In probability theory, Bobkov's inequality is a functional isoperimetric inequality for the canonical Gaussian measure. It generalizes the Gaussian isoperimetric inequality. The equation was proven in 1997 by the Russian mathematician Sergey Bobkov.^[1]

Notation:

Let

• ${\displaystyle \gamma ^{n}(dx)=(2\pi )^{-n/2}e^{-\|x\|^{2}/2}d^{n}x}$ be the canonical Gaussian measure on ${\displaystyle \mathbb {R} ^{n}}$ with respect to the Lebesgue measure,
• ${\displaystyle \phi (x)=(2\pi )^{-1/2}e^{-x^{2}/2}}$ be the one dimensional canonical Gaussian density
• ${\displaystyle \Phi (t)=\gamma ^{1}[-\infty ,t]}$ the cumulative distribution function
• ${\displaystyle I(t):=\phi (\Phi ^{-1}(t))}$ be a function ${\displaystyle I(t):[0,1]\to [0,1]}$ that vanishes at the end points ${\displaystyle \lim \limits _{t\to 0}I(t)=\lim \limits _{t\to 1}I(t)=0.}$

For every locally Lipschitz continuous (or smooth) function ${\displaystyle f:\mathbb {R} ^{n}\to [0,1]}$ the following inequality holds^[2][3]

${\displaystyle I\left(\int _{\mathbb {R} ^{n}}fd\gamma ^{n}(dx)\right)\leq \int _{\mathbb {R} ^{n}}{\sqrt {I(f)^{2}+|\nabla f|^{2}}}d\gamma ^{n}(dx).}$

There exists a generalization by Dominique Bakry and Michel Ledoux.^[4]

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