Help with probability distribution function question

In summary, the homework statement is that X and Y are two independent random variables with the same probability density funtion over a given range. The Attempt at a Solution states that a) X+Y is simply the product of each of their densities, and b) f(X*Y) is the sum of their individual distributions.
  • #1
TPDC130
6
0

Homework Statement



Let X and Y be two independent random variables with the same probability density funtion over:

f(x) = {1/a if x € [0,a]
{0 if x=0

Find the density distribution of a) X + Y and b) X*Y


Homework Equations





The Attempt at a Solution



Ok, my initial thoughts are for:

a) That f(X+Y) is simply the product of each of their densities, which would result in 1/(a^2)

b) f(X*Y) is the sum of their individual distributions. i.e. 2/a


This seems a little too simple for me and I think I am looking at it from the wrong perspective.

Any thoughts or advice?
 
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  • #2
No, i don't think this is right. Just consider that the two random variables are idependently and identically distributed w.r.t the distribution you mentioned above. So [itex] X + Y [/itex]takes its values in the interval [itex] [0, 2a][/itex]. What you were referring to is the Gaussian distribution where the result concerning a would be valid. In this case however, since the points are distributed according to (1) a continuous distribution which is (2) uniform, we conclude that if the two random variable [itex]X, Y[/itex] also are distributed according to this distribution, then there sum has to be distributed (again uniformly) with probability density [itex] 1/2a [/itex]. Converesely, take question b. You may regard X and Y as some sort of random coordinates which give you point lying a sqaure with side length a. Again, recalling that we deal with uniform distributions, we find the probability density function to be constant [itex] 1/a^{2}[/itex]. The problem is that one has to distinguish between uniform distributions and the standard distributions, i.e., Gaussian, binomial, geometric, multinomial, Poisson, exponential.
 
  • #3
You can get the density f(z) of X+Y by differentiating the CDF F(z) = Pr{X+Y <= z}, and the latter is an integral over the two-dimensional region {x+y <= z, 0 <= x,y <= a}. Or, Google "convolution".

RGV
 

1. What is a probability distribution function (PDF)?

A probability distribution function is a mathematical function that describes the probability of a random variable taking on a certain value or falling within a certain range of values. It is used to model and analyze the likelihood of different outcomes in a given scenario.

2. How is a probability distribution function different from a probability density function (PDF)?

A probability distribution function is a discrete function that assigns probabilities to specific values of a random variable, while a probability density function is a continuous function that describes the relative likelihood of a variable taking on a range of values. In other words, a PDF is the derivative of a PDF.

3. What is the difference between a discrete and continuous probability distribution function?

A discrete probability distribution function is used for variables that have a finite or countable number of possible outcomes, while a continuous probability distribution function is used for variables that have an infinite number of possible outcomes. Discrete distributions are represented by a probability mass function, while continuous distributions are represented by a probability density function.

4. How is the area under a probability distribution function related to probability?

The area under a probability distribution function represents the total probability of all possible outcomes. In other words, the sum of all probabilities within the distribution must equal 1. This allows us to calculate the probability of a specific outcome or range of outcomes by finding the corresponding area under the curve.

5. Can a probability distribution function be used to make predictions or forecasts?

Yes, a probability distribution function can be used to make predictions or forecasts by using the calculated probabilities to estimate the likelihood of certain outcomes occurring. However, it is important to note that a probability distribution function is based on assumptions and may not always accurately predict real-world scenarios.

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