## Monday, July 7, 2014

### Law of Iterated Expectation

Let $X$ and $Y$ denote continuous, random, real-valued variables with joint probability density function $f(X, Y)$. The marginal density function of $Y$ is $f_Y(y) := \int_{x \in \mathbb R} f(x, y) dx$. The expectation $E(Y)$ of $Y$ can be recovered by integrating against the marginal density function. In particular, $$\label{eq:E(Y)} E(Y)=\int_{y \in \mathbb R} y f_Y(y) dy = \int_{y \in \mathbb R} \int_{x \in \mathbb R} y f(x, y) \ dx \ dy.$$ The conditional probability density function of $Y$ given that $X$ is equal to some value $x$ is defined by $$\label{eq:cdf} f_{Y \mid X} (y \mid X = x):= f_{Y \mid X} (y \mid x)= \frac{f(x, y)}{f_X(x)} = \frac{f(x,y)}{\int_{y \in \mathbb R} f(x, y) \ dy}.$$ The conditional expectation $E(Y \mid X = x)$ of $Y$ given that $X$ has value $x$ is given by $$\label{eq:cond exp} E(Y \mid X = x) = \int_{y \in \mathbb R} y f_{Y \mid X} (y \mid x) \ dy.$$ But $E(Y \mid X = x)$ depends on $X$, so in turn is itself a random variable denoted $E(Y \mid X)$, whence we can compute its expectation. Now \begin{align*} E (E (Y \mid X)) &= \int_{x \in \mathbb R} E(Y \mid x) f_X(x) \ dx \\ & = \int_{x \in \mathbb R} f_X(x) \left( \int_{y \in \mathbb R} y f_{Y \mid X}(x, y) \ dy \right) \ dx \quad \text{(by \ref{eq:cond exp} )} \\ & = \int_{x \in \mathbb R} \int_{y \in \mathbb R} f_X(x) \cdot y \frac{f(x,y)}{f_X(x)} \ dy \ dx \quad \text{(by \ref{eq:cdf})}\\ & = \int_{y \in \mathbb R} \int_{x \in \mathbb R} y f(x, y) \ dx \ dy \\ & = E(Y). \quad \text{(by \ref{eq:E(Y)})} \end{align*} This result is sometimes called the law of the iterated expectation.