Let's say i want to predict an output `C` by multiplying two distributions `A``B` = `C`.
Assuming I am just guessing at the distribution of `A` and `B` (Uniform? Bernoulli? Geometric? Log-Normal?), would I get a better estimate by just multiplying `mean(A)` `mean(B)` ?
Point values suck. However, predicting the mean is often possible/realistic. And I feel like I am taking wild guess when describing a distribution of a data set to be honest.
TLDR:
What results in better prediction/guestimate? multiplying incorrect probability distributions? Or multiplying more-correct means/point values?
Assuming I am just guessing at the distribution of `A` and `B` (Uniform? Bernoulli? Geometric? Log-Normal?), would I get a better estimate by just multiplying `mean(A)` `mean(B)` ?
Point values suck. However, predicting the mean is often possible/realistic. And I feel like I am taking wild guess when describing a distribution of a data set to be honest.
TLDR: What results in better prediction/guestimate? multiplying incorrect probability distributions? Or multiplying more-correct means/point values?