Random Number Generators

Nick T. Thomopoulos
Additional contact information
Nick T. Thomopoulos: Illinois Institute of Technology, Stuart School of Business

Chapter Chapter 2 in Essentials of Monte Carlo Simulation, 2013, pp 9-14 from Springer

Abstract: Abstract The integrity of computer simulation models is only as good as the reliability of the random number generator that produces the stream of random numbers one after the other. The chapter describes the difficult task of developing an algorithm to generate random numbers that are statistically valid and have a large cycle length. The linear congruent method is currently the common way to generate the random numbers for a computer. The parameters of this method include the multiplier and the seed. Only a few multipliers are statistically recommended, and two popular ones in use for 32-bit word length computers are presented. Another parameter is the seed and this allows the analyst the choice of altering the sequence of random numbers with each run, and also when necessary, offers the choice of using the same sequence of random numbers from one run to another.

Keywords: Random Number; Random Number Generator; Random Data; Pseudo Random Number; Full Cycle (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-6022-0_2

Ordering information: This item can be ordered from
http://www.springer.com/9781461460220

DOI: 10.1007/978-1-4614-6022-0_2

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().