And as you can see, they're very non uniformly distributed.

The gaussian will look something like this.

That's called correlation.

Such a representation of probability over spaces is called a histogram in that it divides the continuous space into a finite many grid cells and approximates the posterior distribution by a histogram over the original distribution.

So first let's revisit this normal distribution.

Let me ask the hard question now.

Dispersion of the normal distribution

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

Mean of the distribution

Your purse?

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

And so therefore, their kinetic energy can be characterized by a velocity dispersion.

This tells you the height of the normal distribution function.

0 density, 1 distribution

And, there's a standard deviation of the distribution of sample means, standard air.

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

In this video, I'd like to talk about the Gaussian distribution, which is also called the normal distribution.

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

Linear middle of the distribution

Standard deviation of the distribution

Degrees of freedom for the t distribution

You learned something really important.

Secondary loadsharing unit is out.

Putting it in population parameters, but putting it in sample statistics.

We're all unwitting victims of a collective blind spot.

Mean value of the normal distribution

Middle value in the normal distribution

Let me make it very clear.

Then that corresponds to a

It uses the basic magnetic force of a charged particle to measure the mass and relative concentration of atoms and molecules.

And I have to explain to you, the top of this graph shows you that frequency distribution that I talked about.

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

Organic fruit. candles.

I have a plane here.

A diversion, like on Hogan's Heroes.

If you remember from lecture two when we started to talk about probability, we can use the normal distribution.

Part machine, part organic.

The eigenvectors happen to be 0.7064 and 0.7078 with an eigenvalue of 4.004, and the 2nd one is orthogonal with an eigenvalue much smaller.

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

MV is the linear middle of the distribution.

STD is the standard deviation of the distribution.

Standard deviation of the normal distribution

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

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

It's one standard deviation in the distribution of sample means. So this distribution of sample means, again.