Pdf of weighted uniform random variables

In summary, the conversation discussed the random variables y(i) and the inquiry about their joint distribution. The suggestion was made to approximate the sum using the Central Limit Theorem if N is large enough, or to use standard tools for calculating sum/ratio distributions for a more precise result.
  • #1
PAHV
8
0
Let x(1),...,x(N) all be independent uniformally distributed variables defined on (0,1), i.e. (x(1),...,x(N)) - U(0,1). Define the random variable y(i) = x(i)/(x(1)+...+x(N)) for all i=1,...,N. I’m looking for the pdf of the random variables y(1),…,y(N). Has anyone come across such random variables? If so, any references would be appreciated!
 
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  • #2
I don't think it's too hard to work out. Start with deriving the CDF. P[Yi < y] = P[x(i)/(x(1)+...+x(N)) < y] = etc.
 
  • #3
Is the question about the joint distribution of the [itex] y_i [/itex]?
 
  • #4
Yes, the question is about the joint distribution of the y(i). Any help on getting started with the pdf is highly appreciated!
 
  • #5
Try doing it for n=2. Even that is quite tricky. In general it looks very messy.
 
  • #6
Hey PAHV and welcome to the forums.

If N is large enough, then you can approximate the sum by using the Central Limit Theorem (i.e. normal) approximation.

If not (or you are particular on having everything precise), then use the standard tools for calculating sum/ratio distributions.
 

What is a Pdf of Weighted Uniform Random Variables?

A Pdf (Probability density function) of weighted uniform random variables is a mathematical function that describes the probability distribution of a set of weighted uniform random variables. It gives the probability of a random variable falling within a certain range of values.

What is the difference between a Weighted Uniform Random Variable and a Regular Uniform Random Variable?

A regular uniform random variable has a constant probability of occurring for all possible values, whereas a weighted uniform random variable assigns different probabilities to different values. This means that certain values have a higher chance of occurring than others in a weighted distribution.

How is a Pdf of Weighted Uniform Random Variables calculated?

The Pdf of weighted uniform random variables is calculated by dividing the weighted probability by the total weight of all possible values. This gives the probability of each value occurring in the distribution.

What are some real-world applications of Pdf of Weighted Uniform Random Variables?

The Pdf of weighted uniform random variables is commonly used in statistical modeling and data analysis. It can be used to represent the distribution of various phenomena in fields such as finance, biology, and engineering. For example, it can be used to model the distribution of stock prices or the distribution of genetic traits in a population.

What are the limitations of Pdf of Weighted Uniform Random Variables?

One limitation of Pdf of weighted uniform random variables is that it assumes that all possible values have a finite probability of occurring. This may not always be the case in real-world scenarios. Additionally, it may not accurately represent distributions that have a large number of outliers or extreme values.

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