Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
Latin supercube sampling for very high-dimensional simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Discrete-event simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Variance with alternative scramblings of digital nets
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Variance Reduction via Lattice Rules
Management Science
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
On the theoretical comparison of low-bias steady-state estimators
ACM Transactions on Modeling and Computer Simulation (TOMACS)
A Randomized Quasi-Monte Carlo Simulation Method for Markov Chains
Operations Research
Exact sampling with highly uniform point sets
Mathematical and Computer Modelling: An International Journal
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The coupling-from-the-past (CFTP) algorithm of Propp and Wilson permits one to sample exactly from the stationary distribution of an ergodic Markov chain. By using it n times independently, we obtain an independent sample from that distribution. A more representative sample can be obtained by creating negative dependence between these n replicates; other authors have already proposed to do this via antithetic variates, Latin hypercube sampling, and randomized quasi-Monte Carlo (RQMC). We study a new, often more effective, way of combining CFTP with RQMC, based on the array-RQMC algorithm. We provide numerical illustrations for Markov chains with both finite and continuous state spaces, and compare with the RQMC combinations proposed earlier.