Nobody seems to be capable of explaining this properly. It's like monad tutorials, they explain what happens while mistakenly thinking they are telling you why it happens. I keep trying to fit this idea into my head and I can't because the information is not given.
- Where did this difference come from? When did it develop?
- What are the basic premises that a Bayesian believes that a frequentist doesn't, and vice versa? Reason it all the way through front and back.
- What does the B/F's model look like? What are the pieces they use, how are they arranged, what are the dependencies, how does causality flow?
- Why are the choices made by one invalid for the other's model? Where do they agree deliberately despite this?
- What are the consequences in the real world? Give me a real example on why this difference matters? "Real" meaning I don't care about dice, I care about engineering and science.
Instead you get some bullshit about fitting a distribution you don't understand to a model you can't see, while relying on understanding the nuances between words like probability and likelihood which is what you are trying to learn in the first place. Plus I swear the numbers agree in 99% of the "examples" given, with some handwaving "but it's different" to excuse it.
Fucking explanations, how do they work? Not in academia.