Guaranteed Monte Carlo Methods for Bernoulli Random Variables

Abstract

Simple Monte Carlo is a versatile computational method with a convergence rate of $O(n^{-1/2})$. It can be used to estimate the means of random variables whose distributions are unknown. Bernoulli random variables, $Y$, are widely used to model success (failure) of complex systems. Here $Y=1$ denotes a success (failure), and $p=\mathbb{E}(Y)$ denotes the probability of that success (failure). Another application of Bernoulli random variables is $Y=1_R(\mathbf{X})$, where then $p$ is the probability of $\mathbf{X}$ lying in the region $R$. This article explores how estimate $p$ to a prescribed absolute error tolerance, $\epsilon$, with a high level of confidence, $1-\alpha$. The proposed algorithm automatically determines the number of samples of $Y$ needed to reach the prescribed error tolerance with the specified confidence level by using Hoeffding's inequality. The algorithm described here has been implemented in MATLAB and is part of the Guaranteed Automatic Integration Library (GAIL).

References in corpus (1)

• The Cost of Deterministic, Adaptive, Automatic Algorithms: Cones, Not Balls