First, I want you to remove the outliers by only extracting data between the lower and upper quartile.

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Go ahead, eat.

We're talking about real jail time.

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

Next time, make an appointment.

F gets 20 and E gets the 17 and not only is that the first number written in E but it is the final one because the very next thing we lock down is E's distance.

Here we had one probability to estimate.

So what PlPA and SOPA risk doing is taking a centuries old legal concept, innocent until proven guilty, and reversing it guilty until proven innocent.

Today, we've got great technology we have GPS.

So I always just check for one case, and the default case will just be off .

This controls how strictly to match the given value.

Now, I do have a quick quiz for you.

Delicious until proven guilty.

Because the laws are very specific.

That's exactly what we're gonna do in inferential statistics.

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

I thought the law said a man was innocent until proven guilty.

What I mean by square is the result of multiplying the input number by itself.

Can you clearly define this word?

Do you want to check your settings and try again?

So just to conclude what are the take home messages from this?

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.

At this point, our robot has localized itself.

let me explain it to you.

It is definite that he will go to America.

So how do I think about that?

Using our protocols... the results are conclusive.

So sigma is called the standard deviation and it, so it specifies the width of this Gaussian probability density whereas the square of sigma, so sigma squared, is called the variance.

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.

You've heard of entropy.

This has nothing to do with total probability and all with Bayes Rules, because I'm talking about observations.

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

So let me delete this.

What's the probability of Y?

I am not yet sure of that.

Before we're done with localization, I'd like to talk about robot motion.

There is no significant scientific dispute about that.

So what i have drawn, the non dotted lines this right here is an odd function and you could even look at the definition.

And we can guarantee a clean, blue sky or an uncertain future with nuclear power.

What Graph SLAM does is defining our probabilities using a sequence of such constraints.