Area and Perimeter of the Convex Hull of Stochastic Points

Given a set $P$ of $n$ points in the plane, we study the computation of the probability distribution function of both the area and perimeter of the convex hull of a random subset $S$ of $P$. The random subset $S$ is formed by drawing each point $p$ of $P$ independently with a given rational probability $\pi_p$. For both measures of the convex hull, we show that it is #P-hard to compute the probability that the measure is at least a given bound $w$. For $\varepsilon\in(0,1)$, we provide an algorithm that runs in $O(n^{{6}/\varepsilon)$} time and returns a value that is between the probability that the area is at least $w$, and the probability that the area is at least $(1-\varepsilon)w$. For the perimeter, we show a similar algorithm running in $O(n^{{6}/\varepsilon)$} time. Finally, given $\varepsilon,\delta\in(0,1)$ and for any measure, we show an $O(n\log n+ (n/\varepsilon^{2)\log(1/\delta))$-time} Monte Carlo algorithm that returns a value that, with probability of success at least $1-\delta$, differs at most $\varepsilon$ from the probability that the measure is at least $w$.