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We consider the problem of sampling from a density of the form $p(x) \propto \exp(-f(x)- g(x))$, where $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a smooth and strongly convex function and $g: \mathbb{R}^d \rightarrow \mathbb{R}$ is a convex and Lipschitz function.
Equation of state calculations by fast computing machines
N. Metropolis, A. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller · 1953
Earlier work this paper cites.
Fonctions convexes duales et points proximaux dans un espace Hilbertien
J.-J. Moreau · 1962
Earlier work this paper cites.
Monte Carlo sampling methods using Markov chains and their applications
W. K. Hastings · 1970
Earlier work this paper cites.
Logarithmic Sobolev inequalities
L. Gross · 1975
Earlier work this paper cites.
Quadpack. A Subroutine Package for Automatic Integration
R. Piessens, E. de Doncker-Kapenga, and C. W. Ueberhuber · 1983
Earlier work this paper cites.
Diffusions hypercontractives
D. Bakry and M. Émery · 1985
Earlier work this paper cites.
Non-Uniform Random Variate Generation
L. Devroye · 1986
Earlier work this paper cites.
Logarithmic Sobolev inequalities and stochastic Ising models
R. Holley and D. Stroock · 1987
Earlier work this paper cites.
Conductance and the rapid mixing property for Markov chains: the approximation of permanent resolved
M. Jerrum and A. Sinclair · 1988
Earlier work this paper cites.
Sampling-based approaches to calculating marginal densities
A. E. Gelfand and A. F. Smith · 1990
Earlier work this paper cites.
Adaptive rejection sampling for Gibbs sampling
W. R. Gilks and P. Wild · 1992
Earlier work this paper cites.
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J. Besag and P. J. Green · 1993
Earlier work this paper cites.
Computable bounds for geometric convergence rates of Markov chains
S. P. Meyn and R. L. Tweedie · 1994
Earlier work this paper cites.
Lévy-Gromov’s isoperimetric inequality for an infinite-dimensional diffusion generator
D. Bakry and M. Ledoux · 1996
Earlier work this paper cites.
Rates of convergence of the Hastings and Metropolis algorithms
K. L. Mengersen and R. L. Tweedie · 1996
Earlier work this paper cites.
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S. G. Bobkov · 1999
Earlier work this paper cites.
Faster mixing via average conductance
L. Lovász and R. Kannan · 1999
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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G. O. Roberts and J. S. Rosenthal · 2001
Earlier work this paper cites.
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G. Hargé · 2004
Earlier work this paper cites.
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G. O. Roberts and J. S. Rosenthal · 2004
Earlier work this paper cites.
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