Pdf of the sum of two distributions

In summary, when 0 < z < 1, the pdf of X+Y is z, and when 1 < z < 2, it is 2-z. The distribution function is z^2/2 for 0 < z < 1 and 2z-z^2/2-1 for 1 < z < 2.
  • #1
shan
57
0
I'm not too sure where to post this so feel free to move it :)

Anyway I'm hoping someone could explain the answer of this problem to me (I would ask my lecturer but he's conveniently away for the week for a meeting).



Suppose X and Y are iid continuous random variables with density f. Then X+Y has density:

f_X+Y(z) = integral from infinity to neg infinity of f(x)f(z-x)dx

which is the convolution of the densities of X and Y.

Use this to determine the pdf (probability density function) and distribution function of X+Y when X and Y are iid U(0,1). (independently and identically distributed, Uniform(0,1)).

The answer starts by showing the region where f(x)f(z-x) is non-zero ie the area between z-x=0 and z-x=1 (and I understand this part, if X and Y are uniform, the sum will be 0 outside (0,2)). It then says that the density function is:

For 0<z<1
f_X+Y(z) = (integral from z to 0 of 1dx) = (x evaluated at z and 0) = z.

For 1<z<2
f_X+Y(z) = (integral from 1 to z-1 of 1dx) = (x evaluated at 1 and z-1) = 2-z.

I don't understand why for 1<z<2, the integral is from 1 to z-1? Where did these boundaries come from?

The other part is the distribution function which is supposedly:

For 0<z<1
F_X+Y(z) = (integral from z to 0 of wdw) = (w^2/2 evaluated at z and 0) = z^2/2

For 1<z<2
F_X+Y(z) = (integral from z to 0 of f_X+Y(w)dw) = (integral from 1 to 0 of f_X+Y(w)dw + integral from z to 0 of f_X+Y(w)dw)
= (integral from 1 to 0 of wdw + integral from z to 1 of (2-w)dw) = 1^2/2 + (2w-w^2/2) evaluated at z and 1)
= 1/2 + (2z-z^2/2)-(2-1/2) = 2z-z^2/2-1

Again, I'm having problems with where the numbers in the integral came from when 1<z<2.

Sorry for the hard read >_< I can't seem to preview the latex images and since I don't know how to use them very well I thought I could do without...
 
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  • #2
Hi there!

It looks like you are having some confusion with the integration limits of your problem. The bounds of the integral will depend on the value of z. When 0 < z < 1, the integrand is non-zero between 0 and z, so the integral should be taken from 0 to z. When 1 < z < 2, the integrand is non-zero between 1 and z-1, so the integral should be taken from 1 to z-1.

The reason for this is that X and Y are uniform on (0,1). This means that the sum X+Y can not be greater than 2, since if both X and Y = 1 then X+Y = 2. Therefore, when z > 1, the integrand f(x)f(z-x) must be equal to 0 for x > 1, since otherwise the sum would exceed 2. This is why the upper bound of the integral when 1 < z < 2 must be z-1 instead of z.

I hope this explanation was helpful in understanding your question!
 

FAQ: Pdf of the sum of two distributions

What is a Pdf of the sum of two distributions?

A Pdf of the sum of two distributions refers to the probability density function that results from adding two probability distributions together. It represents the likelihood of obtaining a particular sum of values from the two distributions.

How is the Pdf of the sum of two distributions calculated?

The Pdf of the sum of two distributions is calculated by convolving the two individual Pdfs of the distributions. This involves multiplying each value from one Pdf by the corresponding value from the other Pdf, and then summing all of these products. The resulting values make up the new Pdf of the sum.

What does the shape of the Pdf of the sum of two distributions indicate?

The shape of the Pdf of the sum of two distributions can vary depending on the characteristics of the individual distributions being added. In general, it tends to be wider and flatter than the individual Pdfs, and can exhibit different modes, peaks, and tails.

How does the Pdf of the sum of two distributions relate to the Central Limit Theorem?

The Central Limit Theorem states that the sum of a large number of independent and identically distributed random variables will tend towards a normal distribution. This means that the Pdf of the sum of two distributions can be approximated by a normal distribution if the individual distributions are large enough and meet certain criteria.

Can the Pdf of the sum of two distributions be used to predict outcomes?

The Pdf of the sum of two distributions is a mathematical representation of the likelihood of obtaining a particular sum from the two distributions. It can be used to make predictions in certain situations, but it should not be relied upon as the sole predictor of outcomes as it is based on probability and not certainty.

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