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By now, we have defined the
notion of independence of

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events and also the notion of

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independence of random variables.

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The two definitions look
fairly similar, but the

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details are not exactly the
same, because the two

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definitions refer to different
situations.

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For two events, we know what
it means for them to be

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independent.

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The probability of their
intersection is the product of

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their individual
probabilities.

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Now, to make a relation with
random variables, we introduce

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the so-called indicator
random variables.

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So for example, the random
variable X is defined to be

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equal to 1 if event A occurs
and to be equal

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to 0 if event [A]

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does not occur.

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And there is a similar
definition for random variable

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Y.

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In particular, the probability
that random variable X takes

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the value of 1, this
is the probability

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that event A occurs.

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It turns out that the
independence of the two

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events, A and B, is equivalent
to the independence of the two

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indicator random variables.

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And there is a similar
statement, which

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is true more generally.

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That is, n events are
independent if and only if the

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associated n indicator random
variables are independent.

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This is a useful statement,
because it allows us to

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sometimes, instead of
manipulating events, to

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manipulate random variables,
and vice versa.

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And depending on the
context, one maybe

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easier than the other.

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Now, the intuitive content is
that events A and B are

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independent if the occurrence
of event A does not change

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your beliefs about B. And in
terms of random variables, one

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random variable taking a certain
value, which indicates

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whether event A has occurred or
not does not give you any

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information about the other
random variable, which would

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tell you whether event B
has occurred or not.

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It is instructive now to go
through the derivation of this

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fact, at least for the case of
two events, because it gives

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us perhaps some additional
understanding about the

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precise content of the
definitions we have

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introduced.

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So let us suppose that random
variables X and Y are

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independent.

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What does that mean?

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Independence means that the
joint PMF of the two random

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variables, X and Y, factors as a
product of the corresponding

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marginal PMFs.

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And this factorization must be
true no matter what arguments

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we use inside the joint PMF.

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And the combination of X and Y
in this instance have a total

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of four possible values.

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These are the combinations
of zeroes and

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ones that we can form.

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And for this reason, we have
a total of four equations.

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These four equalities are what
is required for X and Y to be

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independent.

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So suppose that this is true,
that the random variables are

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independent.

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Let us take this first relation
and write it in

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probability notation.

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The random variable X taking
the value of 1, that's the

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same as event A occurring.

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And random variable Y taking
the value of 1, that's the

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same as event B occurring.

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So the joint PMF evaluated at
1, 1 is the probability that

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events A and B both occur.

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On the other side of the
equation, we have the

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probability that X is equal to
1, which is the probability

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that A occurs, and similarly,
the probability that B occurs.

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But if this is true, then by
definition, A and B are

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independent events.

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So we have verified one
direction of this statement.

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If the random variables are
independent, then events A and

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B are independent.

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Now, we would like to verify
the reverse statement.

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So suppose that events A
and B are independent.

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In that case, this
relation is true.

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And as we just argued, this
relation is the same as this

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relation but just written
in different notation.

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So we have shown that if A and
B are independent, this

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relation will be true.

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But how about the remaining
three relations?

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We have more work to do.

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Here's how we can proceed.

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If A and B are independent, we
have shown some time ago that

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events A and B complement will
also be independent.

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Intuitively, A doesn't tell
you anything about

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B occuring or not.

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So A does not tell you anything
about whether B

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complement will occur or not.

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Now, these two events being
independent, by the definition

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of independence, we have that
the probability of A

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intersection with B complement
is the product of the

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probabilities of A and
of B complement.

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And then we realize that this
equality, if written in PMF

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notation, corresponds exactly
to this equation here.

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Event A corresponds to X taking
the value of 1, event B

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complement corresponds to
the event that Y takes

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the value of 0.

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By a similar argument, B and
A complement will be

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independent.

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And we translate that into
probability notation.

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And then we translate this
equality into PMF notation.

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And we get this relation.

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Finally, using the same property
that we used to do

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the first step here, we have
that A complement and B

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complement are also
independent.

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And by following the same line
of reasoning, this implies the

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fourth relation as well.

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So we have verified that
if events A and B are

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independent, then we can argue
that all of these four

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equations will be true.

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And therefore, random variables
X and Y will also be

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independent.