You don't need to list the consequences, Ethan.

It is difficult to calculate the results of the election.

Here, I'm mostly just assuming still observational studies.

And so we have to figure out how they balance those two things.

Of course, in practice, I don't know the velocity, but let me assume for a moment the velocity is 0.

First, we reached line 9, then we step we reached line 11, then we step we reached line 13, and then we quit and this is precisely the output we would have expected.

First, we'll use the less than to compare 2 and 3.

I was so happy.

But watch out they continue the line beyond the last point out into nothing.

I'll use'X' and'Y' as I've been for the predictor and outcome respectively.

Now that you worked the math, you know exactly how to implement this.

And I think the CEO basically did not understand the importance of meaning.

And let's say when their tape measures weren't so good,

And all of these can influence your measurements,

So we could have multiple X's.

And the reason that we need to use science is because then we can reproduce what we're doing it's not just wisdom or guesswork.

In our context, of course, we're using a theory in order to explain what the cause of a failure is and in our context we're not looking for vague guesses, we're looking for something that explains all earlier observations and predicts the future one.

We click on run, and we get 1.0.

The motions are 1., 1., 2., 1., 1.

There's also a measurement update step where we use the measurement z.

We can't predict any particular project, but the result of this whole worldwide, chaotic, unpredictable activity of competition and the evolutionary process of technology is very predictable.

Here is the Kalman filter procedure.

So, observe variables, and some people will call these manifest variables are represented by rectangles.

While you can still deterministically move your pieces, the outcome of an action also involves throwing of the dice, and you can't predict those.

So first, let's talk about just what regression is.

You move 1. You measure 10, and you move a final 1.

For example, a number of respected economists did not expect the financial collapse of 2008.

So keep it simple. There's just four variables.

And we'll assume certain values, like what's the mean of that distribution? And then we'll conduct say, an experiment.

These are old faculty salaries.

So, there is a lot of water below the surface.

Each of these cells or each of these boxes in a grid are a possible outcome.

What results do you anticipate?

So, how do we untangle that positive correlation in trying to predict the outcome measure?

However many people we have on our study, or however many cases, that's how many predicted scores we have.

Is that good or bad?

Let's go see.

An unexpected result came about.

It makes it adversarial it's polarizing.

Different people trade off between standing by their outcome, faithfully holding to it, going down in a blaze of glory, or giving it up, putting their finger in the wind, and doing whatever they think is going to be a winning position.

Now we just repeat. We look at the next measurement. We weight particles accordingly.

So the expansion of space is actually speeding up.

And your doctor wishes to understand what the reason for your symptoms and your sickness is.

Very correctly, then it would most likely assign this position to the right most cell as should be, given the sequence of observations over here.

And what we say is our new belief state is the result of updating what we get from predicting what our action will do and then updating it, based on our observation, O, of the world.