Alright. I have tried to find the easiest way to explain Baye's. I have watched quite a number of videos and read quite a number of articles to suppliment what I already know and to get better devices for delivering the explanation. At this point I think its now just me being a perfectionist. So I'll teach it as best as I can get myself to: Using proofs.
First let's state it formally
How likely it is that some outcome will be the outcome given some event. That is what probability is. Its a ratio. Given an event, there's many possible outcomes. The probability of some outcome being the outcome that happens is a ratio of how much of the outcome is represented in total outcomes to the total number of possible outcomes. For unweighted or equally likely outcomes at least.
Its a very important foundational concept in a lot of Computer Science
Fields. Without understanding how it works, you'll have a hard time
understanding more advanced concepts like the Baye's theorem, or
wrapping your head around what's going on with Markov chains or, working
with the foundational concepts for most of modern AI i.e: Neural
Networks. I'll try to explain my understanding of it to you here without
using any formal terms. So don't be surprised if you don't see a lot of
P(A n B n C) or posterior and the such. There will be time for that.
