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

Suppose I use the following Gaussians μ 10, σ 2 4, ν 12, r 2 4

And now I'm going to be really demanding.

So those would be the high peaks of the density field.

At the end of the month we count your clicks

That's largely influenced by the sample size.

In this case it's the same, and in this case it's 0.

Pie jaws are often used to hold thin wall parts that will deform easily without full support

At 10 they also just both multiply.

And we said at 1.70, people are willing to eat two million cupcakes in a year.

Compute for me the mean and the variance using the maximum likelihood estimator I just gave you.

I'm giving you a skeleton program, which has a function update, that takes as an input a mean and a variance for the first distribution and a mean and a variance for the second distribution and outputs the new mean and the new variance of the product of those.

Think about all the fast food chains, clothing boutiques, or mobile phone kiosks at the mall.

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The variance is a measure of how far the data is spread.

How did we keep track of time without a clock?

The prior has a mean of mu and a variance of sigma squared.

In the 1 dimensional case, it is mu and sigma squared.

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There are 4 possibilities 0 and 0, 1 and 0, 0 and 1, and 1 and 1.

These were the results for Jeremy Lin.

I finally want to teach you about the concept of a standard score.

These are often called multivariate gaussians.

And yes, it's as easy as this. We just add the two means and the two variances.

Can I change the order and always get the same result?

This is one possible, events in which a is true divided by well, let's say, equally probable.

This point and the regression line, that's the prediction error or residual for that individual.

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So how many equally likely possibilities?

But there's no reason I could expect it.

If the probability distribution of x is Gaussian, it would mean Mu and variant sigma squared, then we'll write this as x the random variable tilde.

I've got in my example minus the mean, minus the center, minus the mean of distribution.

So there's about 86 billion neurons in our brain.

And again, start to embrace this notation, this is gonna be very ah, common, and we'll use it a lot as we move forward through the course.

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

Let me just briefly summarize what we're doing.

But a lot more, going forward in the class.

Cutting a cake into equal pieces is rather difficult.