An exhaustive analysis of multiplicative congruential random number generators with modulus 231-1
SIAM Journal on Scientific and Statistical Computing
Random number generators: good ones are hard to find
Communications of the ACM
Steady-state simulation of queueing processes: survey of problems and solutions
ACM Computing Surveys (CSUR)
Communications of the ACM - Special issue on simulation
Distributed stochastic discrete-event simulation in parallel time streams
WSC '94 Proceedings of the 26th conference on Winter simulation
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Bad subsequences of well-known linear congruential pseudorandom number generators
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Don't trust parallel Monte Carlo!
PADS '98 Proceedings of the twelfth workshop on Parallel and distributed simulation
Uniform random number generators
Proceedings of the 30th conference on Winter simulation
Tables of linear congruential generators of different sizes and good lattice structure
Mathematics of Computation
A spectral method for confidence interval generation and run length control in simulations
Communications of the ACM - Special issue on simulation modeling and statistical computing
Simulation Modeling and Analysis
Simulation Modeling and Analysis
On credibility of simulation studies of telecommunication networks
IEEE Communications Magazine
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The growing popularity of stochastic discrete event simulation in areas such as telecommunication, combined with much marketing hype about ease of use, has coaxed some practitioners into a misguided belief that choosing prefabricated components from libraries and configuring them into a model by pointing and clicking is all that is needed. While neglect of statistical aspects of simulation has already led to some highly problematic published results, this erroneous assumption must also be guarded against in university teaching. This paper therefore argues for the importance of teaching those issues that critically affect the analysis and credibility of a simulation's results alongside those methods and tools targeted at the needs of model design and construction.