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Cipher algorithms and cryptographic hashes can be used as very high-quality pseudorandom number generators. However, generally they are considerably slower (typically by a factor 2–10) than fast, non-cryptographic random number generators.
Roger D. Nelson developed the project as an extrapolation of two decades of experiments from the controversial Princeton Engineering Anomalies Research Lab (PEAR). [6]In an extension of the laboratory research utilizing hardware Random Event Generators (REG) [7] called FieldREG, investigators examined the outputs of REGs in the field before, during and after highly focused or coherent group ...
A pseudorandom binary sequence (PRBS), pseudorandom binary code or pseudorandom bitstream is a binary sequence that, while generated with a deterministic algorithm, is difficult to predict [1] and exhibits statistical behavior similar to a truly random sequence.
It discards 1 − π /4 ≈ 21.46% of the total input uniformly distributed random number pairs generated, i.e. discards 4/ π − 1 ≈ 27.32% uniformly distributed random number pairs per Gaussian random number pair generated, requiring 4/ π ≈ 1.2732 input random numbers per output random number.
A simple algorithm to generate random Poisson-distributed numbers (pseudo-random number sampling) has been given by Knuth: [70]: 137-138 algorithm poisson random number (Knuth): init: Let L ← e −λ, k ← 0 and p ← 1. do: k ← k + 1. Generate uniform random number u in [0,1] and let p ← p × u.
A simple algorithm to generate a permutation of n items uniformly at random without retries, known as the Fisher–Yates shuffle, is to start with any permutation (for example, the identity permutation), and then go through the positions 0 through n − 2 (we use a convention where the first element has index 0, and the last element has index n − 1), and for each position i swap the element ...