And if you look at the end result, our estimate is almost exactly 11, which is the result of 10 1.

And fifth, the element of uncertainty.

Yeah...

We talked about planning, but not under uncertainty, and you've had many, many classes of under uncertainty, and now it gets to the point where we can make decisions under uncertainty.

So it's 0, 0, 0, 0 please don't change it.

Zero to 10. How high? 10.

Then you arrive at a prediction that adds the motion of command to the mean, and it has an increased uncertainty over the initial uncertainty.

No neat packages of faith, with Bible references to prove them.

We have the same needs.

Obviously trial and error is very important.

A gaussian with a small amount of uncertainty might look like this.

First, it can only handle true and false values.

We've surrendered our power, trading off our discomfort with uncertainty for the illusion of certainty that they provide.

In this class so far we talked a good deal about planning.

I will find myself in the same state as before.

Probabilities are the cornerstone of artificial intelligence.

Kind of exciting.

I initialized both with 0 because I don't know the actual location and the velocity.

And then the next update comes in at 6, and it gives us a measurement of 5.99 and now a reduced uncertainty of 2.39.

So I love this picture, there is so much in it.

Before I do this, please help me filling out some other probabilities that are actually important.

By the end of this course, you're going to know the basics of data analysis, uncertainty in measurement and how to design an experiment, and of course, some of the important concepts in physics for example, the conservation of energy.

The velocity estimate is correctly 1, which is .99999, and I have yet another uncertainty matrix which illustrates

This all would work out really well if the initial estimate was 5, but we're setting it to 0 with a very large uncertainty of 10,000.

It's a body world, and the species really exists in the body.

So today is an exciting day.

For my first question, I'll assume P equals 1.

So that was the word model . What about probabilistic ? The word

Data corruption at critical risk.

And I want to tell you when

This is 1.24 to the 10th, but a really small uncertainty, even smaller than this one over here.

It's 4.98.

And then we go to move to the right again by 1, which makes the prediction 6.99.

You might look at the image of a person and say it's a person.

At this point unconfirmed reports Indicate that

We update our position to be 2.666, which is now a jump away from 1, and we reduce our uncertainty.

We now apply the motion of 1. We get to 5.9.

We've seen how a search can discover sequences of actions to solve problems.

There's all kinds of reasons that you fall in love with one person rather than another.

It doesn't matter what kind of spin you put on it come on!

If you're tracking really well and all the particles are centered in one location, there isn't really a need to have as many around as when you're globally uncertain.

You then predict that you add 1, but the uncertainty increases to 5.99, which is the motion uncertainty of 2.

That gives me my new x.

It might be possible to have a fairly small uncertainty in 1 dimension, but a huge uncertainty in the other.

You can actually derive what the centrifugal noise in the sensor and characterize those.