stat113 final project_Nigel_RNG

stat113 final project_Nigel_RNG - Nigel Chou Final Project,...

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Nigel Chou Final Project, Stat 113, Fall 2007 Random Number Generators To understand Random number Generators (RNGs), we first have to define what random numbers are. A random number is a value in a set that has an equal probability of being selected from the total population of possibilities [1]. Hence, its is essentially a uniformly distributed random variable . Ideally, a RNG should be unbiased , i.e. all potential values have the same probability of being chosen. There are two major categories of Random Number Generators: hardware random number generators (also called non-deterministic RNGs) and pseudorandom number generators, (or deterministic RNGs). Hardware RNGs Hardware RNGs generate an output (in bits which can be converted to numbers) that is based on an unpredictable physical process, such as thermal noise or quantum phenomena, or other processes that tend to be random but are not as easily characterized by the laws of physics. One such example is blowing air in a chamber with numbered ping-pong balls and then drawing them from the mixing chamber. These processes are assumed to be unpredictable, but the assertion of randomness can be subject to experimental testing. Hardware random number generators are often relatively slow, and may be biased due to the nature of the underlying process. Also, different hardware random number generators may be only suitable for specific applications depending on both the application and the generator. When generating random numbers on a computer, software engineers often attempt to
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This note was uploaded on 04/08/2008 for the course STAT 113 taught by Professor Mukherjee during the Fall '08 term at Duke.

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stat113 final project_Nigel_RNG - Nigel Chou Final Project,...

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