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The Metropolis-Hastings algorithm allows one to sample asymptotically from any probability distribution $\pi$.
Reversible jump Markov chain Monte Carlo for Bayesian model determination
P. Green · 1995
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Parallel n-ary speculative computation of simulated annealing
Andrew Sohn · 1995
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Nonequilibrium measurements of free energy differences for microscopically reversible Markovian systems
G.E. Crooks · 1998
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A guided walk Metropolis algorithm
Paul Gustafson · 1998
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A note on Metropolis Hastings kernels for general state spaces
Luke Tierney · 1998
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A noisy Monte Carlo algorithm
L. Lin, K.F. Liu, and J. Sloan · 2000
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Annealed importance sampling
R. Neal · 2001
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Comparison methods for stochastic models and risks
A Müller and D Stoyan · 2002
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Estimation of population growth of decline in genetically monitored populations
M. Beaumont · 2003
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On the use of local optimizations within Metropolis-Hastings updates
H�kon Tjelmeland and Jo Eidsvik · 2003
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Taking bigger Metropolis steps by dragging fast variables
Radford M. Neal · 2004
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Sequential Monte Carlo samplers
P. Del Moral, A. Doucet, and A. Jasra · 2006
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An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
J. Møller, A. N. Pettitt, R. Reeves, and K. K. Berthelsen · 2006
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MCMC for doubly-intractable distributions
I. Murray, Z. Ghahramani, and D. J. C. MacKay · 2006
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A tutorial on adaptive MCMC
Christophe Andrieu and Johannes Thoms · 2008
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The pseudo-marginal approach for efficient Monte Carlo computations
Christophe Andrieu and Gareth O. Roberts · 2009
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Particle Markov chain Monte Carlo methods
Christophe Andrieu, Arnaud Doucet, and Roman Holenstein · 2009
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Does waste recycling really improve the multi-proposal metropolis–hastings algorithm? an analysis based on control variates
Jean-Françcois Delmas and Benjamin Jourdain · 2009
Control variates for estimation based on reversible markov chain monte carlo samplers
Petros Dellaportas and Ioannis Kontoyiannis · 2012
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On the use of backward simulation in the particle Gibbs sampler
F. Lindsten and T. B. Schön · 2012
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Coupled MCMC with a randomised acceptance probability
G.K. Nicholls, C. Fox, and A.M. Watt · 2012
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Annealed importance sampling for reversible jump MCMC algorithms
G. Karagiannis and C. Andrieu · 2013
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Establishing some order amongst exact approximations of MCMCs
C. Andrieu and M. Vihola · 2014
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A backward particle interpretation of Feynman-Kac formulae
Pierre Del Moral, Arnaud Doucet, and Sumeetpal S Singh · 2010
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On the utility of graphics cards to perform massively parallel simulation of advanced monte carlo methods
Anthony Lee, Christopher Yau, Michael B Giles, Arnaud Doucet, and Christopher C Holmes · 2010
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Konstantin S Turitsyn, Michael Chertkov, and Marija Vucelja · 2011
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On the stability of sequential monte carlo methods in high dimensions
Alexandros Beskos, Dan Crisan, Ajay Jasra, et al · 2014
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Sampling normalizing constants in high dimensions using inhomogeneous diffusions
C. Andrieu, J. Ridgway, and N. Whiteley · 2016
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The use of a single pseudo-sample in approximate bayesian computation
Luke Bornn, Natesh S Pillai, Aaron Smith, and Dawn Woodard · 2017
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Markov chains and mixing times , volume 107
David A Levin and Yuval Peres · 2017
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Pseudo-marginal metropolis?hastings sampling using averages of unbiased estimators
Chris Sherlock, Alexandre H. Thiery, and Anthony Lee · 2017
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Scalable Monte Carlo inference for state-space models
S. Yıldırım, C. Andrieu, and A. Doucet · 2017
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