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Exact approximations of Markov chain Monte Carlo (MCMC) algorithms are a general emerging class of sampling algorithms.
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W. Hoeffding · 1956
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Generalized convex inequalities
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The existence of probability measures with given marginals
V. Strassen · 1965
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Some inequalities for the square root of a positive definite matrix
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Optimum Monte-Carlo sampling using Markov chains
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Analytical best upper bounds on stop-loss premiums
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Nonlocal Monte Carlo algorithm for self-avoiding walks with fixed endpoints
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Subgeometric rates of convergence of f f -ergodic Markov chains
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Geometric ergodicity and hybrid Markov chains
G. O. Roberts and J. S. Rosenthal · 1997
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Inferring coalescence times from dna sequence data
S. Tavare, D. J. Balding, R. Griffiths, and P. Donnelly · 1997
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Adaptive Markov chain Monte Carlo through regeneration
W. R. Gilks, G. O. Roberts, and S. K. Sahu · 1998
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A note on Metropolis-Hastings kernels for general state spaces
L. Tierney · 1998
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The penalty method for random walks with uncertain energies
D. Ceperley and M. Dewing · 1999
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Geometric ergodicity of Metropolis algorithms
S. F. Jarner and E. Hansen · 2000
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Controlled MCMC for optimal sampling
C. Andrieu and C. P. Robert · 2001
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Geometric L 2 L^{2} and L 1 L^{1} convergence are equivalent for reversible Markov chains
G. O. Roberts and R. L. Tweedie · 2001
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Approximate bayesian computation in population genetics
M. A. Beaumont, W. Zhang, and D. J. Balding · 2002
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Polynomial convergence rates of Markov chains
S. F. Jarner and G. O. Roberts · 2002
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Comparison methods for stochastic models and risks. 2002
A. Müller and D. Stoyan · 2002
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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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Stochastic comparisons of stratified sampling techniques for some monte carlo estimators
L. Goldstein, Y. Rinott, and M. Scarsini · 2011
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Constructing summary statistics for approximate bayesian computation: semi-automatic approximate Bayesian computation
P. Fearnhead and D. Prangle · 2012
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Stochastic comparisons of symmetric sampling designs
L. Goldstein, Y. Rinott, and M. Scarsini · 2012
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Coupled MCMC with a randomized acceptance probability
G. Nicholls, C. Fox, and A. Watt · 2012
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Explicit error bounds for Markov chain Monte Carlo
D. Rudolf · 2012
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Efficient MCMC for climate model parameter estimation: Parallel adaptive chains and early rejection
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Practical drift conditions for subgeometric rates of convergence
R. Douc, G. Fort, E. Moulines, and P. Soulier · 2004
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On the Markov chain central limit theorem
G. L. Jones · 2004
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Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes
A. Beskos, O. Papaspiliopoulos, G. O. Roberts, and P. Fearnhead · 2006
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Stochastic orders
M. Shaked and J. G. Shanthikumar · 2007
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Extremal moment methods and stochastic orders. application in actuarial science: Chapters iv, v and vi
W. Hürlimann · 2008
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On the use of bounds on the stop-loss premium for an inventory management decision problem
G. Janssens and K. Ramaekers · 2008
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A. Solonen, P. Ollinaho, M. Laine, H. Haario, J. Tamminen, and H. o. Järvinen · 2012
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Annealed importance sampling reversible jump mcmc algorithms
G. Karagiannis and C. Andrieu · 2013
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Nonasymptotic bounds on the estimation error of MCMC algorithms
K. Łatuszyński, B. Miasojedow, and W. Niemiro · 2013
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Pseudo-marginal algorithms with multiple CPUs
C. C. Drovandi · 2014
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Conditional convex orders and measurable martingale couplings
L. Leskelä and M. Vihola · 2014
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Comparison of asymptotic variances of inhomogeneous Markov chains with applications to Markov chain Monte Carlo methods
F. Maire, R. Douc, and J. Olsson · 2014
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On the efficiency of pseudo-marginal random walk Metropolis algorithms
C. Sherlock, A. H. Thiery, G. O. Roberts, and J. S. Rosenthal · 2014
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Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms
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The use of a single pseudo-sample in approximate Bayesian computation
L. Bornn, N. Pillai, A. Smith, and D. Woodard · 2015
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Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
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