  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.
