We study again our die. You observe the following sequence 1, 2, 3, 2.

You presume correctly.

You mean his alleged suicide.

I guess that she is over thirty.

At this point, our robot has localized itself.

What this says is based on the outcome, we believe for the parameter µ.

We find that along the main diagonal our uncertainty has shrunk substantially.

I will now teach you about estimators.

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Supposed suicide.

like when you're trying to guess the number of M amp M's in a jar, but is also an essential skill in math and science, especially when dealing with what are known as Fermi problems.

likelihood. We want to have the most likely albedo and the most likely shape which could give rise to that image.

Let me ask you again. Suppose you make 111 coin flips.

And what is does is predict how many more species there are on land and in the ocean.

We try to evaluate what category that

In this sample, they might derive an estimate on how people are likely to vote.

I just want to tell you what you've learned, because you did amazingly well.

This Buddhist image cannot be dated exactly.

Let me give you an algorithm that defines value iteration.

W 0, where 0 is your iteration number, and then you up with it iteratively.

Also, there'd be more job opportunities there.

This is an enormous amount of work.

To do so, I assume that half of the people are of type A and half of the people are of type B.

How dare you presume to...

So if you were 10 you know from the beginning to you're either 50 or 10 if you are 10, then when you go to this guy, he would be able to deduce that he is 30.

Stop estimating backup size

Simple extrapolation.

Now, it may seem like we made a backwards step.

You really learned a lot about SLAM.

Obviously, it's 1 out of 4. It's 0.25.

This meant they must have been closer together in the past.

You learned about localization.

How would this affect our belief?

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Control estimates their descent at 100 meters an hour.

Estimated time of death? Coroner puts time of death somewhere between 10 and 1 last night.

Mapping is all about building maps of the environment.

You know, I bet you are too.

We find for day 0 we had rain, and maximum likelihood would just say the probability for day 0 is 1.

Even though our business is a little bit steadier than that would seem to imply.

And then we iterate.

With the appropriate definition of value, we would get the following formula, which is 77.

SLAM doesn't stand for slamming a robot.