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Most Markov chain Monte Carlo methods operate in discrete time and are reversible with respect to the target probability.
Monmarché, P · 1903
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Martingale approach to some limit theorems
Papanicolaou, G · 1977
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Hybrid Monte Carlo
Duane, S · 1987
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Weak convergence and optimal scaling of random walk Metropolis algorithms
Roberts, G. O · 1997
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A guided walk Metropolis algorithm
Gustafson, P · 1998
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Lifting Markov chains to speed up mixing
Chen, F · 1999
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Some adaptive Monte Carlo methods for Bayesian inference
Tierney, L · 1999
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Analysis of a nonreversible Markov chain sampler
Diaconis, P · 2000
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On stable numerical differentiation
Ramm, A · 2001
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Optimal scaling for various Metropolis-Hastings algorithms
Roberts, G. O · 2001
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Slice sampling
Neal, R. M · 2003
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An exact Gibbs sampler for the Markov-modulated Poisson process
Fearnhead, P · 2006
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Multiscale methods: averaging and homogenization
Pavliotis, G · 2008
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Efficient MCMC schemes for computationally expensive posterior distributions
Fielding, M · 2011
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Principles of multiscale modeling
Weinan, E · 2011
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Rejection-free Monte Carlo sampling for general potentials
Peters, E. A · 2012
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Optimal tuning of the hybrid Monte Carlo algorithm
Beskos, A · 2013
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Optimal non-reversible linear drift for the convergence to equilibrium of a diffusion
Lelièvre, T · 2013
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Optimizing the integrator step size for Hamiltonian Monte Carlo
Betancourt, M · 2014
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Piecewise-deterministic Markov chain Monte Carlo
Vanetti, P · 2017
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Generalized Bouncy Particle Sampler
Wu, C · 2017
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Hypercoercivity of piecewise deterministic Markov process-Monte Carlo
Andrieu, C · 2018
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High-dimensional scaling limits of piecewise deterministic sampling algorithms
Bierkens, J · 2018
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Piecewise-deterministic Markov processes for continuous-time Monte Carlo
Fearnhead, P · 2018
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A piecewise deterministic Markov process via radius-angle swaps in hyperspherical coordinates
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