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We propose a methodology to parallelize Hamiltonian Monte Carlo estimators.
Optimum Monte-Carlo sampling using Markov chains
P. H. Peskun · 1973
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Hybrid Monte Carlo
S. Duane, A. D. Kennedy, B. J. Pendleton, and D. Roweth · 1987
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Analysis of parallel replicated simulations under a completion time constraint
P. W. Glynn and P. Heidelberger · 1991
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A generalized guided Monte Carlo algorithm
A. M. Horowitz · 1991
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The asymptotic efficiency of simulation estimators
P. W. Glynn and W. Whitt · 1992
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Bayesian learning via stochastic dynamics
R. M. Neal · 1993
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Regeneration in Markov chain samplers
P. Mykland, L. Tierney, and B. Yu · 1995
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Weak convergence and optimal scaling of random walk Metropolis algorithms
G. O. Roberts, A. Gelman, and W. R. Gilks · 1997
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Faithful couplings of Markov chains: now equals forever
J. S. Rosenthal · 1997
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A coupling-regeneration scheme for diagnosing convergence in Markov chain Monte Carlo algorithms
V. E. Johnson · 1998
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Log Gaussian Cox processes
J. Møller, A. R. Syversveen, and R. P. Waagepetersen · 1998
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Optimal scaling of discrete approximations to Langevin diffusions
G. O. Roberts and J. S. Rosenthal · 1998
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Parallel computing and Monte Carlo algorithms
J. S. Rosenthal · 2000
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Explaining the perfect sampler
G. Casella, M. Lavine, and C. P. Robert · 2001
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Bayesian prediction of spatial count data using generalized linear mixed models
O. F. Christensen and R. Waagepetersen · 2002
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Circularly-coupled Markov chain sampling
R. M. Neal · 2002
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Using all Metropolis–Hastings proposals to estimate mean values
H. Tjelmeland · 2004
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Scaling limits for the transient phase of local Metropolis–Hastings algorithms
O. F. Christensen, G. O. Roberts, and J. S. Rosenthal · 2005
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Geometric numerical integration: structure-preserving algorithms for ordinary differential equations
E. Hairer, G. Wanner, and C. Lubich · 2005
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Chapter 3: Total variation distance between measures
D. Pollard · 2005
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CODA: Convergence diagnosis and output analysis for MCMC
M. Plummer, N. Best, K. Cowles, and K. Vines · 2006
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Theoretical and numerical comparison of some sampling methods for molecular dynamics
E. Cances, F. Legoll, and G. Stoltz · 2007
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Markov chains and stochastic stability
S. Meyn and R. Tweedie · 2009
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Stan: a probabilistic programming language
B. Carpenter, A. Gelman, M. D. Hoffman, D. Lee, B. Goodrich, M. Betancourt, M. A. Brubaker, J. Guo, P. Li, and A. Riddell · 2016
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Perfect simulation , volume 148
M. Huber · 2016
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On the geometric ergodicity of Hamiltonian Monte Carlo
S. Livingstone, M. Betancourt, S. Byrne, and M. Girolami · 2016
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The scalable Langevin exact algorithm: Bayesian inference for big data
M. Pollock, P. Fearnhead, A. M. Johansen, and G. O. Roberts · 2016
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A conceptual introduction to Hamiltonian Monte Carlo
M. Betancourt · 2017
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Free Energy Computations: A Mathematical Perspective
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Handbook of Markov chain Monte Carlo
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Riemann manifold Langevin and Hamiltonian Monte Carlo methods
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tmg: truncated multivariate Gaussian sampling
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Optimal tuning of the Hybrid Monte Carlo algorithm
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CLTs and asymptotic variance of time-sampled Markov chains
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The geometric foundations of Hamiltonian Monte Carlo
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Geometric integrators and the Hamiltonian Monte Carlo method
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Coupling and convergence for Hamiltonian Monte Carlo
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Piecewise deterministic Markov processes for continuous-time Monte Carlo
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Smoothing with couplings of conditional particle filters
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