His teeth had gaps.

Here we create a new list called q, which is an empty list. There's nothing there.

Also the noise will disappear in these pores.

I'm going to look in my grammar for all rules c goes to something.

So the one on the right was done first, and then the one on the left was done over it.

And if you want to factor a p out of this, this is going to be equal to p times, if you take p divided p you get a 1, p square divided by p is p.

These two rules start at position 1.

And the sentences are P or not P, P and not P,

And then this term over here, this whole thing over here, is going to be plus p times 1 is p. p times negative 2p is negative 2p squared.

We start off in an initial state.

Let's say that this is our grammar

Once again, based on this P, I'm going to start bringing in rules 1 and 2.

Here is the solution.

F, L, E, P, T, P L, E, P, F, L, F, L, E, P, T, P, L, F, E, T.

APPLE.

Now in probability terms, people often write it as follows

What gap?

This helps keep the sliced cheese intact.

Let me just do this over here.

And so, having built a model of the probability of x we're then going to say that for the new aircraft engine, if p of x test is less than some epsilon then we flag this as an anomaly.

Staggered columns!

So chart state 0 includes the following parse states

Now let's add that pink p to both sides of this equation.

Stop up these chinks!

Close these crevices!

Detective Kerry weighed approximately 130 lbs.

like that plotted here, if you build a model, hopefully you will find that aircraft engines, or hopefully the model p of x will say that points that

So that cancels out.

So this is going to be equal to 1 minus p.

That's when it's easy to get them to fall for you!

Find the creases.

Ah, what about men with a gap between their front teeth?

If I get up there, I can find Hannah.

Something inching past the edge of reserve

We might.

Another way to think about that is let's say that we're in a particular state like this one

The first one looks pretty good. I just apply rule 1.

P of happiness given S and R times P of S and R, which is of course the product of those 2 because they are independent, plus P of happiness given not S R, probability of not as R plus P of H given S and not R times the probability of P of S and not R plus the last case,

P of A given B where B is the evidence and A is the variable we care about is P of B given A times P of A over P of B.

P

p.

What's next?

P .

P ... P ...?

Teacup!