What is the convolution of two independent standard gaussian distributions?

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In summary, the convolutions of the density of the distribution X1+X2 and X*root2 where X has the standard gaussian distribution produces the same distribution as X.
  • #1
stukbv
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Hello, my question ;

Suppose X1 and X2 are independent random variables, each with the standard gaussian distribution. Compute, using convolutions the density of the distribution X1 + X2 and show that X1+ X2 has the same distribution as X * root2 where X has standard gaussian distribution.

Basically I have said
take fx+y (z) = integral fx(z-y)fy(y) dy.

I have said fx (z-y) = 1/root2pi * exp(-.5(z-y)^2) and that fy = 1/root2pi exp(-(y^2)/2)

I have then multiplied these together inside an integral from minus infinity to infinity
I end up getting
1/2pi *exp(-z^2 / 2) * integral exp(y(-y+z))

Now how do i go further, and am i even on the right lines!?

Any help would be very much appreciated

Thanks.
 
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  • #2
Hi stukbv! :smile:

If I get it right, your problem is

[tex]\int_{-\infty}^{+\infty}{e^{-(y^2-zy)}dy}[/tex]

Try to complete the square of [itex]y^2-zy[/itex]...
 
  • #3
You are near the final result. consider micromass's hint.

if [itex] X,Y [/itex] be independent RVs [itex] \widetilde{} G(0,1) [/itex] then [itex]
X+Y \widetilde{} G(0, \sqrt{2}) [/itex]
 
  • #4
Hi so now i have 1/2pi * e^(-k^2/2) * integral exp(-(l^2-kl))
Then i complete the square and get;
1/2pi exp(-k^2/4) integral exp(-(l-k/2)^2) dl

But how on Earth is this the same as the distribution of the other!?

Thanks
 
  • #5
Well, you just need to calculate

[tex]\int_{-\infty}^{+\infty}{e^{-(y-z/2)^2}dy}[/tex]

Isn't this easy to calculate? It is a well known integral... hint: substitute u=y-z/2
 
  • #6
Dear stukbv,
Don't forget: "integral of any pdf over all its range is equal to 1"!
 
  • #7
I still don't get it, i can integrate it normally but I wouldn't know how to work out the value at infinity and - infinity!?
 
  • #8
What is

[tex]\int_{-\infty}^{+\infty}{\frac{1}{\sqrt{2\pi}}e^{-\frac{x^2}{2}}dx}[/tex]

Hint, it is the integral of a pdf...
 
  • #9
This is 1, but i don't have that above i have just exp(-u^2) with no / 2 ?!
 
  • #10
Well, try a suitable substitution that will give you /2 in the exponent.
 
  • #11
:S how can i just change it ?
 
  • #12
Just try a good substitution x=ay. What value must a be to get /2 in the exponent?
 
  • #13
x=2y-z?
 
  • #14
no, x= root2 (y-z/2)
 
  • #15
stukbv said:
no, x= root2 (y-z/2)

Yes! That looks good!
 
  • #16
So, i finally get that fx+y(z) = 1/root(2pi) *exp(-z^2/4)

And now to say that it equals dist root2 X where X ~ N(0,1) then do i just say that
D = root2 * X + 0 , so i can just replace all X's with D/root2 and its the same thing?
Does it matter that one uses D , i.e ill get the same as above but with D's where the Z's are, is this still ok?
 
  • #17
stukbv said:
So, i finally get that fx+y(z) = 1/root(2pi) *exp(-z^2/4)

And now to say that it equals dist root2 X where X ~ N(0,1) then do i just say that
D = root2 * X + 0 , so i can just replace all X's with D/root2 and its the same thing?
Does it matter that one uses D , i.e ill get the same as above but with D's where the Z's are, is this still ok?

This is quite difficult to follow. Can't you just say that [itex]X+Y\sim N(0,\sqrt{2})[/itex]...
 
  • #18
i guess so yeah! thanks.
 
  • #19
Also , in another part it asks me to let Y=aX+b and says what is the density of the distribution of Y if X has the standard distribution, (i.e. to derive it) is it right to let Y-b/a = X, put these in place of all the x's in the fx and then multiply by the "stretch factor" of 1/a?
 

1. What are convolutions and how are they used in scientific research?

Convolutions are mathematical operations used to represent the relationship between two signals or functions. In scientific research, they are often used to analyze and extract features from data, as well as to model and simulate complex systems.

2. What is the Gaussian distribution and why is it important?

The Gaussian distribution, also known as the normal distribution, is a probability distribution that is commonly used to model many natural phenomena. It is important because it allows us to make predictions and estimate the likelihood of events based on their probability.

3. How do convolutions and Gaussian relate to one another?

Gaussian functions are often used in convolution operations to smooth and filter data. This is because the Gaussian distribution has a bell-shaped curve that allows it to effectively filter out noise and highlight important features in the data.

4. Can convolutions and Gaussian be applied in other fields besides scientific research?

Yes, convolutions and Gaussian have a wide range of applications in fields such as engineering, economics, finance, and image processing. They are also used in machine learning and artificial intelligence algorithms to analyze and process large datasets.

5. Are there any limitations to using convolutions and Gaussian in scientific research?

While convolutions and Gaussian can be powerful tools in scientific research, they also have their limitations. For example, they may not be suitable for analyzing non-linear relationships or highly complex systems. It is important for scientists to carefully consider the specific characteristics of their data before using these methods.

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