1 / 9

Understanding Random Numbers: Definitions, Types, and Applications

This text explores the concept of random numbers, defining them as unpredictable values derived from a process that cannot be reproduced. It differentiates between pseudo-random and true random number generators, illustrating examples and their mechanisms. Pseudo-random numbers utilize algorithms like the linear congruential method, while true random numbers extract randomness from physical phenomena such as radioactive decay or atmospheric noise. The importance of random numbers in cryptology, gaming, and sampling is also discussed, emphasizing their critical role in ensuring security and fairness across various applications.

colum
Download Presentation

Understanding Random Numbers: Definitions, Types, and Applications

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Random NumbersZachary Neyland

  2. Definitions • Random number – a random number is a number that is generated by a process whose outcome is unpredictable and typically cannot be reproduced • Seed – starts the process for generating random numbers • Period – the length a given series of random numbers has before repeating a number

  3. Types of Random Number Generators • Pseudo-Random Number Generators • Not truly random • True Random Number Generators • Extract randomness from physical phenomena

  4. Pseudo-Random Number Generators • Linear congruential method • An example simple linear formula • Xn+1 <- (k*Xn + C) mod M • Sample sequence (k = 19, C = 51, M = 100, X0 = 25): • 25, 26, 45, 6, 47, 44, 87, 4, 27, 64, 67, 24, 7, 84, 47

  5. True Random Number Generators • Need to use physical phenomena • Zener diode • Radioactive decay • Atmospheric noise/radio noise • Sample and convert to a 1 or 0

  6. PRNGs vs TRNGs

  7. OH GOD MY BRAIN • Impossible to verify that a given number was produced randomly • Very easy to determine if a sequence of infinite length is random • Random if quantity of information it contains is also infinite

  8. Applications of Random Numbers • Cryptology/Security • Game and gambling • Random Sampling (drug screening, exam questions) • Lotteries

  9. References • http://www.random.org • A.K. Dewdney The (new) Turing omnibus, New York Henry Holt and Company

More Related