Undergrad Probability Density Function of the Product of Independent Variables

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To find the probability density function (PDF) of the product of two independent random variables A and B, one must first express the joint PDF, f_{A,B}, in terms of the individual PDFs, f_A and f_B, leveraging their independence. The cumulative distribution function (CDF) of Y, defined as Y=AB, can be calculated using a double integral over the joint PDF. Differentiating the CDF yields the PDF of Y, f_Y. If A or B can be zero, the integration must be adjusted to avoid division by zero, complicating the solution. The discussion also notes that if A and B are standard normal variables, the ratio A/B results in a Cauchy distribution.
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How do I find the probabilty density function of a variable y being y=ab, knowing the probabilty density functions of both a and b?
How do I find the probabilty density function of a variable y being y=ab, knowing the probabilty density functions of both a and b? I know how to use the method to calculate it for a/b - which gives 1/pi*(a²/b²+1) - using variable substitution and the jacobian matrix and determinant, but which functions should i use for the product?
 
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Let the random variables be ##A## and ##B##, with density functions ##f_A## and ##f_B## respectively. Let ##Y=AB##. For simplicity, assume that ##A,B## can only be positive.
1. express the joint probability density function ##f_{A,B}## of the two variables in terms of ##f_A## and ##f_B##. Use the fact that they are independent.

2. calculate the cumulative distribution function of Y:
$$F_Y(y) = \int_{0}^\infty \int_{0}^{y/a} f_{A,B}(a,b)\, db\,da$$

3. Differentiate ##F_Y## to get ##f_Y##, the PDF of ##Y##.

If A or B can be zero, the solution needs to be more complex, to avoid divisions by zero. The integrations need to be split into parts that avoid zero.

By the way, the formula you provided for the PDF of A/B does not look right, as it contains no integrals. Have you omitted some info from the question, such as that the two variables are standard normal, or some other specific distribution?
 
andrewkirk said:
Let the random variables be ##A## and ##B##, with density functions ##f_A## and ##f_B## respectively. Let ##Y=AB##. For simplicity, assume that ##A,B## can only be positive.
1. express the joint probability density function ##f_{A,B}## of the two variables in terms of ##f_A## and ##f_B##. Use the fact that they are independent.

2. calculate the cumulative distribution function of Y:
$$F_Y(y) = \int_{0}^\infty \int_{0}^{y/a} f_{A,B}(a,b)\, db\,da$$

3. Differentiate ##F_Y## to get ##f_Y##, the PDF of ##Y##.

If A or B can be zero, the solution needs to be more complex, to avoid divisions by zero. The integrations need to be split into parts that avoid zero.

By the way, the formula you provided for the PDF of A/B does not look right, as it contains no integrals. Have you omitted some info from the question, such as that the two variables are standard normal, or some other specific distribution?

Oh, yes, they were standard normal in the A/B example I mentioned. My bad. Though I'm not sure if I can say my variables are standard normal variables.
 
megf said:
Summary: How do I find the probabilty density function of a variable y being y=ab, knowing the probabilty density functions of both a and b?

How do I find the probabilty density function of a variable y being y=ab, knowing the probabilty density functions of both a and b? I know how to use the method to calculate it for a/b - which gives 1/pi*(a²/b²+1) - using variable substitution and the jacobian matrix and determinant, but which functions should i use for the product?
If A, B are standard normals A/B has a Cauchy distribution.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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