Here's the way the light field is expressed mathematically.

But just before I do that it probably makes sense to just draw them by themselves.

Is that. Putting these two, predictors together.

Distinct

We can just conclude that. This more complex model.

That's a bit unusual.

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A function will take you from one set of well, in what we've been dealing with one set of numbers to another set of numbers.

But again, always using color and personalized proximity to that information.

Consider this network, which is one for determining insurance for car owners.

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And the way I do that is just use the scale function and put it in front of every variable, every Y variable, every X variable.

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So, again the rows correspond to individual subjects, or cases.

So knowing the number of active years that someone has been engaged in physical exercise and their age, knowing that linear combination.

We don't actually modify what the variable is.

Let's say that you wanted to take the sum of 0 through 9.

Well, let's finish it off together.

We'll start off by creating some nodes with one node for every possible literal in the formula.

How much fruit are we going to end up with at the end?

It's the probability distribution of one or more query variables given the values of the evidence variables.

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Let me write this down.

First this variable had this value.

Another way to think about it is you can count the number of different values it can take on.

So that's the k different variables of let's say this, just to be concrete.

Thousands of them. Hundreds of thousands.

I'll deal with them personally.

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.

So now you're trying to pick us off one at a time,huh?

And we already said that all the numbers are greater than zero.

You could just view them as one variable.

1 if heads, 0 if tails

A transform will take you from one set of functions to another set of functions.

So we may look at one sample and see that two variables are correlated, but is that reliable.

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If you take it to the, take it to an extreme, if you put in hundreds of predictors, there's only so much variance each of them can uniquely explain.

Except this special klystron frequency modulator.

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