Why this relation is true when computing the Gaussian integral?

In summary, the conversation discusses the possibility of applying the same method used in the Gaussian integral to other functions, and the idea of moving multiplicative factors in and out of integrals. The key is understanding calculus well, and the calculation as a whole is considered ingenious. It is also noted that this approach may not always work for improper integrals.
  • #1
Arian.D
101
0
[tex] \int_0^\infty e^{-x^2}dx \int_0^\infty e^{-y^2}dy = \int_0^\infty \int_0^\infty e^{-(x^2+y^2)} dxdy[/tex]

Under what conditions we could do the same for other functions? I don't get how Poisson (or Euler, or Gauss, whoever that did this for the first time) realized that this is true. It looks quite ingenious to me and I wonder if there's a theorem or something that I could apply to similar cases and this case as well.
 
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  • #2
You can move any multiplicative factor that doesn't depend on the integration variable into or out of the relevant integral.

Hint: [itex]e^{-(x^2+y^2)} = e^{-x^2} e^{-y^2}[/itex].
 
  • #3
That's not ingenious. That's just knowing calculus well. What's somewhat ingenious is the calculation as a whole. That step isn't really the trick. The trick is doing that change of variables at the beginning.
 
  • #4
Muphrid said:
You can move any multiplicative factor that doesn't depend on the integration variable into or out of the relevant integral.

Hint: [itex]e^{-(x^2+y^2)} = e^{-x^2} e^{-y^2}[/itex].

I don't understand, would you care to explain what you mean by this?
I'm sure you don't mean that we could move [itex]e^{_y^2}[/itex] into the integral with respect to x, do you? so if I have:
[tex] I = \int_a^b f(x)dx . \int_a^b g(y)dy [/tex]
Could I conclude that:
[tex] I = \int_a^b \int_a^b f(x).g(y) dxdy [/tex]

Why so? This doesn't look obvious to me at all :-(

homeomorphic said:
That's not ingenious. That's just knowing calculus well. What's somewhat ingenious is the calculation as a whole. That step isn't really the trick. The trick is doing that change of variables at the beginning.
Yea, I meant the whole calculation process of the Gaussian integral is ingenious, not particularly this step. I liked the proof so much, I also found another proof due to Laplace on wikipedia that was beautiful as well.
 
  • #5
Arian.D said:
I don't understand, would you care to explain what you mean by this?
I'm sure you don't mean that we could move [itex]e^{_y^2}[/itex] into the integral with respect to x, do you? so if I have:
[tex] I = \int_a^b f(x)dx . \int_a^b g(y)dy [/tex]
Could I conclude that:
[tex] I = \int_a^b \int_a^b f(x).g(y) dxdy [/tex]

Why so? This doesn't look obvious to me at all :-(

As far as the x integral is concerned, g(y) is a constant factor that you can move into it or pull out of it as easily as you could with a true constant like, say, the number 5.
 
  • #6
Muphrid said:
As far as the x integral is concerned, g(y) is a constant factor that you can move into it or pull out of it as easily as you could with a true constant like, say, the number 5.

yes, but you can't move dx into the second integral for sure. The problem is not how g(y) goes in, the problem is how the double integral appears. That's where I found it not obvious enough to be taken for granted intuitively. I'd be thankful if you care to explain it in details or show me a proof of this.
 
  • #7
Let [itex]\int_a^b f(x) \; dx = F[/itex], which is just a number with no y-dependence.

[tex]I = F \int_a^b g(y) \; dy = \int_a^b F g(y) \; dy = \int_a^b \left(\int_a^b f(x) \; dx \right) g(y) \; dy[/tex]

From there, g(y) can be moved into the x-integral exactly as I described.
 
  • #8
Muphrid said:
Let [itex]\int_a^b f(x) \; dx = F[/itex], which is just a number with no y-dependence.

[tex]I = F \int_a^b g(y) \; dy = \int_a^b F g(y) \; dy = \int_a^b \left(\int_a^b f(x) \; dx \right) g(y) \; dy[/tex]

From there, g(y) can be moved into the x-integral exactly as I described.

Nice. Thanks. Sounds convincing.
But we can do this if the integrals are indefinite, right? because the anti-derivative of f(x) is still a function of x and is independent of y?

Also, if the integrals are improper, then we need f or g (or maybe both) to have some nice properties, because then we'll have to move a limit into an integral which isn't always true. Right? or we could easily generalize the same argument to improper integrals as well?
 
  • #9
As long as neither y nor x appear in the limits of the integrals, this approach ought to be valid.
 

Related to Why this relation is true when computing the Gaussian integral?

1. Why is the Gaussian integral important in mathematics and science?

The Gaussian integral is important because it is a fundamental tool in probability theory and statistics. It is used to calculate the area under a normal distribution curve, which is essential for understanding and analyzing data in various fields such as physics, economics, and psychology. It also has applications in signal processing, image processing, and engineering.

2. How do you compute the Gaussian integral?

The Gaussian integral can be computed using various methods such as the substitution method, integration by parts, or the use of complex numbers. The most common method is the substitution method, which involves transforming the integral into a simpler form by substituting variables. This allows for easier integration and solution of the integral.

3. What is the significance of the Gaussian distribution in the Gaussian integral?

The Gaussian distribution, also known as the normal distribution, is a probability distribution that is commonly found in nature and in many real-life phenomena. It is characterized by a bell-shaped curve and is defined by two parameters, the mean and the standard deviation. The Gaussian integral is used to calculate the area under this curve, providing valuable insights into the behavior and characteristics of a given system or data set.

4. Can the Gaussian integral be solved analytically?

Yes, the Gaussian integral can be solved analytically using various methods as mentioned earlier. However, there are some cases where the integral cannot be solved analytically and requires numerical methods for approximation. These methods involve dividing the integral into smaller parts and using numerical techniques such as the trapezoidal rule or Simpson's rule to approximate the area under the curve.

5. What are the practical applications of the Gaussian integral?

The Gaussian integral has numerous practical applications in mathematics, science, and engineering. It is used in statistics to determine the probability of a given event occurring, in physics to calculate the force of a particle in a potential field, and in signal processing to filter out noise from a signal. It also has applications in finance, biology, and computer science, making it a versatile and important tool in various fields of study.

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