Probability random vector, transformation

In summary, the problem involves finding the pdf of Z, where Z = X + Y, given that the pdfs of X and Y are e^(-x) and e^(-y), respectively. The probability of Z being less than or equal to 0 is not necessarily 0, and the probability of Z being less than or equal to 6 can be found by splitting the integral into two parts. The general formula for finding the pdf of Z is f(z) = ∫f(x,z-x)dx or f(z) = ∫f(z-y,y)dy.
  • #1
Laura1321412
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Homework Statement



Let f(x,y) = e^(-x-y), 0<x< infinity, 0<y<infinity, zero elsewhere, be the pdf of X and Y. Then if Z = X + Y, compute P(Z<=0), P(Z,<=6), and, more generally P(Z<=z), for 0<z<infinity. What is the pdf of Z?

Homework Equations



P(x,y) = ∫∫(f(x,y) dxdy


The Attempt at a Solution



so, P(Z<= 0 ) is pretty obviously 0

P(Z<=6) = P(X+Y <= 6)
=P(X<=6-Y)

into equation

∫from (0 to infinty) ∫ from (0 to 6-Y ) e^(-x-y) dx dy

-e^(-x-y) eval from 0 to 6-Y
= (-e^-6 ) + e^-y

∫from (0 to infinity) (-e^-6 ) + e^-y dy
= -ye^(-6) -e^-y eval from (0 to infinity)
= -infinity -0 - ( 0 - 1)
= - infinity

so, I am definately going wrong somewhere because a probability of negative infinity makes no sense... I know this could be done using a different method of tranformations, but i think I am supposed to do something along these lines because that's what is taught in the preceeding chapter.
 
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  • #2


Hello, thank you for your post. Your attempt at solving this problem is on the right track, but there are a few errors that I would like to point out.

First, P(Z<=0) is not necessarily zero. Remember that Z = X + Y, so in order for Z to be less than or equal to 0, both X and Y would have to be 0. This means that P(Z<=0) would be equal to f(0,0), which is not necessarily zero.

Second, when calculating P(Z<=6), you are correct in setting up the integral as P(X<=6-Y). However, when evaluating the integral, you need to take into account the range of values for Y. Since Y can range from 0 to infinity, you need to split the integral into two parts: one from 0 to 6, and one from 6 to infinity. This is because when Y is greater than 6, the integral will evaluate to 0.

Lastly, when calculating the integral for P(Z<=z), you need to take into account the range of values for both X and Y. This means that the integral should be set up as ∫∫(f(x,y) dxdy, with the limits of integration being 0 to z for both x and y.

In general, the pdf of Z can be found by convolving the pdfs of X and Y. This means that the pdf of Z would be f(z) = ∫f(x,z-x)dx, with the limits of integration being 0 to z. This can also be written as f(z) = ∫f(z-y,y)dy, with the limits of integration being 0 to z.

I hope this helps clarify any confusion you may have had. Keep up the good work!
 

Related to Probability random vector, transformation

1. What is a probability random vector?

A probability random vector is a mathematical concept that represents a set of random variables, with each variable having its own probability distribution. This means that the vector describes the possible outcomes of a random experiment, with each outcome having a certain probability of occurring.

2. How is a probability random vector different from a regular vector?

A probability random vector differs from a regular vector in that it represents random variables rather than fixed values. This means that the values of a probability random vector are not known beforehand, but rather follow a probability distribution.

3. What is a transformation of a probability random vector?

A transformation of a probability random vector refers to the process of applying a mathematical function to the random variables in the vector. This results in a new probability random vector with potentially different outcomes and probabilities.

4. How is a transformation of a probability random vector useful?

A transformation of a probability random vector can be useful in many ways, such as simplifying complex probability distributions or finding the distribution of a new variable that is a combination of the original variables. It can also help in analyzing and predicting the behavior of a system or process.

5. What are some common transformations applied to probability random vectors?

Some common transformations applied to probability random vectors include linear transformations, such as addition and multiplication, as well as non-linear transformations like logarithmic and exponential functions. Other common transformations include convolution, marginalization, and conditioning.

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