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

And then I'll observe again the 2.

I'm giving you a skeleton code. This is the same update function as before.

These are old faculty salaries.

And it goes over here?

The arrow calculation, the matrix S with a transpose, the Kalman gain K with the inverse, back to my next prediction and my measurement update, and this is the prediction step.

Is that good or bad?

This could be the cause. This could be the cause. This could be the cause.

Machines, on the other hand, are deterministic.

I say again, in error predicting the fault.

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

Forecast

Every time I use them, I just look them up.

Bush count 'em again ... You can violate your expectations about experience.

Is it the problem history? Diagnostic information?

It's structured as an X coordinate, a Y coordinate, and also a heading direction, and these 3 values together comprise a single guess, but a single guess is not a filter.

First, I want you to know if I make this modification what is the effect on x2?

Do research where two models are competing against one another.

Anticipation.

And in this example, I wanna use working memory capacity to predict, SAT.

You can read the weather, you can predict the price of heating oil.

You obviously do what the precautionary principle suggests, you try to anticipate it, but after anticipating it, you constantly asses it, not just once, but eternally.

Maybe its growth rate slows a little bit.

Alright, lets turn to Iran.

Your uncertainty shrinks to 3.99, which is slightly better than the measurement uncertainty.

Give me novelty, give me familiarity.

We can violate your expectations about shape.

Again, we wanna minimize the residuals.

That's a bit unusual.

In Kalman filters we iterate measurement and motion.

If we plug in the nth measurement and the measurement uncertainty, it does the same with the motion, the prediction part over here.

Here are my best guesses. Okay?

Generally speaking, past fixes and a bit less so past changes are good predictors for future fixes.

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.

The other equation is the prediction equation, so let's go from X1 to X2.

Contrast this with a child with autism playing the same game.

Try to feel his next movement.

Take upstairs.

We're expecting resistance

If they understand which way, say, a blown tire affects their dynamics. There has been a lot of work on this.

And at this point, we decided

We have no idea what he'll do when he gets in that room with Burnett.

And they estimated that in 2008,

Formally, this operation of bringing in everything that c could become, because we're expecting to see a c, is known as predicting.

That's arbitrary.