The Product of Two Gaussian Random Variables is not Gaussian distributed:
- Is the product of two Gaussian random variables also a Gaussian?
- Also Wolfram Mathworld
- So this is saying $X \sim N(\mu_1, \sigma_1^2)$, $Y \sim N(\mu_2, \sigma_2^2)$ then $XY \sim W$ where W is some other distribution, that is not Gaussian
But the product of two Gaussian PDFs is a Gaussian PDF:
- Calculate the product of two Gaussian PDF's
- Full Proof
- This tutorial which I am trying to understand Writes: $N(\mu_1, \sigma_1^2)\times N(\mu_2, \sigma_2^2) = N(\frac{\sigma_1^2 \mu_2 + \sigma_2^2 \mu_1}{\sigma_1^2 + \sigma_2^2},\frac{1}{\frac{1}{\sigma_1^2} + \frac{1}{\sigma_2^2}})$
What is going on here?
What am I doing when I take the product of two pdfs
vs. when I take the product of two variables from the pdfs?
When (what physical situation) is described by one,
and what by the other?
(I think a few real world examples would clear things up for me)
Best Answer
The product of the PDFs of two random variables $X$ and $Y$ will give the joint distribution of the vector-valued random variable $(X,Y)$ in the case that $X$ and $Y$ are independent. Therefore, if $X$ and $Y$ are normally distributed independent random variables, the product of their PDFs is bivariate normal with zero correlation.
On the other hand, even in the case that $X$ and $Y$ are IID standard normal random variables, their product is not itself normal, as the links you provide show. The product of $X$ and $Y$ is a scalar-valued random variable, not a vector-valued one as in the above case.