x-b)^2}}dx$$

I've tried wikipedia and they only have formulas for the integrand with only x*e^... not x^4e^...
Wolframalpha won't do it either, because I actually have an integral that looks just like that, unknown constants and all.
 
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Well, we know that:

$$\int_{-\infty}^\infty e^{-bx^2}dx=\sqrt{\frac{\pi}{b}}$$

Taking some derivatives:

$$\frac{d^2}{db^2}\int_{-\infty}^\infty e^{-bx^2}dx=\int_{-\infty}^{\infty}x^4e^{-bx^2}dx=\frac{3}{4}\frac{\sqrt{\pi}}{b^{5/2}}$$

I'm not sure if you can use this for your integral, but it looks possible.
 
Sorry, but you can't :/
 
Matterwave's answer is correct!
 
skate_nerd said:
Is there a formula for this gaussian integral:

$$int_{-\infty}^{\infty}{x^4}{e^{-a(x-b)^2}}dx$$

You are basically just asking for the moments of the normal distribution. These are well known:
\int_{-\infty}^{\infty} \frac{x^p}{\sigma \sqrt{2\pi}} e^{-(x-\mu)^2 / (2 \sigma^2)} dx= \sigma^p (p-1)!
when p is even, and zero when p is odd.

So ##p=4##, ##\sigma = \frac{1}{\sqrt{2a}}## and ##\mu = b##, which gives ##\frac{3\sqrt{\pi}}{4 a^{5/2}}## as Matterwave gave.
 
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I'm sorry i just don't think that's correct. Try wolframalpha. I know the formulas for the x, x^2, x^3 cases, but not the x^4. But everything up to x^3 agrees with wolframalpha's output, and this formula you guys are giving doesn't look very similar and does not agree.
$$\int_{-\infty}^{\infty}{x}e^{-a(x-b)^2}dx=b\sqrt{\frac{\pi}{a}}$$
$$\int_{-\infty}^{\infty}{x^2}e^{-a(x-b)^2}dx=(\frac{1}{2a}+b^2)\sqrt{\frac{\pi}{a}}$$
$$\int_{-\infty}^{\infty}{x^3}e^{-a(x-b)^2}dx=(\frac{3b}{2a}+b^3)\sqrt{\frac{\pi}{a}}$$
There's a pattern here but I can't figure it out.
 
skate_nerd said:
I'm sorry i just don't think that's correct.

I'm an idiot, I looked up the central moments (and even that's wrong because it should be ##(p-1)!##). The non-central moments are

##\mu##
##\mu^2+\sigma^2##
##\mu^3+3\mu\sigma^2##
##\mu^4+6\mu^2\sigma^2+3\sigma^4##,
for p = 1, 2, 3, and 4. Those should agree with what you have.
 
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Ahhh thank you thank you thank you! That works 100%! Man I've been trying to get this formula for a week...I really appreciate it.
 
The "freshman calculus" way to do this is to integrate by parts:
u = x^3 \qquad dv = xe^{-x^2}\,dxso that
du = 3x^2 \qquad v = -{1 \over 2}e^{-x^2}and now the degree of the polynomial has been reduced by 2.

Now, that is for the case where a and b are 0, but the other cases are really the same. You just have to change variables; then you will have a more complicated polynomial in place of x^4, but once you know how to do \int x^{2k}e^{-x^2} you can integrate any polynomial times e^{-x^2}.
 
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Another clever way to do this integral is to look at the characteristic function for a Gaussian distribution:

$$C(t)\equiv\left<e^{itx}\right>=\frac{1}{\sigma\sqrt{2\pi}} \int_{-\infty}^{\infty}e^{itx}e^{-(x-\mu)^2/2\sigma^2}$$

You can do this integral by completing the square in the exponential, or by just looking it up. You will find:

$$C(t)=e^{it\mu-\frac{1}{2}t^2\sigma^2}$$

You can expand this function in powers of t, and match it to the expansion of:

$$\left<e^{itx}\right>\approx 1+\left<itx\right>+\frac{\left<(itx)^2\right>}{2}+...$$

If you match the powers of t on both sides, you can get all the general moments of the Gaussian distribution. You can get all the odd ones too, for which the formula I gave earlier would no longer work. This Characteristic function is closely related to the moment generating function, but I'm more familiar with working with these.
 

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