Random Numbers and Computers

This book covers pseudorandom number generation algorithms, evaluation techniques, and offers practical advice and code examples. Random Numbers and Computers is an essential introduction or refresher on pseudorandom numbers in computer science. The first comprehensive book on the topic, readers are provided with a practical introduction to the techniques of pseudorandom number generation, including how the algorithms work and how to test the output to decide if it is suitable for a particular purpose.

Practical applications are demonstrated with hands-on presentation and descriptions that readers can apply directly to their own work. Examples are in C and Python and given with an emphasis on understanding the algorithms to the point of practical application. The examples are meant to be implemented, experimented with and improved/adapted by the reader.

Practical presentation and analysis of existing and historic pseudorandom number generation techniques. The hands-on presentation and descriptions give readers a practical knowledge to apply directly to their own work. Skipping the step of translating theoretical results into something directly applicable, it also lowers the bar for enjoying the book by not requiring a sophisticated math background. This is particularly beneficial to undergraduate students Implementations, with walk-through, in C and Python, that includes descriptions of an algorithm followed by an implementation in a familiar and commonly used programming language with its own description. This reinforces the ideas behind the algorithm and provides code that can be used as-is or adapted intelligently Experiments to illustrate the properties of pseudorandom number generators, including how to compare them with each other. The experiments are advanced enough to show a real use while still fitting nicely into the presentation format enforced by a static book. It also gives the reader an opportunity to see the "how and why" of the selection and implementation. This benefits the reader by going beyond a simple presentation and augmenting it with a worked example

Autorentext
Ronald T. Kneusel holds a PhD in Computer Science from the University of Colorado, Boulder and an MS in Physics from Michigan State University. His background includes development of algorithms and applications for varied scientific disciplines from remote sensing to medical imaging. He is currently a Senior Data Scientist for Harris Corporation applying deep learning models to remote sensing imagery and other data sources. A product of the microcomputer revolution, Kneusel has had a life-long fascination with all aspects of computer science and mathematics. This is his second Springer book. The first, Numbers and Computers (in 2nd edition), explores how computers store and manipulate numbers.

Inhalt
1 Random and Pseudorandom Sequences.- 2 Generating Uniform Random Numbers.- 3 Generating Nonuniform Random Numbers.- 4 Testing Pseudorandom Generators.- 5 Parallel Random Number Generators.- 6 Cryptographically Secure Pseudorandom Number Generators.- 7 Other Random Sequences.