And now I'm going to be really demanding.

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

That's largely influenced by the sample size.

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

At 10 they also just both multiply.

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

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

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.

Otherwise it doesn't work.

AutoOpt Molecule

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

If not, think about it and perhaps do the same quiz again.

Best

Optimized

Compaction

Optimize

Optimization

These were the results for Jeremy Lin.

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

Mean of the distribution

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

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

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

Let me just briefly summarize what we're doing.

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.

Always Best

Compact Folder

Compacting database

Let's practice this. I'll give you a couple of quizzes now.

These are often called multivariate gaussians.

Linear middle of the distribution

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

B Balance left and right volumes

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

Unknown Optimization

Here is an interesting data sequence 3, 3, 3, 3, 3.

In particular, consider a single data item as our sequence.

Since on average it didn't move, we just add the mean of its starting position to the no movement, and we get a mean of 0.

Mean value of the normal distribution

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

In the clustering coefficient of a graph, you have to sum all the nodes in the graph.

They'll fluctuate, right.

Bugfixes and optimizations

And so these are going to be three values drawn from a Gaussian distribution with mean zero and variance or standard deviation equal to one.