EVALUATING BEST-CASE AND WORST-CASE VARIANCES WHEN BOUNDS ARE AVAILABLE

作     者：FISHMAN, GS GRANOVSKY, BL RUBIN, DS 

作者机构：TECHNION ISRAEL INST TECHNOLDEPT MATHHAIFAISRAEL 

出 版 物：《SIAM JOURNAL ON SCIENTIFIC AND STATISTICAL COMPUTING》 (工业与应用数学会科学计算杂志)

年 卷 期：1992年第13卷第6期

页      面：1347-1360页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：BEST-CASE VARIANCE COMPUTATIONAL COMPLEXITY MONTE-CARLO PARAMETRIC LINEAR PROGRAMMING SAMPLE SIZE WORST-CASE VARIANCE 

摘      要：This paper describes procedures for computing the tightest possible best-case and worst-case bounds on the variance of a discrete, bounded, random variable when lower and upper bounds are available for its unknown probability mass function. An example from the application of the Monte Carlo method to the estimation of network reliability illustrates the procedures and, in particular, reveals considerable tightening in the worst-case bound when compared to the trivial worst-case bound based exclusively on range.