A Third Classified Bibliography on Random Number Generation and Testing

Sowey, Eric

doi:10.2307/2981887

Cited by 10 publications

(6 citation statements)

References 45 publications

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“…The problem of generating stochastic sequences of numbers with specified properties is extensively analysed in the literature since the early 70's (for a complete reference guide see Sowey 1986). In particular, Liu & Munson (1982) proposed a white Gaussian noise input to a linear digital filter followed by a zero-memory non linearity (ZMNL).…”

Section: Basic Differences and Advantages From Previous Workmentioning

confidence: 99%

Generating artificial light curves: revisited and updated

Emmanoulopoulos¹,

McHardy²,

Papadakis³

2013

Monthly Notices of the Royal Astronomical Society

230

203

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The production of artificial light curves with known statistical and variability properties is of great importance in astrophysics. Consolidating the confidence levels during cross-correlation studies, understanding the artefacts induced by sampling irregularities, establishing detection limits for future observatories are just some of the applications of simulated data sets. Currently, the widely used methodology of amplitude and phase randomisation is able to produce artificial light curves which have a given underlying power spectral density (PSD) but which are strictly Gaussian distributed. This restriction is a significant limitation, since the majority of the light curves e.g. active galactic nuclei, X-ray binaries, gamma-ray bursts show strong deviations from Gaussianity exhibiting 'burst-like' events in their light curves yielding long-tailed probability distribution functions (PDFs). In this study we propose a simple method which is able to precisely reproduce light curves which match both the PSD and the PDF of either an observed light curve or a theoretical model. The PDF can be representative of either the parent distribution or the actual distribution of the observed data, depending on the study to be conducted for a given source. The final artificial light curves contain all of the statistical and variability properties of the observed source or theoretical model i.e. same PDF and PSD, respectively. Within the framework of Reproducible Research, the code, together with the illustrative example used in this manuscript, are both made publicly available in the form of an interactive mathematica notebook.

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Section: Basic Differences and Advantages From Previous Workmentioning

confidence: 99%

Generating artificial light curves: revisited and updated

Emmanoulopoulos¹,

McHardy²,

Papadakis³

2013

Monthly Notices of the Royal Astronomical Society

230

203

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“…The numbers in the book were generated using an electronic roulette wheel and were available in punched card format to allow easy interfacing with computers. There also appeared many electronic random number generators designed to be connected to computers or output devices like teleprinters (Sowey, 1972).…”

Section: A the First Quantum Random Number Generatorsmentioning

confidence: 99%

Quantum random number generators

2017

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Random numbers are a fundamental resource in science and engineering with important applications in simulation and cryptography. The inherent randomness at the core of quantum mechanics makes quantum systems a perfect source of entropy. Quantum random number generation is one of the most mature quantum technologies with many alternative generation methods. We discuss the different technologies in quantum random number generation from the early devices based on radioactive decay to the multiple ways to use the quantum states of light to gather entropy from a quantum origin. We also discuss randomness extraction and amplification and the notable possibility of generating trusted random numbers even with untrusted hardware using device independent generation protocols.

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“…An overview and discussion of RNGs for parallel computing, with several references, can be found in L' Ecuyer et al (2017). Extended bibliographies on uniform RNGs are given by Sowey (1972) and Beebe (2017). The latter contains 3885 references.…”

Section: Further Informationmentioning

confidence: 99%

History of uniform random number generation

L’Ecuyer¹

2017

2017 Winter Simulation Conference (WSC)

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Random number generators were invented before there were symbols for writing numbers, and long before mechanical and electronic computers. All major civilizations through the ages found the urge to make random selections, for various reasons. Today, random number generators, particularly on computers, are an important (although often hidden) ingredient in human activity. In this article, we give a historical account on the design, implementation, and testing of uniform random number generators used for simulation.

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A Third Classified Bibliography on Random Number Generation and Testing

Cited by 10 publications

References 45 publications

Generating artificial light curves: revisited and updated

Generating artificial light curves: revisited and updated

Quantum random number generators

History of uniform random number generation

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