Existence and Essential Uniqueness of Conditional Expectation Conditioned on Sigma-Algebra

Theorem
Let $\struct {\Omega, \Sigma, \Pr}$ be a probability space.

Let $X$ be an integrable random variable on $\struct {\Omega, \Sigma, \Pr}$.

Let $\mathcal G \subseteq \Sigma$ be a sub-$\sigma$-algebra of $\Sigma$.

Then there exists an integrable random variable $Z$ on $\struct {\Omega, \GG, \Pr}$ such that:


 * $\ds \int_G Z \rd \Pr = \int_G X \rd \Pr$ for each $G \in \mathcal G$.

Further, if $Z$ and $Z'$ are two integrable random variables satisfying this condition, we have:


 * $Z = Z'$ almost everywhere.

Proof
First take $X \ge 0$.

Define a function $\mu : \GG \to \R$ by:


 * $\ds \map \mu A = \int_A X \rd \Pr$

for each $A \in \GG$.

From Integral of Integrable Function over Measurable Set is Well-Defined, this is well-defined.

From Measure with Density is Measure, $\mu$ is a measure.

Note that if $\map \Pr A = 0$ for $A \in \GG$, we have $\map \mu A = 0$ from Integral of Integrable Function over Null Set.

So $\mu$ is absolutely continuous with respect to $\Pr \restriction_\GG$.

So by the Radon-Nikodym Theorem, there exists a $\Pr \restriction_\GG$-integrable function $Z \ge 0$ that is $\GG$-measurable such that:


 * $\ds \map \mu A = \int_A Z \rd \Pr \restriction_\GG$

for each $A \in \GG$.

Then from Integral of Positive Measurable Function with respect to Restricted Measure, we have:


 * $\ds \map \mu A = \int_A Z \rd \Pr$

for each $A \in \GG$.

That is:


 * $\ds \int_A X \rd \Pr = \int_A Z \rd \Pr$

for each $A \in \GG$.

Finally, we have that:


 * $\ds \int Z \rd \Pr = \int X \rd \Pr < \infty$

so $Z$ is integrable.

So to conclude, $Z$ is a $\GG$-measurable integrable random variable with:


 * $\ds \int_A X \rd \Pr = \int_A Z \rd \Pr$

for all $A \in \GG$.

Now take $X$ a general integrable random variable.

Then from Positive Part of Real-Valued Random Variable is Real-Valued Random Variable, Negative Part of Real-Valued Random Variable is Real-Valued Random Variable and the definition of an integrable random variable:


 * $X^+$ and $X^-$ are integrable random variables.

Since $X^+ \ge 0$ and $X^- \ge 0$, by our previous work there exists integrable random variables $Z_1$, $Z_2$ with:


 * $\ds \int_A X^+ \rd \Pr = \int_A Z_1 \rd \Pr$

and:


 * $\ds \int_A X^- \rd \Pr = \int_A Z_2 \rd \Pr$

for all $A \in \GG$, with $Z_1$ and $Z_2$ $\GG$-measurable.

Then from Integral of Integrable Function is Additive: Corollary 2 and Linear Combination of Real-Valued Random Variables is Real-Valued Random Variable, we have:


 * $Z_1 - Z_2$ is an integrable random variable that is $\GG$-measurable.

Then, we have:

So setting $Z = Z_1 - Z_2$, we have:


 * $\ds \int_A X \rd \Pr = \int_A Z \rd \Pr$

for all $A \in \GG$.

Now let $Z'$ be another integrable random variable that is $\GG$-measurable with:


 * $\ds \int_A X \rd \Pr = \int_A Z' \rd \Pr$

for all $A \in \GG$.

Then:


 * $\ds \int_A Z \rd \Pr = \int_A Z' \rd \Pr$

for all $A \in \GG$.

Then, from Measurable Functions with Equal Integrals on Sub-Sigma-Algebra are A.E. Equal, we have that:


 * $Z = Z'$ almost everywhere

completing the proof.