Those values are discrete.

It could be 9.58. We can actually list them. So in this case, when we round it to the nearest hundredth, we can actually list of values.

You have discrete random variables, and you have continuous random variables.

This is the first value it can take on, this is the second value that it can take on.

Y is the mass of a random animal selected at the New Orleans zoo.

The exact, the precise time that you would see at the men's 100 meter dash.

What we're going to see in this video is that random variables come in two varieties.

And I want to think together about whether you would classify them as discrete or continuous random variables.

While continuous and I guess just another definition for the word discrete in the English language would be polite, or not obnoxious, or kind of subtle.

It's 0 if my fair coin is tails.

And we know that our variance is essentially the probability of success times the probability of failure.

Anyway, I'll let you go there.

Once again, you can count the values it can take on.

Most of the times that you're dealing with, as in the case right here, a discrete random variable let me make it clear this one over here is also a discreet random variable.

Discrete

That's my random variable Z. Is this a discrete random variable or a continuous random variable?

Suppose we multiply two Gaussians as in Bayes rule a prior and a measurement probability.

There's no way for you to list them.

Diffuse

Eccentricity

looks the data has a very high probability of being in the central region, low probability of being further out, low probability of being further out, and so on.

Then the value of the state for the action go up would be obtained as follows.

I gave you the formulas for how to compute the mean of the outcome.

Probability

Phil, the odds against

There's a certain amount of randomness involved for the outcome of dice, and therefore, we call this stochastic.

Discretely of course, ma'm.

But if you can list the values that it could take on, then you're dealing with a discrete random variable.

What are the odds?

Let's think about let's say that random variable Y, instead of it being this, let's say it's the year that a random student in the class was born.

Let's use the Laplacian smoother with K 1 to calculate the few interesting probabilities

Probability of success

Probability of failure

And discrete random variables, these are essentially random variables that can take on distinct or separate values.

Go to Germany, find him. Brother's dead. The family is lost.

And these are mutually exclusive events, you can't have both of them

The smallness of that probability is what we mean by extremity.

You might say OK, that's the probably of getting exactly 1 times the probability of getting 2 out of 3 plus the probability of getting 3 out of 3.

We now apply Bayes rule.

And here is my answer. You can really read off the formula that I just gave you.

What's the probability of the joint X, Y?

If I pick a random T value, if I take a random T statistic

There's going to be a 10 percent chance you get a pretty good item.

Now let's have something a little bit more interesting.

Frack the odds.