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We propose a new computationally efficient sampling scheme for Bayesian inference involving high dimensional probability distributions.
Hybrid Monte Carlo
S. Duane, A. D. Kennedy, B J. Pendleton, and D. Roweth · 1987
Earlier work this paper cites.
Neural networks and principal component analysis: Learning from examples without local minima
P. Baldi and K. Hornik · 1989
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Exploiting tractable substructures in intractable networks
L. Saul and M. I. Jordan · 1996
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An introduction to variational methods for graphical methods
M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul · 1999
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Variational MCMC
N. de Freitas, P. Højen-Sørensen, M. Jordan, and R. Stuart · 2001
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Approximation with artificial neural networks
Balázs Csanád Csáji · 2001
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Graphical models, exponential families, and variational inference
M. Wainwright and M. Jordan · 2008
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Online learning for latent dirichlet allocation
Matthew D. Hoffman, David M. Blei, and Francis R. Bach · 2010
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Approximate Riemannian conjugate gradient learning for fixed-form variational Bayes
A. Honkela, T. Raiko, M. Kuusela, M. Tornio, and J. Karhunen · 2010
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Bayesian learning via stochastic gradient Langevin dynamics
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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
M. Hoffman and A. Gelman · 2011
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Radford M Neal et al · 2011
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Fixed-form variational posterior approximation through stochastic linear regression
T. Salimans and D. A. Knowles · 2013
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Optimal tuning of the hybrid monte carlo algorithm
Alexandros Beskos, Natesh Pillai, Gareth Roberts, Jesus-Maria Sanz-Serna, Andrew Stuart, et al · 2013
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Split Hamiltonian Monte Carlo
B. Shahbaba, S. Lan, W.O. Johnson, and R.M. Neal · 2014
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Spherical Hamiltonian Monte Carlo for constrained target distributions
S. Lan, B. Zhou, and B. Shahbaba · 2014
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Dimension-independent likelihood-informed MCMC
Tiangang Cui, Kody J.H. Law, and Youssef M. Marzouk · 2016
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Precomputing strategy for Hamiltonian Monte Carlo method based on regularity in parameter space
C. Zhang, B. Shahbaba, and H. Zhao · 2017
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Hamiltonian Monte Carlo acceleration using surrogate functions with random bases
C. Zhang, B. Shahbaba, and H. Zhao · 2017
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Jiaming Song, Shengjia Zhao, and Stefano Ermon · 2017
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Daniel Levy, Matthew D. Hoffman, and Jascha Sohl-Dickstein · 2017
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Variational hamiltonian Monte Carlo via score matching
C. Zhang, B. Shahbaba, and H. Zhao · 2018
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