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Hamiltonian Monte Carlo (HMC) is a state-of-the-art Markov chain Monte Carlo sampling algorithm for drawing samples from smooth probability densities over continuous spaces.
Faster Hamiltonian Monte Carlo by learning leapfrog scale
Changye Wu, Julien Stoehr, and Christian P Robert · 1905
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Equation of state calculations by fast computing machines
Nicholas Metropolis, Arianna W Rosenbluth, Marshall N Rosenbluth, Augusta H Teller, and Edward Teller · 1953
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Studies in molecular dynamics. I. General method
Berni J Alder and T E Wainwright · 1959
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A lower bound for the smallest eigenvalue of the Laplacian
Jeff Cheeger · 1969
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A topological application of the isoperimetric inequality
Mikhail Gromov and Vitali D Milman · 1983
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Hybrid Monte Carlo
Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth · 1987
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Global Monte Carlo algorithms for many-fermion systems
Michael Creutz · 1988
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Conductance and the rapid mixing property for Markov chains: The approximation of permanent resolved
Mark Jerrum and Alistair Sinclair · 1988
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The mixing rate of Markov chains, an isoperimetric inequality, and computing the volume
László Lovász and Miklós Simonovits · 1990
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Elements of Information Theory
T.M. Cover and J.A. Thomas · 1991
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Adaptive rejection sampling for Gibbs sampling
Walter R Gilks and Pascal Wild · 1992
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Random walks in a convex body and an improved volume algorithm
László Lovász and Miklós Simonovits · 1993
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Random walks on graphs: A survey
László Lovász et al · 1993
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Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Adrian FM Smith and Gareth O Roberts · 1993
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An improved acceptance procedure for the hybrid Monte Carlo algorithm
Radford M Neal · 1994
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Markov chains for exploring posterior distributions
Luke Tierney · 1994
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Isoperimetric problems for convex bodies and a localization lemma
Ravi Kannan, László Lovász, and Miklós Simonovits · 1995
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Logarithmic Sobolev inequalities for finite Markov chains
Persi Diaconis and Laurent Saloff-Coste · 1996
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Isoperimetric and analytic inequalities for log-concave probability measures
Sergey G Bobkov · 1999
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Concentration of measure and logarithmic Sobolev inequalities
Michel Ledoux · 1999
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Hit-and-run mixes fast
László Lovász · 1999
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Faster mixing via average conductance
László Lovász and Ravi Kannan · 1999
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Monte Carlo Statistical Methods
C. P. Robert and G. Casella · 1999
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Some remarks on isoperimetry of Gaussian type
Franck Barthe and Bernard Maurey · 2000
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Exploring hybrid monte carlo in bayesian computation
Lingyu Chen, Zhaohui Qin, and Jun S Liu · 2001
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Mixed and isoperimetric estimates on the log-Sobolev constants of graphs and Markov chains
Christian Houdré · 2001
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Langevin diffusions and Metropolis-Hastings algorithms
Mamba: Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia, 2014
BJ Smith · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, et al · 2015
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Theoretical guarantees for approximate sampling from smooth and log-concave densities
Arnak S Dalalyan · 2016
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On the geometric ergodicity of Hamiltonian Monte Carlo
Samuel Livingstone, Michael Betancourt, Simon Byrne, and Mark Girolami · 2016
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Stan: A probabilistic programming language
Bob Carpenter, Andrew Gelman, Matthew D Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell · 2017
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Gareth O Roberts and Osnat Stramer · 2002
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General state space Markov chains and MCMC algorithms
Gareth O Roberts, Jeffrey S Rosenthal, et al · 2004
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Evolving sets, mixing and heat kernel bounds
Ben Morris and Yuval Peres · 2005
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Geometric random walks: a survey
Santosh Vempala · 2005
Cited alongside, same era.
Mixing time bounds via the spectral profile
Sharad Goel, Ravi Montenegro, and Prasad Tetali · 2006
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Blocking conductance and mixing in random walks
Ravi Kannan, László Lovász, and Ravi Montenegro · 2006
Cited alongside, same era.
Xiang Cheng and Peter Bartlett · 2017
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Underdamped Langevin MCMC: A non-asymptotic analysis
Xiang Cheng, Niladri S Chatterji, Peter L Bartlett, and Michael I Jordan · 2017
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On the convergence of Hamiltonian Monte Carlo
Alain Durmus, Eric Moulines, and Eero Saksman · 2017
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Eldan’s stochastic localization and the KLS hyperplane conjecture: An improved lower bound for expansion
Yin Tat Lee and Santosh Srinivas Vempala · 2017
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Rapid mixing of Hamiltonian Monte Carlo on strongly log-concave distributions
Oren Mangoubi and Aaron Smith · 2017
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Coupling and convergence for Hamiltonian Monte Carlo
Nawaf Bou-Rabee, Andreas Eberle, and Raphael Zimmer · 2018
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Fast MCMC sampling algorithms on polytopes
Yuansi Chen, Raaz Dwivedi, Martin J Wainwright, and Bin Yu · 2018
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Convergence rate of riemannian hamiltonian monte carlo and faster polytope volume computation
Yin Tat Lee and Santosh S. Vempala · 2018
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Algorithmic theory of ODEs and sampling from well-conditioned logconcave densities
Yin Tat Lee, Zhao Song, and Santosh S Vempala · 2018
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Sampling can be faster than optimization
Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, and Michael I Jordan · 2018
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Dimensionally tight running time bounds for second-order Hamiltonian Monte Carlo
Oren Mangoubi and Nisheeth K Vishnoi · 2018
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Optimal convergence rate of Hamiltonian Monte Carlo for strongly logconcave distributions
Zongchen Chen and Santosh S Vempala · 2019
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User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient
Arnak S Dalalyan and Avetik Karagulyan · 2019
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Log-concave sampling: Metropolis-hastings algorithms are fast
Raaz Dwivedi, Yuansi Chen, Martin J Wainwright, and Bin Yu · 2019
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Nonconvex sampling with the Metropolis-adjusted Langevin algorithm
Oren Mangoubi and Nisheeth K Vishnoi · 2019
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