Why is the Gaussian function easier to integrate using polar coordinates?

In summary, the conversation discusses the evaluation of equations 17 and 18, which involves using the error function and a substitution of variables. The first integral can be evaluated without the error function by using the fact that the integral is equal to itself when the integration variable is changed. The second integral is easier to evaluate by switching to polar coordinates.
  • #1
g.lemaitre
267
2

Homework Statement



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Homework Equations


The Attempt at a Solution



Looking at equations 17 and 18, I don't see how that follows. If you substitute infinity for x you're going to get infinity divided by some real number which is infinity
 
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  • #2
Hey g.lemaitre.

The first result is not intuitive and it is based on what is calle the error function or erf(x). The function does converge because e^(-x) when x gets really large goes quickly to 0.

http://en.wikipedia.org/wiki/Error_function

For the second one, you need to use a substitution of u = x^2. If you have done a year of calculus, this should be straight-forward.
 
  • #3
g.lemaitre said:
Looking at equations 17 and 18, I don't see how that follows. If you substitute infinity for x you're going to get infinity divided by some real number which is infinity

[itex]e^{- \infty}=0[/itex]

chiro said:
Hey g.lemaitre.

The first result is not intuitive and it is based on what is called the error function or erf(x). The function does converge because e^(-x) when x gets really large goes quickly to 0.

There is no need for the error function when evaluating the first integral, just use the fact that [itex]\int_{ - \infty }^{ \infty } f(x)dx = \int_{ - \infty }^{ \infty } f(y)dy [/itex] to calculate the square of the integral, by switching to polar coordinates.
 
  • #4
Do you mean ydx and xdy instead of f(x)dx f(y)dy? Sorry to nitpick but changing x to y is change a dummy variable change rather than a variable description change.
 
  • #5
chiro said:
Do you mean ydx and xdy instead of f(x)dx f(y)dy? Sorry to nitpick but changing x to y is change a dummy variable change rather than a variable description change.

The whole point is to exploit a change of the "dummy" integration variable, to transform the problem from one of finding a one-dimensional integral, to one of finding a two-dimensional integral, as the latter turns out to be easier:

[tex] \left( \int_{-\infty}^{\infty} f(x)dx \right)^2 = \left( \int_{-\infty}^{\infty} f(x)dx \right)\left( \int_{-\infty}^{\infty} f(y)dy \right) = \int_{-\infty}^{\infty} \int_{-\infty}^{\infty} f(x)f(y)dxdy[/tex]

To see why it's easier, just switch to polar coordinates.
 
  • #6
gabbagabbahey said:
The whole point is to exploit a change of the "dummy" integration variable, to transform the problem from one of finding a one-dimensional integral, to one of finding a two-dimensional integral, as the latter turns out to be easier:

[tex] \left( \int_{-\infty}^{\infty} f(x)dx \right)^2 = \left( \int_{-\infty}^{\infty} f(x)dx \right)\left( \int_{-\infty}^{\infty} f(y)dy \right) = \int_{-\infty}^{\infty} \int_{-\infty}^{\infty} f(x)f(y)dxdy[/tex]

To see why it's easier, just switch to polar coordinates.

That makes it a lot clearer. Thanks.
 

1. What is a Gaussian function?

A Gaussian function, also known as a normal distribution, is a type of mathematical function that is commonly used in statistics to represent a probability distribution. It has a characteristic bell-shaped curve and is often used to model natural phenomena in fields such as physics, chemistry, and biology.

2. How is a Gaussian function defined?

A Gaussian function is defined by the equation y = ae-(x-b)^2/2c^2, where a is the amplitude, b is the mean, and c is the standard deviation. The parameter a controls the height of the curve, while b and c determine its position and width, respectively.

3. What are the properties of a Gaussian function?

Some key properties of a Gaussian function include symmetry about the mean, a single peak at the mean, and tails that extend infinitely in both directions. It is also a continuous and smooth function, and its integral over the entire real line is equal to 1.

4. How is a Gaussian function used in data analysis?

Gaussian functions are commonly used in data analysis for curve fitting, smoothing, and pattern recognition. They are also used in hypothesis testing and to calculate confidence intervals. In addition, many statistical tests and models assume that the underlying data follows a Gaussian distribution.

5. What are some real-life examples of Gaussian functions?

Some common real-life examples of Gaussian functions include the distribution of human height, the distribution of IQ scores, and the distribution of errors in measurements. They can also be used to model natural phenomena such as the intensity of earthquakes, the speed of gas particles, and the concentration of chemical substances in a solution.

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