Integrating the Normal Distribution Curve

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Discussion Overview

The discussion revolves around the integration of the normal distribution curve, specifically whether it is possible to integrate the function with generic parameters for mean (\mu) and standard deviation (\sigma), as opposed to the standard case where \mu = 0 and \sigma = 1. The scope includes both theoretical considerations and numerical methods for integration.

Discussion Character

  • Exploratory
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant questions whether the normal distribution can be integrated with generic parameters \mu and \sigma, suggesting it might be possible but expressing uncertainty.
  • Another participant proposes a substitution method to transform the integral into a standard form that can be integrated, indicating that this is likely why the standard case is more commonly seen.
  • A participant raises a question about integrating the normal distribution from specific bounds, such as -2 to 2, prompting a discussion about the feasibility of analytical versus numerical integration.
  • One participant asserts that while numerical integration is possible, analytical integration for specific bounds does not yield a simple expression, referencing the error function (Erf) as a potential but unsatisfactory solution.
  • Another participant suggests that changing the bounds of the integral could lead to an analytical answer, although this is met with skepticism regarding the existence of such an expression.

Areas of Agreement / Disagreement

Participants express differing views on the possibility of analytical integration for specific bounds and the use of numerical methods. There is no consensus on whether an analytical solution exists for the integral from -2 to 2.

Contextual Notes

Participants mention the use of substitutions and transformations in integration, but the discussion remains unresolved regarding the analytical capabilities for bounds other than the standard case.

Brad_Ad23
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Given

[tex]P(x)= \frac{1}{\sigma \sqrt{2\pi}} e ^ \frac { -(x - \mu )^2}{2 \sigma ^2 }[/tex]

This is of course the normal distribution curve. When [tex]\mu = 0[/tex] and [tex]\sigma = 1[/tex] I can integrate this from minus infinity to positive infinity no problem using polar coordinates and a bit of multivariable calculus. The question I have, is, is it at all possible to do this if one leaves [tex]\mu[/tex] and [tex]\sigma[/tex] in as generic parameters? I would think so, but I'm not sure. No need to give a worked through example, just, is it possible at all to fit it to some form? Or is it just that with those parameters set to 0 and 1 that this is an integrable function?
 
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Certainly. Just do a little creative substitution:
Let [tex]u= \frac{x-\mu}{\sqrt(2)\sigma^2}[/tex].

Then [tex]du= \frac{1}{\sqrt(2)\sigma^2}dx[/tex]

[tex]P(x)= \frac{1}{\sigma \sqrt{2\pi}} e ^ \frac { -(x - \mu )^2}{2 \sigma ^2 }[/tex]

becomes [tex]P(u)= \frac{1}{\sqrt{\pi}} e ^{-u^2}[/tex]

which you can integrate from negative to positive infinity as usual.
(That's why you probably have only seen that case.)
 
Yep.
 
can it be integrated from a number to anohter... say -2 and 2?
 
Numerically, yes.
Analytically, no (unless you count functions like Erf(x), which is cheating in my view).
 
Well wait a minute. Wouldn't you just change the bounds on the integral with respect to radius from 0 to infinity to 0 to 2? The theta integral would remain the same, and thus you'd get the analytical answer?
 
Maybe I misunderstood allergic's question. I thought that he wanted to know

[tex]\int_0^2 \frac{1}{\sqrt{\pi}} e ^{-u^2} \,{\rm d}u[/tex]

I don't think there is an analytical expression for that. Numerically it's easy (0.497661).
 

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