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In order to sample from a given target distribution (often of Gibbs type), the Monte Carlo Markov chain method consists in constructing an ergodic Markov process whose invariant measure is the target distribution.
T. Kato, Perturbation theory of linear operators
1969
Earlier work this paper cites.
R.T. Rockafellar, Convex Analysis
1970
Earlier work this paper cites.
P.H. Peskun, Optimum Monte-Carlo Sampling Using Markov Chains, Biometrika
1973
Earlier work this paper cites.
A. Douglis and L. Nirenberg, Interior estimates for elliptic systems of partial differential equations. Communications Pure in Applied Mathematics
1975
Earlier work this paper cites.
M.D. Donsker and S.R.S. Varadhan, Asymptotic evaluation of certain Markov process expectations for large times, I, Communications Pure in Applied Mathematics
1976
Earlier work this paper cites.
J. Gärtner, On large deviations from the invariant measure, Theory of probability and its applications
1977
Earlier work this paper cites.
R. Pinsky, The I-function for diffusion processes with boundaries, The Annals of Probability
1985
Earlier work this paper cites.
Y. Amit and U. Grenander, Comparing sweeping strategies for stochastic relaxation, Journal of Multivariate Analysis
1991
Earlier work this paper cites.
A. Frigessi, C.R. Hwang and L. Younes, Optimal spectral structures of reversible stochatic matrices, Monte Carlo methods and the simulation of Markov random fields, Annals of Applied Probability
1992
Earlier work this paper cites.
A. Frigessi, C.R. Hwang, S.J. Sheu and P. Di Stefano, Convergence rates of the Gibbs sampler, the Metropolis algorithm, and their single-site updating dynamics, Journal of Royal Statistical Society Series B, Statistical Methodology
1993
Earlier work this paper cites.
C.R. Hwang, S.Y. Hwang-Ma and S.J. Sheu, Accelerating Gaussian diffusions. The Annals of Applied Probability
1993
Earlier work this paper cites.
K.A. Athreya, H. Doss and J. Sethuraman, On the convergence of the Markov chain simulation method, Annals of Statistics
1996
Earlier work this paper cites.
W.R. Gilks and G.O. Roberts, Strategies for improving MCMC, Monte Carlo Markov Chain in practice
1996
Cited alongside, same era.
K.L. Mergessen and R.L. Tweedie, Rates of convergence of the Hastings and Metropolis algorithms, Annals of Statistics
1996
Cited alongside, same era.
1998
Cited alongside, same era.
A. J. Majda and P. R. Kramer, Simplified models for turbulent diffusion: Theory, numerical modelling and physical phenomena, Physics Reports
1999
Cited alongside, same era.
F. Den Hollander, Large deviations
2000
Cited alongside, same era.
P. Constantin, A. Kiselev, L. Ryshik and A. Zlatos, Diffusion and mixing in fluid flow Annals of Mathematics
2008
Later among the works it cites.
S. Meyn and R.L. Tweedie, Markov Chains and Stochastic Stability
2009
Later among the works it cites.
P. Diaconis, S. Holmes and R. Neal, Analysis of a nonreversible Markov chain sampler, Annals of Applied Probability
2010
Later among the works it cites.
B. Franke, C.-R. Hwang, H.-M. Pai, and S.-J. Sheu, The behavior of the spectral gap under growing drift, Transactions of the American Mathematical Society
2010
Later among the works it cites.
Y. Sun, F. Gomez, and J. Schmidhuber, Improving the Asymptotic Performance of Markov Chain Monte- Carlo by Inserting Vortices. In Advances in Neural Information Processing Systems
2010
Later among the works it cites.
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2000
Cited alongside, same era.
A. Mira, Ordering and improving the performance of Monte Carlo Markov chains, Statist. Sci
2001
Cited alongside, same era.
R.M. Neal, Improving asymptotic variance of MCMC estimators: Non-reversible chains are better, Techincal report, No. 0406, Department of Statistics, University of Toronto
2004
Cited alongside, same era.
G.O. Roberts and J.S. Rosenthal, General state space Markov Chain and MCMC algorithms, Probability Surveys
2004
Cited alongside, same era.
C.R. Hwang, S.Y. Hwang-Ma and S.J. Sheu, Accelerating diffusions, The Annals of Applied Probability
2005
Cited alongside, same era.
S. Asmussen and P.W. Glynn, Stochastic Simulation
2007
Cited alongside, same era.
M. Bedard and J.S. Rosenthal, Optimal Scaling of Metropolis Algorithms: Heading Towards General Target Distributions, Canadian Jounral of Statistics
2008
Cited alongside, same era.
C. Barbarosie, Representation of divergence-free vector fields, Quarterly of Applied Mathematics
2011
Later among the works it cites.
N. Plattner, J.D. Doll, P. Dupuis, H. Wang, Y. Liu, and J.E. Gubernatis. An infinite swapping approach to the rare-event sampling problem. J. of Chemical Physics
2011
Later among the works it cites.
P. Dupuis, Y. Liu, N. Plattner, and J. D. Doll, On the Infinite Swapping Limit for Parallel Tempering. SIAM Multiscale Modeling and Simulation
2012
Later among the works it cites.
P. Diaconis and L. Miclo, On the spectral analysis of second-order Markov chains, submitted, (2013)
2013
Later among the works it cites.
T. Lelievre, F. Nier and G.A. Pavliotis, Optimal non-reversible linear drift for the convergence to equilibrium of a diffusion, CJournal of Statistical Physics
2013
Later among the works it cites.