little higher than if you do it, uh, orally. So we looked at this histogram before.

So I can get any value less than 20 given this distribution.

Let's just go back to our example and see what an amazing thing you've just programmed.

So, you can get into the more interesting version of state spaces, but we're not confined to unimodal distributions.

Let me ask the hard question now.

Measurement Update So question 1 is measurement update.

Finally the shape of this distribution of sample means will be approximately normal even if the distribution in the population isn't normal.

The histogram is a mere approximation for this continuous distribution.

Down here, I can normalize aux to have a total probability of 1, and then I return it.

This tells you the height of the normal distribution function.

So let me draw a

That is manifest by the fact that after motion, the probability distribution is a little bit flatter and a bit more spread out. and after sensing, it's focused a little bit more.

Just as before, begin with a uniform distribution as you always do in global localization.

Let me make it very clear.

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

The Bayes network, as we find out, is a complex representation of a distribution over this very, very large joint probability distribution of all of these variables.

No, you will die. 50 percent will die within 24 hours, if not treated.

So here I made a distribution builder where instead of showing numerical outcomes,

Here, I said samples equal to 30 or greater, some people would be a little more liberal then that and say if you have samples of even twenty or greater you'll get a normal distribution.

What you expect is basically what you observe.

So first let's revisit this normal distribution.

It was, they were all over the place.

In addition to this, I will use a very simple p, that has a 1 at the second position and zeros elsewhere.

When I've done this as many time as I have measurements and motions,

If we subtract from x1 x0 and 10, put this into a squared format and turn this into a Gaussian, we get a probability distribution that relates x1 and x0.

Thus, I suspect that each of these examples was distributed according to a normal distribution or

Since this was so simple, let me quiz you.

And actually when you look at the impacts, the impacts are substantial.

So, the answer is going to be a complete, joint probability distribution over the query variables.

This is the formula for the Gaussian again.

And so to actually calculate this, what I do is I take the cumulative distribution function of this point and I subtract from that the cumulative distribution function of that point.

If you solve this, this gives us 0.4X 0.5 or put differently, 0.6X 0.5.

What is the youngest person to earn 267,000 in our data set?

lecture. That is, what task or test do we administer to get an intelligence score?

For large N, say N large N 30, we know that the distribution of µ you might observe is Gaussian.

The questionnaires were distributed at random.

Then we have a Gaussian distribution that is centered around zero, because that's Mu.

And your new sigma square is your old sigma square plus a variance of the motion Gaussian.

What we're going to do, is assume that the feature,

People talked about it twice less as it should have been.

Here are our equations again.

Here's the trick.

And what we find if we plot up the well known species is this sort of a distribution.

But in summary, we have a Gaussian over here, we have a Gaussian for the motion, with U as the mean and R square as its own motion uncertainty, and the resulting Gaussian in the prediction step just adds these 2 things up mu plus U and sigma square plus R square.

large then the joint distribution of the sum of those with approach a function it looks like this.