At this point, our robot has localized itself.

Here is our sequence. There's a couple of sunny days 5 in total a rainy day, 3 sunny days, 2 rainy days.

So, here's another quiz.

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

Here we had one probability to estimate.

This one, this one, and one out here, and I put a little check marks underneath.

So 55 say A and 45 say B.

You wrote an algorithm that implements what's called Markov localization.

We asked them to estimate their likelihood of experiencing different terrible events in their lives.

For example, for the prior probability, we found that 3 8 messages are spam.

If you understood this, you understand probability, and you understand localization.

So what do they want to know?

let me explain it to you.

Attention. Inbound dradis contact, rated highly probable enemy fighter.

So this unit is a tough one.

Let's now dive in and understand how to use this cryptic name maximum likelihood estimator.

The word perfect occurs in movie, but the word storm has never been seen before.

And it makes much more sense to talk about the probability or random variable equaling a value, or the probability that it is less than or greater than something or the probability that is has some property

Then you wrote a piece of code that used the measurement to turn this prior into a posterior, in which the probability of the 2 red cells was a factor of 3 larger than the posterior of the green cells.

For is, to be 1 9, but for today, it's 0.

In a deterministic action, it obviously succeeds, unless of course we run into a wall.

The next question a lot of people had was does this method we're using depend on the robot already having a map of its environment?

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

Then the value of the state for the action go up would be obtained as follows.

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Supposed suicide.

That it's well true that it could easily happen that p doesn't end from µ and that's because flipping the coin number of times is a stress and estimate of a true probability p.

So, this probability is equal to the product over all i of the probability of words of i given all the subsequent words. So that would be from word 1 up to word i 1.

So how do I think about that?

In particular, what if I use an unbiased coin like this one?

Probability

Phil, the odds against

You might have heard about Bayes Rule before.

That means X equals 1 over 1.5, which is 2 3.

The fact is that the thing we programmed here captures the key of the probabilistic inference necessary to localize the Google care.

What is going to happen to the Markov chain over here? What is that probability?

So the way that we would refer to this is the probability of both of these happening

So let me delete this.

What's the probability of Y?

This is called total probability.

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

What are the odds?

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