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We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC).
Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
G.O. Roberts and R.L. Tweedie · 1996
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Rates of convergence of the Hastings and Metropolis algorithms
K.L. Mengersen and R.L. Tweedie · 1996
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Adaptive proposal distribution for random walk Metropolis algorithm
H. Haario, E. Saksman, and J. Tamminen · 1999
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Estimation of population growth or decline in genetically monitored populations
M.A. Beaumont · 2003
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Markov chain Monte Carlo without likelihoods
P. Marjoram, J. Molitor, V. Plagnol, and S. Tavaré · 2003
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Gaussian Processes to Speed up Hybrid Monte Carlo for Expensive Bayesian Integrals
C.E. Rasmussen · 2003
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Reproducing Kernel Hilbert Spaces in Probability and Statistics
A. Berlinet and C. Thomas-Agnan · 2004
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Kernel methods for pattern analysis
J. Shawe-Taylor and N. Cristianini · 2004
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Estimation of non-normalized statistical models by score matching
A. Hyvärinen · 2005
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All of nonparametric statistics
Larry Wasserman · 2006
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Random features for large-scale kernel machines
A. Rahimi and B. Recht · 2007
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Some extensions of score matching
A. Hyvärinen · 2007
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Coupling and ergodicity of adaptive Markov chain Monte Carlo algorithms
G.O. Roberts and J.S. Rosenthal · 2007
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A tutorial on adaptive MCMC
C. Andrieu and J. Thoms · 2008
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The pseudo-marginal approach for efficient Monte Carlo computations
C. Andrieu and G.O. Roberts · 2009
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Likelihood-free Markov chain Monte Carlo
S.A. Sisson and Y. Fan · 2010
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Statistical inference for noisy nonlinear ecological dynamic systems
Fastfood–approximating kernel expansions in loglinear time
Q. Le, T. Sarlós, and A. Smola · 2013
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Pseudo-marginal Bayesian inference for Gaussian Processes
M. Filippone and M. Girolami · 2014
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Stochastic Gradient Hamiltonian Monte Carlo
T. Chen, E. Fox, and C. Guestrin · 2014
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Kernel Adaptive Metropolis-Hastings
D. Sejdinovic, H. Strathmann, M. Lomeli, C. Andrieu, and A. Gretton · 2014
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Density Estimation in Infinite Dimensional Exponential Families
B. Sriperumbudur, K. Fukumizu, R. Kumar, A. Gretton, and A. Hyvärinen · 2014
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Hamiltonian ABC
E. Meeds, R. Leenders, and M. Welling · 2015
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S. N. Wood · 2010
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MCMC using Hamiltonian dynamics
R.M. Neal · 2011
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A kernel two-sample test
A. Gretton, K. Borgwardt, B. Schölkopf, A. J. Smola, and M. Rasch · 2012
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UCI Machine Learning Repository, 2013
K. Bache and M. Lichman · 2013
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The Fundamental Incompatibility of Hamiltonian Monte Carlo and Data Subsampling
M. Betancourt · 2015
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Optimizing The Integrator Step Size for Hamiltonian Monte Carlo
M. Betancourt, S. Byrne, and M. Girolami · 2015
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Optimal rates for random Fourier features
B.K. Sriperumbudur and Z. Szabó · 2015
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Hamiltonian Monte Carlo Acceleration Using Neural Network Surrogate functions
C. Zhang, B. Shahbaba, and H. Zhao · 2015
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