MASSIVE GENERATION OF DATA WITH RANDOM VARIATES

被引：0

作者：

Maurer, Peter M. ^{[1
]}

机构：

[1] Baylor Univ, Dept Comp Sci, Waco, TX 76798 USA

来源：

PROCEEDINGS OF THE 2021 ANNUAL MODELING AND SIMULATION CONFERENCE (ANNSIM'21) | 2020年

关键词：

Random data; Probability distributions; Random Variates; Random number generation; BETA;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The Data Generation Language (DGL) has been widely used to generate random data for simulation and for software testing. Although DGL is highly versatile, its ability to handle different probability distributions was severely limited. The work described here corrects this problem by adding features that can be used to generate variates from a number of different probability distributions. This data can be used directly by a simulator or stored in a file or database table for future use. Variate generation makes use of a basic stream of uniformly distributed random numbers that can be generated by one of 47 different random number generators.

引用

页数：11

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