Sifting Property of the Impulse Function

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The discussion centers on the sifting property of the Dirac delta function, which isolates the value of a continuous function at a specific point. Participants clarify that while substituting directly into the function yields the same result, the delta function's integral form is beneficial for mathematical manipulations, particularly in probability theory. An example involving the convolution of probability distributions illustrates how the delta function simplifies the process of finding the distribution of the sum of two independent random variables. The conversation emphasizes the utility of the delta function in complex integrals, allowing for easier calculations by focusing on specific contributions. Overall, the sifting property is highlighted as a powerful tool in mathematical analysis and probability.
El Moriana
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1. The problem
I have a problem grasping what the point of the sifting property of the Dirac function is.
It isolates the value at a point in a function, right?
Doesn't just substituting that point into the function do exactly the same thing?

Homework Equations



Sifting poperty:
if f(t) is continuous at t=a then

\int_{-∞}^∞f(t)δ(t-a)dt= f(a)

from: Glyn James, Advanced Modern Engineering Mathematics (3rd Ed), Section 2.5, p.155
 
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so based on the properties of the delta function you know
\int \delta (x) dx = 1

A handwaving explanation is that if f is continuous and if you zoom in on a small enough region (x-\epsilon, x+\epsilon), then f(x) will be close to constant on this region.

The delta function zero everywhere except at x=a and the integral evaluates to exactly the value of the function at the point x=a
\int f(x)\delta (x-a) dx = f(a)

It is the same as substituting into the function for f(a), and this is exactly what the inequality tells you. It is a useful propperty of the delta function for various mathematical manipulations
 
First of all, thanks for the reply.

I understand how the shifting property works and how it equates to the function at point x=a.

What I don't see are the "various mathematical manipulations", its usefulness.
Under what circumstances does it make life simpler to write \int f(t)δ(t-a)dt than simply f(a)?
 
Hows your probability? one that springs to mind is as follows...

say you have two independent random variables X & Y, with joint pdf p_{X,Y}(x,y) = p_{X}(x)p_{Y}(y)

And say you want to find the probability distirbution for Z= X+Y [\itex]<br /> <br /> Well p(z) will only have contibutions when z= x+y, or z-x-y=0, so you can write <br /> p_{Z}(z) = \int \int p_{X}(x)p_{Y}(y) \delta(z-x-y)dxdy<br /> p_{Z}(z) = \int \int p_{X}(x)p_{Y}(z-x) dx<br /> <br /> which shows the distribution of the sum of two RVs is given by the convolution of their distributions
 
El Moriana said:
Under what circumstances does it make life simpler to write \int f(t)δ(t-a)dt than simply f(a)?

In fact its almost the opposite, in that if you end up with an integral with a delta function in it, you can use the above fact to simplify the expression
 
I'm starting to get it. Though, looking at it, would your example not result in:

p_{Z}(z) = \int p_{X}(x)p_{Y}(x-z) dx

due to the sign of the y and the one integral being cancelled?

I take it you would also be able to use this to eliminate the integral w.r.t x if it turns out to be more convenient?

p_{Z}(z) = \int p_{Y}(y)p_{X}(y-z) dy
 
Not quite you can only use it in this case because you only want contributions from where z=x+y. When integrating over y, this occurs when y=z-x in the delta function.
 

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