Having defined the Bayesian network, let's look at some of the reasoning

patterns of work allows us to perform. So let's go back to our good old student

network with the following CPDs. We've already seen those.

So I'm not going to dwell on it. And let's look at some of the

probabilities that one would get if you took this busy network, produced the

joined distribution using the chain rule for busy network.

And now compute it say the values of different marginal probabilities.

So for example now we're asking what is the probability of getting a strong

letter, and we're not going to go through the calculation, because wanting to be

tedious to sum up all these numbers and I can just tell you that the probability of

the the of L1, is 0.5 but we can do more interesting queries.

So we can now condition on one variable. Remember we talked about conditioning of

probability distribution. And and ask how that changes this

probability. So for example, if we're going to

condition on low intelligence, we're going to use red to denote the false

value. And it's going to point to turn out that

the probability not surprisingly goes down.

It goes down to 0.39 because if the intelligence goes down, the probability

of getting a good grade goes down and so does the probability of getting a strong

letter. So this is an example of causal

reasoning, because because, intuitively the reasoning goes, in the causal

direction from top to bottom. We could also make things more

interesting. So we can ask what happens if we make the

difficulty of the course low and in this case, we have the probability of L1,

given i0 and b0. And what you expect the probabilities to

do, well, if it's an easy course, one would expect the grade to go up.

And sure enough the probability goes back up and we're back to 50/50, more or less.

Okay, so this is another example, of [UNKNOWN] in this case with a little

more, evidence. You can also do evedentual reasoning.

Evedentual goes from the bottom up. So we can in this case condition on the

grade and ask what happens to the probability of, of variables that are

parents or, or general ancestors of the grade.

So does it matter that this poor student takes the class and he gets a C.

Initially the probability that the class was difficult is 0.4 and the probability

that the student was intelligent is 0.3. But now with this additional evidence,

again this is not surprising, the probability that the, that the student is

intelligent goes down a fair amount but the other alternative hypothesis, that

the class is difficult also the probability of that goes up as well.

Hm. Now however there is an interesting type

or reasoning that is not quite as standard.

And that is reasoning that is called inter-causal because effectively it's

flow of information between two causes of a. of a single effect.

So remember we had the we're going to continue with the scenario before where

our poor student gets a C but now I'm going to tell you, wait a minute.

This class really is difficult so I'm going to condition on on v1 and notice

that the propability that the student his intelligence has gone up, it went up from

0.08 to 0.11 so that's not a huge increse and as you'll, see when you play around

with Bayesian networks, that often the changes in probability are somewhat

subtle. and the reason is that, you have to, I

mean, even in a hard class if you go back and look at the CPD it's kind of hard to

get a C, according to this model. which is that the students get a B.

and so now, have that the probability of high intelligence still goes down, it

goes down from 0.3 to 0.175 but now if I tell you the class is hard, the

probability goes up, in fact it goes up even higher than this, okay?

So, this is an illustration where this, where this intercausal reasoning can

actually make a fairly significant difference in the probabilities.

So intercausal reasoning is a little hard to understand, I mean she's a little bit

mysterious because after all, these are, I mean look at these you look at

difficulty you look at intelligence there's no edge between them how, how

would how would one cause affect another. So let's drill down into a concrete

scenario which is this one and just to sort of really understand the mechanism.

So, this is the most sort of purest form of intercausal reasoning.

Here we have two random variables x and, x1 and x2.

We're going to assume that they're distributed uniformally so each of them

is one with probability 50% and zero probability 50%.

And we have one effect one joined effect which is simply the deterministic oar of

those two parents. And in general we have the terministic

variable we're going to denote with these with these double lines.

So, in this case, there's only four assignments that have nonzero

probability, because, the value of Y is completely determined from, by the values

of X1 and X2. And so we have we have these four

distributions over here and now, I'm going to condition on the evidence y = 1.

Now let's look at what happened. Before I conditioned on this evidence,

the X2 were independent of each other, right?

I mean, look at this. They're independent of each other.

What happens when I condition on y = 1? Well we talked about conditioning.

This one goes away, and we have 0.33, 0.33.

0.33 or rather one third, one third. Okay.

In this probability distribution x1 and x2 are no longer independent of each

other. Okay.

Why is that? Because if I now condition on say x1

equals 0, then okay, so actually before we do that,

so that, in, in this probability distribution the probability of x1 equals

1 is equal to two-thirds and the probability of x, two equals one.