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Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information.
A stochastic approximation method
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Hybrid Monte Carlo
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HBC: Hierarchical Bayes compiler, 2007
H. Daume III · 2007
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A tutorial on adaptive MCMC
C. Andrieu and J. Thoms · 2008
Graphical models, exponential families, and variational inference
M. Wainwright and M. Jordan · 2008
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Primal-dual subgradient methods for convex problems
Y. Nesterov · 2009
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Optimal tuning of the hybrid monte-carlo algorithm
A. Beskos, N. Pillai, G. Roberts, J. Sanz-Serna, and A. Stuart · 2010
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T. Minka, J. Winn, J. Guiver, and D. Knowles · 2010
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PyMC: Bayesian stochastic modelling in python
A. Patil, D. Huard, and C. Fonnesbeck · 2010
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Evaluating derivatives: principles and techniques of algorithmic differentiation
A. Griewank and A. Walther · 2008
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Riemann manifold langevin and hamiltonian monte carlo methods
M. Girolami and B. Calderhead · 2011
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Handbook of Markov Chain Monte Carlo , chapter 5: MCMC Using Hamiltonian Dynamics
R. Neal · 2011
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