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

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

And similarly I'm going to assume that X2 is distributed, Gaussian, that's what this little tilda stands for, that means distributed Gaussian with mean mu2 and Sigma squared 2, so it's distributed according to a different Gaussian, which has a different set of parameters, mu2 sigma square 2.

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

Suppose you're doing many experiments.

So first let's revisit this normal distribution.

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

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

Let's now model a second motion. Say x2 is 5 apart.

May be distributed in accordance with the GPL.

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

You'll see the normal distribution or Bell curve repeatedly throughout this course ah, but that doesn't mean that all variables are distributed normally.

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

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

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

The way to look at this is any value x has this probability up here.

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.

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

Told her I was taller when I stood on my wallet.

50 of the distribution falls below the mean, 50 falls above. It's perfectly symmetrical.

Now we need to find out the standard deviation of this.

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.

So a T distribution looks very similar to a normal distribution but it has fatter tails.

luminous matter changes is a function of mass.

Dispersion of the normal distribution

You manipulated a probability distribution over places into a new one by incorporating the measurement.

So, Steven Spielberg, eat your heart out.

Then that corresponds to a

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

Mean of the distribution

Your purse?

In the next video, I'll talk a bit about the Gaussian distribution and review properties of the Gaussian probability distribution, and in videos after that, we will apply it to develop an anomaly detection algorithm.

Of 100. Now it's looking pretty close to the population.

The distribution shows.

So, this script's end stands for normal, since Gaussian and normal distribution, they mean the same phase of synonymous and a

Look at the new locations. Texas,

Here's another challenge this time you are a golfer.

Now in theory, you could say you can use a hypothesis test if you recognize that it's not normally distributed and do the appropriate binomial test and figure out that you can't determine anything but it's generally not adding anything over intuition.

This tells you the height of the normal distribution function.

In this case, we run it twice. We print the output.

0 density, 1 distribution

Each place had a probability of 0.2.

And the parameter estimation problem is, let's say I suspect that these examples came from a Gaussian distribution so

Are they interconnected?