He is weighted down with various cares.

Score Weighted

Rating Weighted

weighted saturation

Center weighted average

They're all weighted equally.

We have a weighted mean over here.

Then the new mean, mu prime, is the weighted sum of the old means where mu is weighted by r squared and nu is weighted by sigma squared normalized by the sum of the weight factors.

You can see the curve is obviously heavily left weighted.

It's the probability weighted sum of the squared distances from the mean.

That means that nu is weighted much, much larger than the mu.

Remember, that is the weighted sum of the squared distances from the mean.

For the weighted average we get 2 times 10 plus 8 times 13.

Well that's just the probability weighted sum of the values that this could take on.

That's all I'd like to say about shortest paths and weighted graphs in this unit.

You could have that situation where maybe the six sided is weighted a little bit more.

The problem is that each timing is a different duration so it needs to be weighted differently.

Well, we would take a weighted average of these indices, or you could say a weighted average of the growth, you could do it either way, so let's do this either way to give you the same result.

The topic of unit 5 is weighted graphs. I'd like to start up with a little magic trick.

They could be weighted by constants like plus 1 or minus 1, and they could be added up.

Finding the shortest path in a weighted, undirected connected graph took time m times the logarithm of n.

Down, down, down.

Down, down, down.

Down. Down. Down.

And by rating it and by rating the different people who submit information, we can get refined results and weighted results.

Down, down!

Skinning is well suited to the GPU, as the vertex shader can transform plates multiple times and add together the weighted locations.

Settle down. Settle down people. Settle down.

We had talked about shortest paths in a graph, but now let's extend this notion to the shortest weighted path in our graph.

Sit down, sit down!

Man down! Man down!

Sit down, sit down.

Sit down! Sit down!

Lie down, lie down

Guns down! Guns down!

Settle down. Settle down.

Man down! Who's down?

Well, let's say I wanted to take the Laplace transform of the sum of the we call it the weighted sum of two functions.

And the stock tends to go down, down, down.

Sit down. Sit down, Keller.

Sit down, gentlemen. Sit down.

Down, down. Now, move that.

Sit down. Sit down, Crispina.

The mean is obtained by the weighted mean of the x js normalized by the sum over e ijs and finally the sigma is obtained as a sum over the weighted expression like this and this is the same expression as before and now again we are normalizing over the sum over all e ijs.

Down