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Uniform Random Numbers: Theory and Practice by Shu Tezuka: New
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N.º de artículo de eBay:286041598947
Características del artículo
- Estado
- Book Title
- Uniform Random Numbers: Theory and Practice
- Publication Date
- 1995-05-31
- Pages
- 209
- ISBN
- 9780792395720
Acerca de este producto
Product Identifiers
Publisher
Springer
ISBN-10
0792395727
ISBN-13
9780792395720
eBay Product ID (ePID)
192658
Product Key Features
Number of Pages
Xii, 209 Pages
Publication Name
Uniform Random Numbers : Theory and Practice
Language
English
Publication Year
1995
Subject
Probability & Statistics / Stochastic Processes, Number Theory, System Theory, Optimization
Type
Textbook
Subject Area
Mathematics, Science
Series
The Springer International Series in Engineering and Computer Science Ser.
Format
Hardcover
Dimensions
Item Weight
38.8 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
95-010562
Dewey Edition
20
Series Volume Number
315
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
512/.76
Table Of Content
1 Introduction.- 2 Preliminaries from Number Theory.- 3 Linear Congruential Generators.- 4 Beyond Linear Congruential Generators.- 5 Statistical Tests.- 6 Derandomization.- A Sample C Routines.- References.
Synopsis
In earlier forewords to the books in this series on Discrete Event Dynamic Systems (DEDS), we have dwelt on the pervasive nature of DEDS in our human-made world. From manufacturing plants to computer/communication networks, from traffic systems to command-and-control, modern civilization cannot function without the smooth operation of such systems. Yet mathemat ical tools for the analysis and synthesis of DEDS are nascent when compared to the well developed machinery of the continuous variable dynamic systems char acterized by differential equations. The performance evaluation tool of choice for DEDS is discrete event simulation both on account of its generality and its explicit incorporation of randomness. As it is well known to students of simulation, the heart of the random event simulation is the uniform random number generator. Not so well known to the practitioners are the philosophical and mathematical bases of generating "random" number sequence from deterministic algorithms. This editor can still recall his own painful introduction to the issues during the early 80's when he attempted to do the first perturbation analysis (PA) experiments on a per sonal computer which, unbeknownst to him, had a random number generator with a period of only 32,768 numbers. It is no exaggeration to say that the development of PA was derailed for some time due to this ignorance of the fundamentals of random number generation., In earlier forewords to the books in this series on Discrete Event Dynamic Systems (DEDS), we have dwelt on the pervasive nature of DEDS in our human-made world. From manufacturing plants to computer/communication networks, from traffic systems to command-and-control, modern civilization cannot function without the smooth operation of such systems. Yet mathemat- ical tools for the analysis and synthesis of DEDS are nascent when compared to the well developed machinery of the continuous variable dynamic systems char- acterized by differential equations. The performance evaluation tool of choice for DEDS is discrete event simulation both on account of its generality and its explicit incorporation of randomness. As it is well known to students of simulation, the heart of the random event simulation is the uniform random number generator. Not so well known to the practitioners are the philosophical and mathematical bases of generating "random" number sequence from deterministic algorithms. This editor can still recall his own painful introduction to the issues during the early 80's when he attempted to do the first perturbation analysis (PA) experiments on a per- sonal computer which, unbeknownst to him, had a random number generator with a period of only 32,768 numbers. It is no exaggeration to say that the development of PA was derailed for some time due to this ignorance of the fundamentals of random number generation.
LC Classification Number
QA402.5-402.6
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