Algorithms for random generation and counting: a Markov chain approach
Algorithms for random generation and counting: a Markov chain approach
Convergence rates for Markov chains
SIAM Review
What do we know about the metropolis algorithm?
Journal of Computer and System Sciences
Understanding Molecular Simulation: From Algorithms to Applications
Understanding Molecular Simulation: From Algorithms to Applications
Markov chain algorithms for planar lattice structures
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Sampling adsorbing staircase walks using a new Markov chain decomposition method
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Analysis of swapping and tempering monte carlo algorithms
Analysis of swapping and tempering monte carlo algorithms
Torpid mixing of simulated tempering on the Potts model
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
On population-based simulation for static inference
Statistics and Computing
Convergence rates of Markov chains for some self-assembly and non-saturated Ising models
Theoretical Computer Science
Towards optimal scaling of metropolis-coupled Markov chain Monte Carlo
Statistics and Computing
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The Metropolis-coupled Markov chain method (or "Swapping Algorithm") is an empirically successful hybrid Monte Carlo algorithm. It alternates between standard transitions on parallel versions of the system at different parameter values, and swapping two versions. We prove rapid mixing for two bimodal examples, including the mean-field Ising model.