# Total Expectation Theorem

## Theorem

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

Let $X$ be a discrete random variable on $\EE$.

Let $\set {B_1 \mid B_2 \mid \cdots}$ be a partition of $\Omega$ such that $\map \Pr {B_i} > 0$ for each $i$.

Then:

$\ds \expect X = \sum_i \expect {X \mid B_i} \, \map \Pr {B_i}$

whenever this sum converges absolutely.

In the above:

$\expect X$ denotes the expectation of $X$
$\expect {X \mid B_i}$ denotes the conditional expectation of $X$ given $B_i$.

## Proof

 $\ds \sum_i \expect {X \mid B_i} \, \map \Pr {B_i}$ $=$ $\ds \sum_i \sum_x x \, \map \Pr {\set {X = x} \cap B_i}$ Definition of Conditional Expectation $\ds$ $=$ $\ds \sum_x x \map \Pr {\set {X \in x} \cap \paren {\bigcup_i B_i} }$ $\ds$ $=$ $\ds \sum_x x \map \Pr {X = x}$ $\ds$ $=$ $\ds \expect X$ Definition of Expectation

$\blacksquare$

## Also known as

Some sources refer to the Total Expectation Theorem as the partition theorem, which causes ambiguity, as that name is used for other things as well.

Some sources give this as the law of total expectation.