and kind of entered the math of Bayesian networks.

And so you should think of Bayesian networks

being a couple of things at the same time.

It's an inference procedure, something

that allows us to program with probabilities

and computing these probability distributions given

some evidence.

It's a very well engineered substitute

for working with the full joint probability distribution, which

is unwieldy and gets too big.

And it's also a graphical tool where you can just basically

look at the model of the world and see

whether it makes sense.

So we extensively looked at this example, which is up here.

We have a Bayesian network.

So you remember we have some causes.

We have an alarm that can go off.

If there's a burglary, that's what it's for.

And there's an earthquake, that's what it's not for.

And we have this remote supervision system

of the neighbors, which call if they hear the alarm.

And now the question is, John or Mary call you at work.

And now the question is, do you call the police or not?

OK.

And we have this very simple Bayesian network

that has the intuition that burglary and earthquake

caused the alarm, and the alarm causes the calls.

And we have certain probabilities

involved with this.

In the simplest case, on the roots,

we have the prior probabilities.

On the single inflow nodes, we have a very,

we have a very simple probability transformation.

If the alarm is true, then John calls with 90%.

And if it's false, he still calls with 0.5%.

And here we have this kind of conditional probability table

that tells me the probabilities of whatever the four

possibilities of what burglary and earthquake may be.

It's a very succinct and simple representation of the world.

And it tells me we have this conditional independence

situation here that we want to exploit.

And if we wish to Lydia Tommie below.

We can take a look at otherwise set of higher probability

table.

We weren't going to face a quake for a month.

We had to live in the widowhood,

where nefarious interior droughts

only Marketing onion crop they don't