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In this segment, we
point out and discuss

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some important but also
intuitive properties

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of conditional expectations.

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The first property is the
one that is written up here.

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What is the intuitive meaning?

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If you condition on Y, then
the value of Y is known,

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and so g of Y is also known.

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There's no randomness in it.

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It can be treated as a
constant, and therefore it

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can be pulled outside
the expectation.

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So that's the intuition.

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How does one establish
such a result formally?

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Let us take the discrete case.

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So let us assume that X
and Y are both discrete.

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What does it take to
establish a fact of this kind?

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We want to show that two
random variables are equal,

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and that amounts
to the following.

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We consider an outcome
of the experiment,

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and we want to
show that whatever

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the outcome of
the experiment is,

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these two random variables
will be the same.

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So let us consider an outcome
for which the random variable,

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Y, takes a specific
value, little y.

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And of course, this has to
be a specific little y that

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is possible.

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Otherwise, conditioning on that
event would not be meaningful.

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So if an outcome has this
value for the random variable,

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Y, then what does the
random variable do?

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This is, by definition,
the random variable

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that takes the
value-- expected value

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of g Y X, conditional
expectation,

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given that capital Y
took on this value.

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This was our definition
of the concept

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of the abstract
conditional expectation

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as a random variable.

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This is the random
variable that takes

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this specific numerical
value whenever

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the random variable, capital
Y, takes the value, little y.

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And similarly, if the
random variable, capital Y,

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takes the value, little y,
this random variable here

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is the expected value of X,
given that Y is little y.

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And when capital
Y takes the value,

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little y, this
function, g of Y, takes

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on this particular
numerical value.

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So we want to show that
these two expressions

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we will be equal no
matter what capital Y is.

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Now, when we place ourselves in
a conditional universe, where

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capital Y takes a
value, little y,

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then the joint PMF
of X and Y gets

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concentrated on those
values of capital Y

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that obey this relation.

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So conditioned on
this event, capital Y

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is, with certainty,
equal to little y.

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Therefore, this
random variable here,

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in the conditional universe,
is the same as this number.

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But now, since this
is a number, it

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can be pulled outside
the expectation.

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So we have concluded that
for any outcome for which

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the random variable, capital
Y, takes this specific value,

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little y, this random
variable takes this value.

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This random variable
takes this value.

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They are the same.

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So no matter what
the outcome is,

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these two random variables
take the same value,

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and therefore they are
the same random variables.

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Now, this is a correct proof
if the random variables

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are discrete.

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If the random variables
are continuous or general,

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then carrying out a rigorous
proof is actually quite subtle,

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and it is beyond our scope.

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However, the intuition
is still correct,

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and the result is correct.

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And we will be using it
freely whenever we need to.

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Let us now move to a
second observation.

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Suppose that h is an
invertible function.

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What does that mean?

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That if I give you
the value of h,

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you can tell me the
value of the argument.

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So in some sense,
Y and h of Y can

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be recovered from each other.

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If I give you Y, you
can calculate h of Y.

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But also, if I give you h of Y,
you can figure out what Y was.

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An example could be
the function, h of Y,

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equals Y to the third power.

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If I tell you the value
of Y, you know Y cubed.

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But if I tell you Y cubed,
you can also figure out Y.

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So Y and Y cubed carry
exactly the same information.

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In that case, the
conditional expectation--

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what you expect, on the
average, X to be-- if I tell you

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the value of Y, should be the
same as what you would expect

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X to be if I give you the
value of, let's say, Y cubed.

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In both cases, I'm giving you
the same amount of information,

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so the conditional distribution
of X should be the same.

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And the conditional expectations
should also be the same.

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So this is, again, a
very intuitive fact.

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How do we verify that
this fact is true?

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Using the same method as before.

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So fix some particular
outcome for which

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the random variable, capital Y,
takes a specific value, little

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y.

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When that happens,
this random variable

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will take this value here.

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That's just by the definition
of conditional expectation.

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This is the random variable
that takes this value whenever

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capital Y happens to
be equal to little y.

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In that case, we also
have that h of capital

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Y takes on a specific
value, h of little y.

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When this random variable
takes this specific value,

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this random variable here will
take a value of this kind.

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So this is the
random variable that

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takes this value
whenever h of capital Y

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happens to be this
specific number.

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But now, the event that h of
Y takes this specific value,

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because the function,
h, is invertible,

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is identical to the event that
Y takes that particular value.

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And so, since this event
is identical to that event,

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the conditional probabilities,
given this event,

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would be the same as the
conditional probabilities given

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that the event.

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And therefore, the
conditional expectations

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would also be the same.

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Once more, this
is a proof that's

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entirely rigorous
if we are dealing

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with discrete random
variables, although

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in the continuous
case, there could

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be some subtleties involved.

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However, the result
is true in general.

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The technical details
are beyond our scope.