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Markov Chain Monte Carlo (MCMC) algorithms are essential tools in computational statistics for sampling from unnormalised probability distributions, but can be fragile when targeting high-dimensional, multimodal, or complex target distributions.
High-temperature equation of state by a perturbation method. i. nonpolar gases
Robert W Zwanzig · 1954
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Efficient estimation of free energy differences from Monte Carlo data
Charles H Bennett · 1976
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Replica Monte Carlo simulation of spin-glasses
Robert H. Swendsen and Jian-Sheng Wang · 1986
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Markov chain Monte Carlo maximum likelihood
Charles J Geyer · 1991
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Exchange Monte Carlo method and application to spin glass simulations
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Nonequilibrium equality for free energy differences
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A note on Metropolis-Hastings kernels for general state spaces
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Replica-exchange Monte Carlo method for the isobaric–isothermal ensemble
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Targeted free energy perturbation
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Suriyanarayanan Vaikuntanathan and Christopher Jarzynski · 2008
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Replica exchange with nonequilibrium switches
Andrew J Ballard and Christopher Jarzynski · 2009
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Characteristic of Bennett’s acceptance ratio method
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Efficiency of exchange schemes in replica exchange
Martin Lingenheil, Robert Denschlag, Gerald Mathias, and Paul Tavan · 2009
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Optimal estimators and asymptotic variances for nonequilibrium path-ensemble averages
David DL Minh and John D Chodera · 2009
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Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions
Dawn B Woodard, Scott C Schmidler, and Mark Huber · 2009
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Density estimation by dual ascent of the log-likelihood
Esteban G. Tabak and Eric Vanden-Eijnden · 2010
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Escorted free energy simulations
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Replica exchange with nonequilibrium switches: Enhancing equilibrium sampling by increasing replica overlap
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Brownian motion, martingales, and stochastic calculus , volume 274 of Grad. Texts Math
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