I guess I really, really need someone to explain to me like I'm a total idiot why I'd want to use a 2D function on data that might be (and probably is) multidimensional and interdependent.

>"The big trouble with probabilities is that, potentially, every event is contingent on every other event and the joint probability distribution of all possible inputs and outputs is a huge dimensional space."

Maybe! But this seems like a way more interesting challenge, with the potential to handle way more dimensions, and be way closer to "reality" and "truth", whatever those definitions really are or really mean.

>" It's not good enough to estimate that A has a 80% probability of being true, in general you need to estimate what the probability of A is if B is true and C is false."

Doesn't that still seem a little lame though? And massively error prone?

Filtering problems through a stochastic process of probabilities kinda feels like trying to rig a pachinko machine.