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We introduce a recent symplectic integration scheme derived for solving physically motivated systems with non-separable Hamiltonians.
Generalizing the no-U-turn sampler to Riemannian manifolds
Michael J Betancourt · 1920
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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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On the construction and comparison of difference schemes
Gilbert Strang · 1968
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Simon Duane, Anthony D Kennedy, Brian J Pendleton, and Duncan Roweth · 1987
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Construction of higher order symplectic integrators
Haruo Yoshida · 1990
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Symplectic integration of Hamiltonian systems
Paul J Channell and Clint Scovel · 1990
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A practical Bayesian framework for backpropagation networks
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BAYESIAN LEARNING FOR NEURAL NETWORKS
Radford M Neal · 1995
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Gradient-based learning applied to document recognition
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Methods of information geometry
Shun-Ichi Amari and Hiroshi Nagaoka · 2000
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Langevin diffusions and Metropolis-Hastings algorithms
Gareth O Roberts and Osnat Stramer · 2002
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Slice sampling
Radford M Neal et al · 2003
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Simulating Hamiltonian dynamics , volume 14
Benedict Leimkuhler and Sebastian Reich · 2004
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Gaussian Processes for Machine Learning , volume 2
Christopher KI Williams and Carl Edward Rasmussen · 2006
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Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop · 2006
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Stochastic variational inference
Matthew D Hoffman, David M Blei, Chong Wang, and John Paisley · 2013
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Adaptive Hamiltonian and Riemann Manifold Monte Carlo
Ziyu Wang, Shakir Mohamed, and Nando Freitas · 2013
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The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Matthew D Hoffman and Andrew Gelman · 2014
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Split Hamiltonian Monte Carlo
Babak Shahbaba, Shiwei Lan, Wesley O Johnson, and Radford M Neal · 2014
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Explicit methods in extended phase space for inseparable Hamiltonian problems
Pauli Pihajoki · 2015
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Simple, scalable and accurate posterior interval estimation
Cheng Li, Sanvesh Srivastava, and David B Dunson · 2017
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Ernst Hairer, Christian Lubich, and Gerhard Wanner · 2006
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Riemann manifold Langevin and Hamiltonian Monte Carlo methods
Mark Girolami and Ben Calderhead · 2011
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MCMC using Hamiltonian dynamics
Radford M Neal et al · 2011
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Practical Bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Shiwei Lan, Vassilios Stathopoulos, Babak Shahbaba, and Mark Girolami · 2012
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Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
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Explicit Symplectic-like Integrators with Midpoint Permutations for Spinning Compact Binaries
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Yarin Gal and Lewis Smith · 2018
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Loss-calibrated approximate inference in Bayesian neural networks
Adam D Cobb, Stephen J Roberts, and Yarin Gal · 2018
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Explicit symplectic algorithms based on generating functions for relativistic charged particle dynamics in time-dependent electromagnetic field
Ruili Zhang, Yulei Wang, Yang He, Jianyuan Xiao, Jian Liu, Hong Qin, and Yifa Tang · 2018
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