Abstract
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.
Original language | English (US) |
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Pages (from-to) | 7127-7136 |
Number of pages | 10 |
Journal | Journal of Computational Physics |
Volume | 228 |
Issue number | 19 |
DOIs | |
State | Published - Oct 20 2009 |
Keywords
- Control variates
- Coupling
- Markov chain Monte Carlo
- Monte Carlo
- Nonequilibrium statistical mechanics
- Variance reduction
ASJC Scopus subject areas
- Numerical Analysis
- Modeling and Simulation
- Physics and Astronomy (miscellaneous)
- General Physics and Astronomy
- Computer Science Applications
- Computational Mathematics
- Applied Mathematics