Density Function for Sums of Random Variables

In summary: The algorithm is correct, but the bounds for z - 1 and z - x are not correct. For z = X + Y, the bounds are 0 < x < z - x < 1. However, for z = Z + Y, the bounds are x < z < z - x. Thanks for the help!
  • #1
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Homework Statement



Given the joint density, f(x,y), derive the probability density function for Z = X + Y and V = Y - X.

Homework Equations



f(x,y) = 2 for 0 < x < y < 1
f(x,y) = 0 otherwise.

The Attempt at a Solution



For Z = X + Y, I can derive the fact that,

[tex]f_Z(z) = \int_{-\infty}^{\infty} f(x,z-x)dx [/tex]

The support should be 0 < x < z - x < 1? But I am kind of lost from here.

0 < x < 1 and 0 < y < 1, so 0 < z < 2? The book I am using tell me there are two cases but I have no idea how they deduced the two cases. From my very limited understanding, f(x,y) = 2 for all x,y in its support. So why are there two cases?

Thanks!
 
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  • #2
try thinking about it geometrically, [itex] f_{X,Y}(x,y) [/itex] is uniform on the triangle within the unit square and below y=x

now think about lines of constant z = x+y, and compare with your intgeral
 
  • #3
i came up with an answer on my own, is the process correct? i looked at the two arguments of f(x, z-x) and try to deduce which bounds are significant.

1) argument "x"

0 < x < z - x.

so x/2 < x < z/2. the z/2 is significant across all z in (0,2) since for z < 2, z/2 < 1. x < z/2 < 1, so the value of z/2 takes precedent over the numerical upper bound of 1.

the lower bound of 0 is significant

2) argument "z - x"

x < z - x < 1

so z - 1 < x < z - x.

the upper bound of z - x is not significant as x < z/2 is equivalent to x < z - x.

0 < z < 2 => -1 < z - 1 < 1, so the lower bound z - 1 < x is significant only for some z.

for 0 < z < 1, -1 < z -1 < 0 is totally not relevant compared to 0 as z-1 < 0 < x anyway. but for 1 < z < 2 we have 0 < z - 1 < 1 which is relevant as 0 < z -1 < x, so z - 1 is the greatest lower bound.

3) Intervals

Hence the intervals are 0 < z < 1 where 0 < x < z/2, and for 1 < z < 2 where z - 1 < x < z/2.

4 ) Algorithm

Basically I followed this self made algo which i hope is correct!

1. figure out the bounds for the two arguments of f(x, z - x)
2. compare it to the bounds for x, e.g. a < x < b
3. find the least upper bound and greatest lower bound. partition z if need be.


Thanks!
 

1. What is a density function for sums of random variables?

A density function for sums of random variables is a mathematical representation that shows the probability of obtaining a certain sum from a set of random variables. It is used in probability and statistics to analyze and predict the behavior of complex systems.

2. How is a density function for sums of random variables calculated?

The calculation of a density function for sums of random variables involves determining the probability distribution of each individual random variable and then using mathematical operations to combine them. This can be done through convolution, which is a mathematical operation that produces a new probability distribution from two existing ones.

3. What is the importance of studying density functions for sums of random variables?

Studying density functions for sums of random variables allows scientists to understand and predict the behavior of complex systems, such as weather patterns, stock market fluctuations, or biological processes. It also helps in making informed decisions and developing mathematical models for various real-world applications.

4. Can a density function for sums of random variables be used to determine the probability of a specific outcome?

Yes, a density function for sums of random variables can be used to determine the probability of a specific outcome, as it represents the likelihood of obtaining a certain sum from a set of random variables. However, it is important to note that the use of this function requires certain assumptions and may not accurately predict rare events.

5. Are there any limitations to using density functions for sums of random variables?

Yes, there are limitations to using density functions for sums of random variables. This function assumes that the random variables are independent and identically distributed, which may not always be the case in real-world scenarios. Additionally, it may not accurately predict rare events or outcomes that are affected by multiple variables.

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