Monte Carlo methods. Vol. 1: basics
Monte Carlo methods. Vol. 1: basics
Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
Approximating Martingales for Variance Reduction in Markov Process Simulation
Mathematics of Operations Research
External control variance reduction for nonstationary simulation
WSC '83 Proceedings of the 15th conference on Winter simulation - Volume 1
Shadow hybrid Monte Carlo: an efficient propagator in phase space of macromolecules
Journal of Computational Physics
Regenerative structure of Markov chains simulated via common random numbers
Operations Research Letters
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We show that Markov couplings can be used to improve the accuracy of Markov chain Monte Carlo calculations in some situations where the steady-state probability distribution is not explicitly known. The technique generalizes the notion of control variates from classical Monte Carlo integration. We illustrate it using two models of nonequilibrium transport.