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,

\[\begin{equation} \label{eq:E(Y)} E(Y)=\int_{y \in \mathbb R} y f_Y(y) dy = \int_{y \in \mathbb R} \int_{x \in \mathbb R} y f(x, y) \ dx \ dy. \end{equation}\]

The conditional probability density function of \(Y\) given that \(X\) is equal to some value \(x\) is defined by

\[\begin{equation} \label{eq:cdf} f_{Y \mid X} (y \mid X = x) := f_{Y \mid X} (y \mid x) = \frac{f(x, y)}{f_X(x)} = \frac{f(x,y)}{\int_{y \in \mathbb R} f(x, y) \ dy}. \end{equation}\]

The conditional expectation \(E(Y \mid X = x)\) of \(Y\) given that \(X\) has value \(x\) is given by

\[\begin{equation} \label{eq:cond exp} E(Y \mid X = x) = \int_{y \in \mathbb R} y f_{Y \mid X} (y \mid x) \ dy. \end{equation}\]

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**.