Re:End-User Probabilistic Programming [pdf]
by axpence on Thursday June 20, 2019 @12:10AM
(#20229788)
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?
Re:End-User Probabilistic Programming [pdf]
by jmmcd on Thursday June 20, 2019 @04:59PM
(#20237317)
I don't have a good answer. But I wonder if there are some realistic situations where we would have a good guess at the mean, but no clue about the distribution.
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?