How can the error function be used to solve this integral?

In summary, The conversation is about a computer scientist seeking help with solving an integral involving a Gaussian function and a given function. They discuss using the error function and integration by parts to solve the integral.
  • #1
astralmeme
2
0
Greetings,

I am a computer scientist revisiting integration after a long time. I am stuck with this simple-looking integral that's turning out to be quite painful (to me). I was wondering if one of you could help.

The goal is to solve the integral

[tex]
\int_{0}^{\infty} e^{-(x - t)^2/2 \sigma^2} x^n\ dx .
[/tex]

Note that this is the convolution of the Gaussian centered around 0 with the function that equals $x^n$ for $x > 0$, and 0 elsewhere (modulo scaling).

In particular, I would be interested in seeing any relationship with the integral

[tex]
\int_{-\infty}^{\infty} e^{-(x - t)^2/2 \sigma^2} x^n\ dx .
[/tex]

which I have worked out.

Any suggestions?

Thanks in advance,
Swar
 
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  • #2
If n is odd, that can be done by letting [itex]u= -(x-\mu)^2/(2\sigma^2)[/itex]. If x is even, try integration by parts, letting [itex]u= x^{n-1}[/itex], [itex]dv= xe^{-(x-\mu)^2/(2\sigma^2)}[/itex] to reduce it to n odd.
 
  • #3
astralmeme said:
Greetings,

I am a computer scientist revisiting integration after a long time. I am stuck with this simple-looking integral that's turning out to be quite painful (to me). I was wondering if one of you could help.

The goal is to solve the integral

[tex]
\int_{0}^{\infty} e^{-(x - t)^2/2 \sigma^2} x^n\ dx .
[/tex]

Note that this is the convolution of the Gaussian centered around 0 with the function that equals $x^n$ for $x > 0$, and 0 elsewhere (modulo scaling).

In particular, I would be interested in seeing any relationship with the integral

[tex]
\int_{-\infty}^{\infty} e^{-(x - t)^2/2 \sigma^2} x^n\ dx .
[/tex]

which I have worked out.

Any suggestions?

Thanks in advance,
Swar

As long as t is not 0, the best you can do is express the integral in terms of the error function.
 
  • #4
Regarding error function, that is my guess too, but can you tell me what exactly would need be done?

I apologize if the question is obvious.


Swar
 
  • #5
astralmeme said:
Regarding error function, that is my guess too, but can you tell me what exactly would need be done?

I apologize if the question is obvious.


Swar
What I would do is first let u=x-t. Then xn becomes (u+t)n.
Expand the polynomial in u and then by succesive itegration by parts, get all the terms to a 0 exponent for u, which will be proportional to erf(t).
 

1. What is Gaussian convolution and what is it used for?

Gaussian convolution is a mathematical operation that involves multiplying a function by a Gaussian distribution. It is commonly used in signal processing and image processing to smooth out noisy data and reduce high-frequency components. It can also be used for edge detection and feature extraction in computer vision algorithms.

2. How does Gaussian convolution work?

Gaussian convolution works by convolving a given function or signal with a Gaussian distribution. This involves sliding the Gaussian distribution over the function and multiplying each point of the function by the corresponding point on the Gaussian distribution. The result is a smoothed version of the original function with reduced high-frequency components.

3. What is the difference between Gaussian convolution and other types of convolution?

Gaussian convolution differs from other types of convolution (such as box convolution or median convolution) in the shape of the convolution kernel. The Gaussian kernel has a bell-shaped curve, which results in a smoother output compared to other types of kernels. It also has the property of being rotationally symmetric, which can be useful in certain applications.

4. How do you choose the appropriate Gaussian kernel for a given application?

The appropriate Gaussian kernel for a given application depends on the specific requirements of the application. The main factors to consider are the standard deviation (which determines the level of smoothing) and the size of the kernel (which affects the computation time). In general, a larger standard deviation will result in more smoothing, but also a slower computation time.

5. Can Gaussian convolution be applied to any type of data?

Yes, Gaussian convolution can be applied to any type of data that can be represented as a function or signal. This includes 1D data (such as time series or audio signals), 2D data (such as images), and even higher-dimensional data (such as video or 3D images). However, the effectiveness of Gaussian convolution may vary depending on the characteristics of the data.

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