Randomizer Number
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Random Number Generators—Principles and Practices
Author: David Johnston
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2018-09-10
Random Number Generators, Principles and Practices has been written for programmers, hardware engineers, and sophisticated hobbyists interested in understanding random numbers generators and gaining the tools necessary to work with random number generators with confidence and knowledge. Using an approach that employs clear diagrams and running code examples rather than excessive mathematics, random number related topics such as entropy estimation, entropy extraction, entropy sources, PRNGs, randomness testing, distribution generation, and many others are exposed and demystified. If you have ever Wondered how to test if data is really random Needed to measure the randomness of data in real time as it is generated Wondered how to get randomness into your programs Wondered whether or not a random number generator is trustworthy Wanted to be able to choose between random number generator solutions Needed to turn uniform random data into a different distribution Needed to ensure the random numbers from your computer will work for your cryptographic application Wanted to combine more than one random number generator to increase reliability or security Wanted to get random numbers in a floating point format Needed to verify that a random number generator meets the requirements of a published standard like SP800-90 or AIS 31 Needed to choose between an LCG, PCG or XorShift algorithm Then this might be the book for you.
Random Number Generators
Author: Luis Gerardo de la Fraga
language: en
Publisher: Springer Nature
Release Date: 2025-05-17
This book discusses the details of random number generation (RNG) as a key technology that is used for information security in various fields, such as electronic commerce and authentication. Readers will see how random numbers are used in various applications such as in the generation of keys for data encryption, games, lotteries, sampling, simulations, statistical sampling, search/sort algorithms, and gambling. The authors describe how the classification of RNGs encompasses linear and nonlinear (chaotic) pseudo and truly random number generators, and how they can be evaluated by applying statistical tests. Covers a vast array of special topics on fractional-order chaotic circuits and systems to develop applications in information security; Describes details of using FPGAs to approach chaotic maps and fractional-order circuits and systems for hardware security; Includes Verilog hardware description for random number generation.
Random Number Generation and Monte Carlo Methods
Author: James E. Gentle
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-03-14
The role of Monte Carlo methods and simulation in all of the sciences has in creased in importance during the past several years. These methods are at the heart of the rapidly developing subdisciplines of computational physics, compu tational chemistry, and the other computational sciences. The growing power of computers and the evolving simulation methodology have led to the recog nition of computation as a third approach for advancing the natural sciences, together with theory and traditional experimentation. Monte Carlo is also a fundamental tool of computational statistics. At the kernel of a Monte Carlo or simulation method is random number generation. Generation of random numbers is also at the heart of many standard statis tical methods. The random sampling required in most analyses is usually done by the computer. The computations required in Bayesian analysis have become viable because of Monte Carlo methods. This has led to much wider applications of Bayesian statistics, which, in turn, has led to development of new Monte Carlo methods and to refinement of existing procedures for random number generation.