Well, that helps but it's not enough by itself.

large scale structure.

X, the distances don't either get greater or they don't get lower.

So by taking this sample you can say that we've randomly sampled a T statistic from this 9 degree of freedom

So we plot data for sample on this.

Thrun So we're now ready to define Bayes networks in a more general way.

This is our T distribution.

Actually, in all cases in this particular set, which is not chosen randomly.

T statistic that we could derive from this, and you can view this T statistic as being a random sample from a

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

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

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

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

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

So first let's revisit this normal distribution.

Let me ask the hard question now.

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

Dispersion of the normal distribution

Then that corresponds to a

Mean of the distribution

Your purse?

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

Look at the new locations. Texas,

This tells you the height of the normal distribution function.

0 density, 1 distribution

Here is an example graph of 5 variables, and this Bayes network defines the distribution over those 5 random variables.

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

What I want to do in this video is to generalize it.

Now, 4th property I wanna point out is that you typically want the output to be well distributed.

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

T distribution right over here.

So, Steven Spielberg, eat your heart out.

Randomize

Linear middle of the distribution

Standard deviation of the distribution

Degrees of freedom for the t distribution

You learned something really important.

Intuitively speaking, this is the case because we actually gain information.

Random Cards

Random Transition

Random

Random HSV

So let me draw a

And I'm just showing this to you again because it's so nice.

But if everything is symmetric then there'll be infinite forces pulling in all directions in same way so you would still feel no net force.