# What does this notation mean for Expectation and Variance?

Let $f: \{-1,1\}^n \rightarrow \mathbb{R}$

Then the influence function of $x_i$ is defined by $$\text{Inf}_i(f) = \mathbb{E}_{(x_1,...,x_{i-1},x_{i+1},...,x_n)}[\text{Var}_{x_i}[f]]$$

What does the subscript on the expectation mean? And what does the subscript on the Var mean? The probability space in question is the uniform distrubtion on $\{-1, 1\}^n$

Assume that the product space $\Omega=X\times Y$ is endowed with the product probability $P=\mu\otimes\nu$ for some probability $\mu$ on $X$ and some probability $\nu$ on $Y$. For every (integrable) random variable $Z$ on $\Omega$, $E_x(Z)$ is the random variable $T$ on $Y$ defined, for every $y$ in $Y$, by $$T(y)=\int_XZ(x,y)\mathrm d\mu(x),$$ and $$E_y(T)=\int_YT(y)\mathrm d\nu(y).$$ Likewise, $\mathrm{Var}_x(Z)=E_x(Z^2)-(E_x(Z))^2$, where $E_x(Z^2)$ is the random variable $S$ on $Y$ defined, for every $y$ in $Y$, by $$S(y)=\int_XZ(x,y)^2\mathrm d\mu(x),$$ and $$E_y(S)=\int_YS(y)\mathrm d\nu(y).$$ Note that, in general, $$E_y(\mathrm{Var}_x(Z))\ne\mathrm{Var}(Z),$$ since $$E_y(\mathrm{Var}_x(Z))=\int_\Omega Z^2\mathrm dP-\int_Y\left(\int_XZ(x,y)\mathrm d\mu(x)\right)^2\mathrm d\nu(y),$$ while $$\mathrm{Var}(Z)=\int_\Omega Z^2\mathrm dP-\left(\int_\Omega Z\mathrm dP\right)^2=\int_\Omega Z^2\mathrm dP-\left(\int_Y\int_XZ(x,y)\mathrm d\mu(x)\mathrm d\nu(y)\right)^2.$$ This shows the general inequality $$E_y(\mathrm{Var}_x(Z))\leqslant\mathrm{Var}(Z).$$
• I can't reconcile this with the expression from my question. The inner variance would be only a random variable on $\{x_1,...,x_{i-1}, x_{i+1}, ... x_{n}\}$ since we have integrated out the randomness in $x_i$. Then taking the outer expectiation would have no effect, leaving the final expression as a random variable on $\{x_1,...,x_{i-1}, x_{i+1}, ... x_{n}\}$ when in fact it should be some deterministic constant $\in \mathbb{R}$ – Mark Sep 16 '14 at 16:39
• The inner variance is indeed a random variable depending on $(x_1,...,x_{i-1},x_{i+1},...,x_n)$. The outer expectation integrates this with respect to the distribution of $(x_1,...,x_{i-1},x_{i+1},...,x_n)$ hence the result is deterministic. Exactly what my post describes with $x=x_i$ and $y=(x_1,...,x_{i-1},x_{i+1},...,x_n)$. – Did Sep 16 '14 at 16:52