Gaussian center not average value?

In summary, the Gaussian centered at x=2 has an average of 2, but the one centered at x=0 to x=4 has an average of 2*sqrt(pi).
  • #1
DocZaius
365
11
I put this under statistics because of your knowledge of the Gaussian.

I have run into an elementary problem. I was considering what the average value, <x>, is for a Gaussian with an x offset, and got results which don't make sense to me.

First, it is obvious that for P1(x)=e^(-(x^2)) the average is 0. The integral of x*P1(x) from -inf to inf is zero.

So I decided to center the Gaussian at x=2: P2(x)=e^(-(x-2)^2). I was expecting an average of 2, but instead got 2 times the square root of pi! The integral of x*P2(x) from -inf to inf is 2*sqrt(pi)...

I thought, well perhaps this has to do with the infinities lying on either side, and the plus side starting at higher values than the minus side...So I decided to see the average for the Gaussian centered at 2 from x=0 to x=4. Surely that must be 2, it is clearly symmetric in that interval. Turns out that x*P2(x) from 0 to 4 is (according to Wolfram Alpha) still 2*sqrt(pi).

Can anyone explain why this last Gaussian's average x isn't 2?

Thanks.
 
Physics news on Phys.org
  • #2
DocZaius said:
So I decided to center the Gaussian at x=2: P2(x)=e^(-(x-2)^2). I was expecting an average of 2, but instead got 2 times the square root of pi! The integral of x*P2(x) from -inf to inf is 2*sqrt(pi)...

Can anyone explain why this last Gaussian's average x isn't 2?
You have the wrong PDF. The PDF for a normal distribution with mean [itex]\mu[/itex] and standard deviation [itex]\sigma[/itex] is given by
[tex]p(x) = \frac 1 {\sigma \sqrt{2\pi}} \exp\left(-\frac 1 2 \left(\frac {x-\mu}{\sigma}\right)^2\right)[/tex]
A couple of examples for a mean of 2:

1. [itex]\sigma=1[/itex]:
[tex]p(x) = \frac 1 {\sqrt{2\pi}} \exp\left(-\frac 1 2 (x-2)^2\right)[/tex]

1. [itex]\sigma=1/\surd 2[/itex]:
[tex]p(x) = \frac 1 {\sqrt{\pi}} \exp\left(-(x-2)^2\right)[/tex]
 
Last edited:
  • #3
I am attempting to look at the average x for the function I brought up. (It's from a quantum textbook). I understand it is different from the one you wrote. Can you explain my problems with the function I wrote?

My problem is that the intuitive idea that a Gaussian centered at x=a would have an average x of a is clearly wrong, but in the case where I look at a symmetric interval around it, I don't understand how that can be the case.
 
  • #4
Your function is not a PDF. Calculate [itex]\int_{-\infty}^{\infty} p(x)\,dx[/itex] for your function. It won't be one.
 
  • #5
Is the concept of "average x" only applicable to functions whose integrals at infinity are 1? I don't see why that must be the case.

I am aware that my function does not count as a probability function since it doesn't integrate to 1 at infinity, but it seems to me that one could still devise an "average x" from it by weighing all its x's with f(x) inside the integrand.
 
  • #6
DocZaius said:
Is the concept of "average x" only applicable to functions whose integrals at infinity are 1? I don't see why that must be the case.

I am aware that my function does not count as a probability function since it doesn't integrate to 1 at infinity, but it seems to me that one could still devise an "average x" from it by weighing all its x's with f(x) inside the integrand.
If your function f(x) is not normalized (i.e., [itex]\int_D f(x)\,dx \ne 1[/itex] where the integral is over some applicable domain such as -∞ to +∞) you can still compute an average, but you will need to account for the fact that the function is not normalized. In your case,
[tex]\bar x \equiv \frac{\int_{-\infty}^{\infty} xf(x)\,dx}{\int_{-\infty}^{\infty} f(x)\,dx}[/tex]
 
  • #7
Thanks!
 

1. What is the difference between the Gaussian center and the average value?

The Gaussian center, also known as the mean or central tendency, is the value at the center of a Gaussian distribution curve. It represents the most commonly occurring value in a set of data. The average value, or arithmetic mean, is the sum of all values in a set divided by the number of values. While they may be the same in some cases, they are not always equal.

2. How is the Gaussian center calculated?

The Gaussian center is calculated by finding the sum of all data points and dividing it by the total number of data points. This is equivalent to finding the average of the data set.

3. Can the Gaussian center be affected by outliers in the data?

Yes, the presence of outliers in a data set can significantly impact the Gaussian center. Outliers are extreme values that do not fit the pattern of the rest of the data, and they can skew the mean towards their direction.

4. What does the Gaussian center represent in a normal distribution?

In a normal distribution, the Gaussian center represents the peak or highest point of the bell-shaped curve. This point represents the most commonly occurring value in the data set.

5. Why is the Gaussian center not always the best measure of central tendency?

While the Gaussian center is a commonly used measure of central tendency, it may not always be the best choice. In some cases, the median or mode may be a more appropriate measure, especially if the data set contains outliers or is heavily skewed. It is important to consider the characteristics of the data before deciding on the best measure of central tendency.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
18
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
695
  • Set Theory, Logic, Probability, Statistics
Replies
25
Views
2K
  • Introductory Physics Homework Help
Replies
6
Views
286
Replies
2
Views
531
  • Set Theory, Logic, Probability, Statistics
Replies
9
Views
1K
Replies
2
Views
1K
Replies
2
Views
2K
  • Calculus and Beyond Homework Help
Replies
9
Views
2K
  • Calculus
Replies
1
Views
1K
Back
Top