The point is to build intuition, those take time and unless the student is particularly mathematically inclined, you can't rush it. That's why you have to start in high school. Right now, the focus of pre calc is to induce them into calculus through slopes, limits, and derivatives. But a good chunk of high level statistics don't need that to build intuition. For example, we generally teach students y=mx+b in middle school and high school. This is considered a perfectly normal part of math education. It's accepted that the students may not understand the full meaning of linearity and regression at first, but at higher levels the formula would be second nature to them. This is the point I am trying to make about statistical learning (not specifically Bayesian theory in particular, but the key concepts around it). Generative ML and stuff like LLMs require a specific frame of mine and view of the world, and it's best to develop it early. The way we are teaching statistical learning theory, at the lower levels, is essentially dumping a massive bag of tricks on them and hoping for the best. We need to change that approach and start treating it like pre-calc and algebra, something to be taught level by level, with building intuition as a core focus. It's not easy to communicate the idea that "the next word prediction of the LLM (depending on the model) is generally the joint probability of all other words that come before it by sampling from a multidimensional probability distribution" to most 14-16 year olds.