# Concentration in Gauss space

This is a theorem called Concentration in Gauss space. Let $$f$$ be a real valued Lipschitz function on $$\mathbb {R}^{n}$$ with Lipschitz constant $$K$$, i.e. $$\left|f(x)-f(y)\right|\leq K\|x-y\|_{2}$$ for all $$x,y\in \mathbb {R}^{n}$$ (such functions are also called K-Lipschitz). Let $$X$$ be the standard normal random vector in $$\mathbb {R}^{n}$$. Then for every $$t\geq 0$$ one has \begin{align*} \mathbb {P}\left\{f(X)-\mathbb {E}f(X)>t\right\}\leq \text{exp}(-t^{2}/2K^{2}). \end{align*} We have \begin{align*} \mathbb {P}\left\{f(X)-\mathbb {E}f(X)>t\right\}=\mathbb {P}\left\{e^{\lambda(f(x)-\mathbb {E}f(x))}>e^{\lambda t}\right\}\leq \mathbb {E}e^{\lambda(f(x)-\mathbb {E}f(x))}/e^{\lambda t} \end{align*} I wonder how to continue from here. Or maybe just inform me where to find the proof. Thanks a lot.

• A detailed proof can be found in Ledoux's The Concentration of Measure Phenomenon. Much machinery is needed to prove the result (unfortunately). A proof outline can also be found in Vershynin's High Dimensional Probability but it's along the same lines as the previous one. An elementary proof of a weaker inequality can be found at slide 49 of this pdf. – Gabriel Romon May 21 at 8:11
• Thank you sir! @GabrielRomon – Jiexiong687691 May 24 at 4:33