Integrating the Normal Distribution: Uncovering Gauss' Method

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Discussion Overview

The discussion revolves around the integration of the function related to the normal distribution, specifically exploring Gauss' method for obtaining values of the normal distribution and the challenges associated with finding closed-form solutions.

Discussion Character

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

Main Points Raised

  • Some participants describe Gauss' integration method using a sequence of steps involving the integral of e^{-x^2} and the application of Fubini's theorem.
  • Others argue that the integration of the normal distribution function cannot be expressed in closed form and suggest that numerical integration is used to obtain values in Gaussian distribution tables.
  • A participant expresses appreciation for the integration techniques presented, indicating a positive reception to the mathematical tricks shared.
  • One participant inquires about the integral from 0 to a specific value, suggesting a need for further clarification on the limits of integration.
  • Another participant mentions the Error function, Erf(x), as the anti-derivative of e^{-x^2/2}, emphasizing its relevance in numerical integration.
  • There is a note that one should not take values from the Error function table at face value without proper context.
  • A participant highlights the necessity of using specific parameters (mu and sigma) to convert to the standard z variable for calculations.

Areas of Agreement / Disagreement

Participants express differing views on the feasibility of finding a closed-form solution for the integral related to the normal distribution, with some supporting the numerical integration approach while others focus on the theoretical aspects of Gauss' method. The discussion remains unresolved regarding the best approach to integrate the function.

Contextual Notes

There are limitations in the discussion regarding assumptions about the applicability of numerical methods versus analytical techniques, as well as the dependence on definitions of the Error function and its relation to the normal distribution.

alba_ei
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how did gauss integrate this function
to obtain the values of the normal distribution?:confused:
 

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By a sequence of clever tricks:

1. Let us consider the integral
[tex]I=\int_{-\infty}^{\infty}e^{-x^{2}}dx[/tex]
Now, since a dummy variable's name is irrelevant, we may write:
[tex]I^{2}=(\int_{-\infty}^{\infty}e^{-x^{2}}dx)^{2}=(\int_{-\infty}^{\infty}e^{-x^{2}}dx)(\int_{-\infty}^{\infty}e^{-x^{2}}dx)=(\int_{-\infty}^{\infty}e^{-x^{2}}dx)(\int_{-\infty}^{\infty}e^{-y^{2}}dy)[/tex]
2. By Fubini's theorem, we have:
[tex]\int_{-\infty}^{\infty}e^{-x^{2}}dx\int_{-\infty}^{\infty}e^{-y^{2}}dy=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}e^{-x^{2}}e^{-y^{2}}dxdy=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}e^{-(x^{2}+y^{2})}dxdy[/tex]
3. Switching to polar coordinates, [itex]x=r\cos\theta,y=r\sin\theta,0\leq{r}\leq\infty,0\leq\theta\leq{2\pi}[/itex], we get:
[tex]\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}e^{-(x^{2}+y^{2})}dxdy=\int_{0}^{\infty}\int_{0}^{2\pi}re^{-r^{2}}d\theta{d}r[/tex]
which is easily integrated to the value [itex]\pi[/tex]<br /> <br /> 4. Hence, we have [itex]I^{2}=\pi\to{I}=\sqrt{\pi}[/itex][/itex]
 
Last edited:
But since the question was
alba_ei said:
how did gauss integrate this function
to obtain the values of the normal distribution?:confused:
The answer is "numerically". In general it is impossible to find a closed form (elementary) anti-derivative but one certainly can do a numerical integration- that's how the values in a "Gaussian distribution" table are calculated.
 
Blarrgh, you read it weller than me!
I LIKE those tricks..:frown: :cry:
 
arildno said:
By a sequence of clever tricks:

1. Let us consider the integral
[tex]I=\int_{-\infty}^{\infty}e^{-x^{2}}dx[/tex]
Now, since a dummy variable's name is irrelevant, we may write:
[tex]I^{2}=(\int_{-\infty}^{\infty}e^{-x^{2}}dx)^{2}=(\int_{-\infty}^{\infty}e^{-x^{2}}dx)(\int_{-\infty}^{\infty}e^{-x^{2}}dx)=(\int_{-\infty}^{\infty}e^{-x^{2}}dx)(\int_{-\infty}^{\infty}e^{-y^{2}}dy)[/tex]
2. By Fubini's theorem, we have:
[tex]\int_{-\infty}^{\infty}e^{-x^{2}}dx\int_{-\infty}^{\infty}e^{-y^{2}}dy=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}e^{-x^{2}}e^{-y^{2}}dxdy=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}e^{-(x^{2}+y^{2})}dxdy[/tex]
3. Switching to polar coordinates, [itex]x=r\cos\theta,y=r\sin\theta,0\leq{r}\leq\infty,0\leq\theta\leq{2\pi}[/itex], we get:
[tex]\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}e^{-(x^{2}+y^{2})}dxdy=\int_{0}^{\infty}\int_{0}^{2\pi}re^{-r^{2}}d\theta{d}r[/tex]
which is easily integrated to the value [itex]\pi[/tex]<br /> <br /> 4. Hence, we have [itex]I^{2}=\pi\to{I}=\sqrt{\pi}[/itex][/itex]
[itex]<img src="https://cdn.jsdelivr.net/joypixels/assets/8.0/png/unicode/64/1f60e.png" class="smilie smilie--emoji" loading="lazy" width="64" height="64" alt=":cool:" title="Cool :cool:" data-smilie="6"data-shortname=":cool:" /> <br /> arildno,you are a mean green integration machine <img src="https://www.physicsforums.com/styles/physicsforums/xenforo/smilies/oldschool/approve.gif" class="smilie" loading="lazy" alt=":approve:" title="Approve :approve:" data-shortname=":approve:" />[/itex]
 
Last edited:
But what's happened if I want to know the integral from 0 to x. specifically from 0 to 2
 
then you should read HallsofIvy's post once more.
 
You could look it up in a table of the "Error function", Erf(x). That is the "anti-derivative" of [itex]e^{-x^2/2}[/itex] and is derived, just as I said, by numerical integration.
 
  • #10
If you encounter a table of values for the Error function, don't quickly take that as your value for the integral.

[tex]Erf(x)=\frac{2}{\sqrt{\pi}}\int_0^x e^{-t^2} dt[/tex]
 
  • #11
Right. You will need to use the given values of [itex]\mu[/itex] and [itex]\sigma[/itex] to convert to the "standard z" variable.
 

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