And then, let's just divide both sides by a.

The house has such a lovely position.

Offered differently, 2 variables that are independent may not be in certain cases conditionally independent.

Did he offer terms?

least maybe it doesn't make intuitive sense just yet, but I showed you that the probability of a and b is equal to the probability of a given b times the probability of b.

But you can see this is a generalisable thing.

And we get this, the probability of b given a is equal to this, probability of a given b.

That is just total probability.

Conditions

Condition

Criteria

I have a condition.

The bill?

So you can figure out the answer by intuitive reasoning, or you can figure it out by going to the conditional probability tables and seeing according to the definition of conditional probability whether the numbers work out.

They're just not connected at all.

Invert Condition

condition expected

Let's ignore that.

And I can negotiate the terms of their relocation.

Check Condition

Condition

Interviewer No. No conditions.

On one condition.

Let me just say it a little bit abstract.

I fix the terms.

Add this filter condition to the list

Okay, it's a deal.

Here I simply write 3 dots for the text Perfect Storm.

People build one after the other, after the other, after the other.

That causes these 2 variables to be conditionally independent.

Now remember, you're buying for this fantastic neigorhood.

In this case, R and T.

In our last lecture, we already had met the assert keyword from Python.

Filter Criteria

Preconditions

Matching Condition

Parameter type

Initial Conditions

Search Criteria

The condition for the match

Precondition

Precondition

Postcondition

Criteria

First Condition