The Bayesian view

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so school.
Maybe do some basic statistics.
You got great experiment.
We'll do the maths class, get some coins, tossed them, write down how many times that fills a lesson very nicely, and then work out the probability density ofthe property distribution off a coin or die or anything like that.
So the way of thinking about probability of school level, maybe a lower undergraduate level is this idea ofthe events that happened many, many times.
We count them and we look at the proportion of times things happen, how many times it heads, how many times it tails.
That's a perfectly reasonable way of thinking and reasoning about the real world, about probability.
But in terms of modelling in terms of statistics, actually more powerful way of thinking about it has expressed these things in a formulaic way that lets his reason with them.
Let's just take the product of two different probabilities.
That's not to think of counting on random events.
This is thinking about how things happen in the real world simulation.
We're going to take this other view, which is this base in view.
This is more powerful way of thinking about just the same thing If I toss this coin now, ask you to guess what have you got here? Heads or tails? The frequent this way of saying that would be well, we'll talk about it 100 times.
I'm gonna guess that 50 of them will be herds and 50 of them will be tails on average.
The Beijing and way of thinking about it would be at the moment.
This is half heads and half tails, 0.5000 point five taels.
It's in an indeterminate state.
Until we look at it, it's half heads and half tails.
It's in both states at once, and that expresses our degree of belief.
So we've got a 0.5 of the degree of belief that it's going to be hands on a 0.5 degree of belief that is going to be tails.
So in our mind, in our belief state, rather than saying it's heads and just being right half the time, we're going to say I half believe it's heads and I half believe it's tails.
I'm going to describe my beliefs not by picking one, not by guessing account out of lots of simulation runs was goingto have this mythical police state good of uncertainty so it could represent them.
My distribution in my mind, my beliefs about which way up this coin is is this Distribution was destroyed in a simple way.
That's going to be the probability off this random variable X, which is the very walls of the body of the coins.
But that's one.
Heads spell it right and that's tales and they're going to be no 0.5 got a distribution over the values.
It's a uniform distribution because I believe the coin is fair.
Let's 0.5 point five, and that's what I've got in my mind.
That's my model of how this queen behaves.
I don't need to toss it 100 times.
I just believe that its distribution.
So if we're learning things from data, we might indeed count things.
But our model way of reasoning, about things, our way of storing things, the parameters of the model are going to be in this framework.
This basic framework in the data does exist.
Data have individual points.
There is a number off.
Then we can count them.
We can look at their values, so we're gonna have a basically of the generative model we've got the model of A.
It generates observations from Class A and rather than saying right will generate 1000 things will look at their distribution Inside, the model is just going to be a model of the distribution that's going to be this continuously valued distribution.
The distribution is going to be Gossens.