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Journal ArticleDOI

Efficient and portable combined random number generators

Pierre L'Ecuyer
- 01 Jun 1988 - 
- Vol. 31, Iss: 6, pp 742-751
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TLDR
An efficient way to combine two or more Multiplicative Linear Congruential Generators (MLCGs) is presented and a generator whose period is the least common multiple of the individual periods is produced.
Abstract
In this paper we present an efficient way to combine two or more Multiplicative Linear Congruential Generators (MLCGs) and propose several new generators. The individual MLCGs, making up the proposed combined generators, satisfy stringent theoretical criteria for the quality of the sequence they produce (based on the Spectral Test) and are easy to implement in a portable way. The proposed simple combination method is new and produces a generator whose period is the least common multiple of the individual periods. Each proposed generator has been submitted to a comprehensive battery of statistical tests. We also describe portable implementations, using 16-bit or 32-bit integer arithmetic. The proposed generators have most of the beneficial properties of MLCGs. For example, each generator can be split into many independent generators and it is easy to skip a long subsequence of numbers without doing the work of generating them all.

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Stan : A Probabilistic Programming Language

TL;DR: Stan as discussed by the authors is a probabilistic programming language for specifying statistical models, where a program imperatively defines a log probability function over parameters conditioned on specified data and constants, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration.
Journal ArticleDOI

Numerical distribution functions for unit root and cointegration tests

TL;DR: In this article, the authors used response surface regressions based on simulation experiments to calculate distribution functions for some well-known unit root and cointegration test statistics, which can be used to calculate both asymptotic and finite sample critical values and P-values for any of the tests.

Stan: A Probabilistic Programming Language.

TL;DR: Stan is a probabilistic programming language for specifying statistical models that provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler and an adaptive form of Hamiltonian Monte Carlo sampling.
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Numerical distribution functions of likelihood ratio tests for cointegration

TL;DR: In this article, the authors employ response surface regressions based on simulation experiments to calculate asymptotic distribution functions for the Johansen-type likelihood ratio tests for cointegration.
Posted Content

Numerical Distribution Functions of Likelihood Ratio Tests for Cointegration

TL;DR: The authors employed response surface regressions based on simulation experments to calculate asymptotic distribution functions for the likelihood ratio tests for cointegration proposed by Johansen and provided tables of critical values that are very much more accurate than those available previously.
References
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Book

The Art of Computer Programming

TL;DR: The arrangement of this invention provides a strong vibration free hold-down mechanism while avoiding a large pressure drop to the flow of coolant fluid.
Journal ArticleDOI

A Guide to Simulation.

TL;DR: Despite the brevity of the book, its mathematical notation, and the problems which it poses without solutions, the textbook is imbued with a feeling for theitty-gritty practical aspects of simulation.