SSJ: a framework for stochastic simulation in Java

L’Ecuyer, Pierre; Meliani, Lakhdar; Vaucher, Jean

doi:10.1109/wsc.2002.1172890

Cited by 58 publications

(51 citation statements)

References 12 publications

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“…The simulations were made using the SSJ software package (L'Ecuyer, Meliani, and Vaucher 2002). For the QMC point sets, we used (a) the Korobov lattice rules proposed in Table 1 of L'Ecuyer and Lemieux (2000b) (we used the third value of a in the table for each value of n) and (b) Sobol' point sets with the same parameters as in Bratley and Fox (1988).…”

Section: Methodsmentioning

confidence: 99%

Efficient simulation of gamma and variance-gamma processes

Avramidis

L’Ecuyer

Tremblay

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)

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We study algorithms for sampling discrete-time paths of a gamma process and a variance gamma process, defined as a Brownian process with random time change obeying a gamma process. The attractive feature of the algorithms is that increments of the processes over longer time scales are assigned to the first sampling coordinates. The algorithms are based on having in explicit form the process' conditional distributions, are similar in spirit to the Brownian bridge sampling algorithms proposed for financial Monte Carlo, and synergize with quasi-Monte Carlo techniques for efficiency improvement. We compare the variance and efficiency of ordinary Monte Carlo and quasi-Monte Carlo for an example of financial option pricing with the variance-gamma model, taken from Madan, Carr, and Chang (1998).

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Section: Methodsmentioning

confidence: 99%

Efficient simulation of gamma and variance-gamma processes

Avramidis

L’Ecuyer

Tremblay

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693)

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“…The numerical results reported in this paper were obtained using the Java library SSJ [29], which offers tools for RQMC and stochastic simulation in general. In the examples, all random variates are generated by inversion.…”

Section: Examplesmentioning

confidence: 99%

Randomized Quasi-Monte Carlo: An Introduction for Practitioners

L’Ecuyer

2018

Springer Proceedings in Mathematics &Amp; Statistics

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We survey basic ideas and results on randomized quasi-Monte Carlo (RQMC) methods, discuss their practical aspects, and give numerical illustrations. RQMC can improve accuracy compared with standard Monte Carlo (MC) when estimating an integral interpreted as a mathematical expectation. RQMC estimators are unbiased and their variance converges at a faster rate (under certain conditions) than MC estimators, as a function of the sample size. Variants of RQMC also work for the simulation of Markov chains, for function approximation and optimization, for solving partial differential equations, etc. In this introductory survey, we look at how RQMC point sets and sequences are constructed, how we measure their uniformity, why they can work for high-dimensional integrals, and how can they work when simulating Markov chains over a large number of steps.

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“…In this section, we present simulation results using a simulator written in the Java based SSJ framework [26]. The target application for the experiments is the RUBiS online auction benchmark.…”

Section: Simulation Based Evaluationmentioning

confidence: 99%