232 Boolean Analysis — Indicator Smoothing
232.1 Overview
Conditional Gaussian averaging of indicators preserves their means and approximates them in integral absolute difference. Coupling the averaging noises also shows that averaging contracts rotation variation.
232.2 Declarations
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). The averaged indicator is the function \(u_\theta :\mathbb R^n\to \mathbb R\); for a nonintegrable integrand the integral is assigned zero.
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). If \(A\) is measurable, then \(u_\theta (x)=\gamma _n\{ y:\cos \theta \, x+\sin \theta \, y\in A\} \) for every real \(\theta \) and every \(x\).
Let \(\Omega ,\Xi \) be measurable spaces, \(A\subseteq \Xi \) a measurable set, and \(f,g:\Omega \to \Xi \) measurable maps. Then \(\{ z:\neg (f(z)\in A\ \Longleftrightarrow \ g(z)\in A)\} \) is measurable.
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). For independent \(X,Y\sim \gamma _n\), put \(R_A(\theta )=\Pr (1_A(X)\ne 1_A(\cos \theta \, X+\sin \theta \, Y))\). If \(A\) is measurable, then \(\int |u_\theta (x)-1_A(x)|\, d\gamma _n(x)=R_A(\theta )\) for every real \(\theta \).
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). If \(A\) is measurable, then \(0\le u_\theta (x)\le 1\) for every real \(\theta \) and every \(x\).
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). If \(A\) is measurable, then \(\int |u_\theta -1_A|\, d\gamma _n\to 0\) as \(\theta \to 0\) through real values.
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). For measurable \(A\) and every real \(\theta \), the function \(u_\theta \) is measurable.
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). For measurable \(A\) and every real \(\theta \), the function \(u_\theta \) is \(\gamma _n\)-integrable.
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). For measurable \(A\) and every real \(\theta \), \(\int u_\theta \, d\gamma _n=\gamma _n(A)\).
Let \(E\) be a measurable space and \(\mu \) an s-finite measure on \(E\). The map \(((a,b),(c,d))\mapsto ((a,c),(b,d))\) preserves the measure \((\mu \otimes \mu )\otimes (\mu \otimes \mu )\).
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For any real \(u\), if \(X_1,X_2,Y_1,Y_2\) are independent with law \(\gamma _n\), then \((\cos u\, X_1+\sin u\, Y_1,\cos u\, X_2+\sin u\, Y_2)\) has law \(\gamma _n\otimes \gamma _n\).
Let \(\mu \) be a finite measure on a measurable space \(E\), and let \(C,D\subseteq E\) be arbitrary sets. Then \(|\mu (C)-\mu (D)|\le \mu (C\mathbin {\triangle } D)\), where all values are viewed as finite real numbers.
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). Let \(A\) be measurable, \(u,v\in \mathbb R\), and \(x,x\prime \in \mathbb R^n\). For independent \(Y,Y\prime \sim \gamma _n\), \(|u_u(x)-u_u(\cos v\, x+\sin v\, x\prime )|\le \Pr (1_A(\cos u\, x+\sin u\, Y)\ne 1_A(\cos u(\cos v\, x+\sin v\, x\prime )+\sin u(\cos v\, Y+\sin v\, Y\prime )))\).
Let \(\gamma _n\) be standard Gaussian measure on \(\mathbb R^n\). For \(A\subseteq \mathbb R^n\) and \(\theta \in \mathbb R\), put \(u_\theta (x)=\int 1_A(\cos \theta \, x+\sin \theta \, y)\, d\gamma _n(y)\). For independent \(X,Y\sim \gamma _n\), put \(R_A(\theta )=\Pr (1_A(X)\ne 1_A(\cos \theta \, X+\sin \theta \, Y))\). If \(A\) is measurable, then for every \(u,v\in \mathbb R\), \(\mathbb E_{X,Y}|u_u(X)-u_u(\cos v\, X+\sin v\, Y)|\le R_A(v)\).