An optimal algorithm for Monte Carlo estimation

被引：164

作者：

Dagum, P ^{[1
]}

Karp, R

Luby, M

Ross, S

机构：

[1] Stanford Univ, Sch Med, Sect Med Informat, Stanford, CA 94305 USA

[2] Int Comp Sci Inst, Berkeley, CA 94704 USA

[3] Univ Calif Berkeley, Div Comp Sci, Berkeley, CA 94704 USA

[4] Univ Calif Berkeley, Dept Ind Engn & Operat Res, Berkeley, CA 94704 USA

来源：

SIAM JOURNAL ON COMPUTING | 2000年 / 29卷 / 05期

关键词：

stopping rule; approximation algorithm; Monte Carlo estimation; sequential estimation; stochastic approximation;

D O I：

10.1137/S0097539797315306

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

A typical approach to estimate an unknown quantity mu is to design an experiment that produces a random variable Z distributed in [0, 1] with E[Z] = mu, run this experiment independently a number of times, and use the average of the outcomes as the estimate. In this paper, we consider the case when no a priori information about Z is known except that is distributed in [ 0, 1]. We describe an approximation algorithm AA which, given and, when running independent experiments with respect to any Z, produces an estimate that is within a factor 1+epsilon of mu with probability at least 1-delta. We prove that the expected number of experiments run by AA (which depends on Z) is optimal to within a constant factor for every Z.

引用

页码：1484 / 1496

页数：13