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Lipschitz-type inequalities for Markov kernels
Let $K(\cdot\mid\cdot)$ be a Markov kernel. For a measure probability $\mu$, denote by $\mu K$ the probability measure induced by $K$ and $\mu$, i.e. $(\mu K)(A):=\int_\Omega \mu(d\omega)K(A\mid\omega)...
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Concentration inequalities for random measures
For random variables $X_1,\dots,X_n$ with common mean $\mathbb{E}[X_i]=\mu$ and common bounds $a\leq X_i\leq b$, we have the very useful Hoeffding's inequality:
$$\mathbb{P}\left(\left|\mu -\frac1n\...