Threshold for the expected measure of random polytopes

Abstract: Let $\mu$ be a log-concave probability measure on ${\mathbb R}^n$ and for any $N>n$ consider the random polytope $K_N={\rm conv}{X_1,\ldots ,X_N}$, where $X_1,X_2,\ldots $ are independent random points in ${\mathbb R}^n$ distributed according to $\mu $. We study the question if there exists a threshold for the expected measure of $K_N$. Our approach is based on the Cramer transform $\Lambda_{\mu}^{\ast }$ of $\mu $. We examine the existence of moments of all orders for $\Lambda_{\mu}^{\ast }$ and establish, under some conditions, a sharp threshold for the expectation ${\mathbb E}{\mu^N}[\mu (K_N)]$ of the measure of $K_N$: it is close to $0$ if $\ln N\ll {\mathbb E}{\mu }(\Lambda_{\mu}^{\ast })$ and close to $1$ if $\ln N\gg {\mathbb E}{\mu }(\Lambda{\mu}^{\ast })$. The main condition is that the parameter $\beta(\mu)={\rm Var}{\mu }(\Lambda{\mu}^{\ast })/({\mathbb E}{\mu }(\Lambda{\mu }^{\ast }))^2$ should be small.