Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator

By Makoto Matsumoto, T. Nishimura. 1998

Subfields: Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Probabilistic & Bayesian Methods

Citations in the corpus

Array programming with NumPy (2020, methodology): “The three other bit generators are a 64bit version of the Philox generator [63] (Philox), Chris Doty-Humphrey’s Small Fast Chaotic generator [64] (SFC64), and the 32-bit Mersenne Twister [65] (MT19937) which has been used in older versions of NumPy.”
High Performance Quadrature Rules: How Numerical Integration Affects a Popular Model of Product Differentiation (2011, methodology): “In practice, Monte Carlo draws are generated using an algorithm for generating apparently random numbers, such as Mersenne twister (Matsumoto and Nishimura, 1998), which can pass the statistical tests associated with random numbers.”
Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks (2014, methodology): “The Mersenne Twister [56] was used to generate a uniform distribution, which was then used to generate exponentially distributed random numbers (inter-arrival times and service times) using the standard inverse transform method.”
Computation of Probability Associated with Anderson–Darling Statistic (2018, unknown): “Random The same experiment was conducted with both simple and random stratified Mersenne Twister method [53] to assess the magnitude of the increases in the resolution of the AD statistic.”
SDF^3: SDF For Free (2006, background): “The actual degree of actors and the port rates are random values picked by a random number generator [7].”
Issues in Multiagent Resource Allocation (2006, methodology): “It oﬀers similar features as Swarm (discrete-event simulation, high-quality PRNGs, spatial modelling, visualisation tools) and it is also open-source and extensible.”
Compressive Sensing for Background Subtraction (2008, methodology): “Both the background and the foreground were acquired using pseudorandom compressive measurements (yb and yt, respectively) generated by a Mersenne Twister algorithm with a 64 × 64 pixel resolution [20].”
Algorithmic Pseudorandomness in Quantum Setups. (2014, background): “On the other hand, nowadays there are efficient algorithms [2, 3] that produce ‘seemingly random’ binary sequences, with excellent statistical properties.”
Combinatorial optimization by simulating adiabatic bifurcations in nonlinear Hamiltonian systems (2019, background): “[See the Supplementary Materials for its definition with a pseudo-random number generator in (44).”
Bayesian physical reconstruction of initial conditions from large-scale structure surveys (2012, methodology): “In particular, we use the Mersenne Twister MT19937, with 32-bit word length, as provided by the gsl rng mt19937 routine, which was particularly designed for Markov Chain Monte Carlo simulations (Matsumoto & Nishimura 1998).”
A comparison of statistical significance tests for information retrieval evaluation (2007, methodology): “Selection of a random number generator (RNG) is important when producing large numbers of samples.”