Fetching the paper…
Reading the bibliography…
Performing numerical integration when the integrand itself cannot be evaluated point-wise is a challenging task that arises in statistical analysis, notably in Bayesian inference for models with intractable likelihood functions.
Crystal statistics. i. a two-dimensional model with an order-disorder transition
L. Onsager · 1944
Earlier work this paper cites.
Bias properties of budget constrained simulations
P. W. Glynn and P. Heidelberger · 1990
Earlier work this paper cites.
The asymptotic efficiency of simulation estimators
P. W. Glynn and W. Whitt · 1992
Earlier work this paper cites.
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
N. J. Gordon, D. J. Salmond, and A. F. Smith · 1993
Earlier work this paper cites.
Regeneration in Markov chain samplers
P. Mykland, L. Tierney, and B. Yu · 1995
Earlier work this paper cites.
Studying convergence of Markov chain Monte Carlo algorithms using coupled sample paths
V. E. Johnson · 1996
Earlier work this paper cites.
Exact sampling with coupled Markov chains and applications to statistical mechanics
J. G. Propp and D. B. Wilson · 1996
Earlier work this paper cites.
Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
G. O. Roberts and R. L. Tweedie · 1996
Earlier work this paper cites.
Weak convergence and optimal scaling of random walk metropolis algorithms
G. O. Roberts, A. Gelman, and W. R. Gilks · 1997
Earlier work this paper cites.
A coupling-regeneration scheme for diagnosing convergence in Markov chain Monte Carlo algorithms
V. E. Johnson · 1998
Earlier work this paper cites.
Geometric ergodicity of metropolis algorithms
S. F. Jarner and E. Hansen · 2000
Earlier work this paper cites.
A noisy Monte Carlo algorithm
L. Lin, K. Liu, and J. Sloan · 2000
Earlier work this paper cites.
Parallel computing and Monte Carlo algorithms
J. S. Rosenthal · 2000
Earlier work this paper cites.
Coupling, Stationarity, and Regeneration
H. Thorisson · 2000
Earlier work this paper cites.
Polynomial convergence rates of Markov chains
S. F. Jarner and G. O. Roberts · 2002
Earlier work this paper cites.
Estimation of population growth of decline in genetically monitored populations
M. Beaumont · 2003
Earlier work this paper cites.
Practical drift conditions for subgeometric rates of convergence
R. Douc, G. Fort, E. Moulines, and P. Soulier · 2004
Earlier work this paper cites.
Identification of regeneration times in MCMC simulation, with application to adaptive schemes
A. E. Brockwell and J. B. Kadane · 2005
Cited alongside, same era.
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
Cited alongside, same era.
MCMC for doubly-intractable distributions
I. Murray, Z. Ghahramani, and D. J. MacKay · 2006
Cited alongside, same era.
Coda: convergence diagnosis and output analysis for MCMC
M. Plummer, N. Best, K. Cowles, and K. Vines · 2006
Cited alongside, same era.
Rapid changes in thalamic firing synchrony during repetitive whisker stimulation
S. Temereanca, E. N. Brown, and D. J. Simons · 2008
Cited alongside, same era.
The pseudo-marginal approach for efficient Monte Carlo computations
C. Andrieu and G. O. Roberts · 2009
Block-wise pseudo-marginal Metropolis–Hastings
M.-N. Tran, R. Kohn, M. Quiroz, and M. Villani · 2016
Later among the works it cites.
Circularly-coupled Markov chain sampling
R. M. Neal · 2017
Later among the works it cites.
On the utility of Metropolis-Hastings with asymmetric acceptance ratio
C. Andrieu, A. Doucet, S. Yıldırım, and N. Chopin · 2018
Closest in time.
Coupling and convergence for Hamiltonian Monte Carlo
N. Bou-Rabee, A. Eberle, and R. Zimmer · 2018
Closest in time.
The correlated pseudomarginal method
G. Deligiannidis, A. Doucet, and M. K. Pitt · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Markov Chains and Stochastic Stability
S. Meyn and R. L. Tweedie · 2009
Cited alongside, same era.
Discrete Choice Methods with Simulation
K. E. Train · 2009
Cited alongside, same era.
Particle Markov chain Monte Carlo methods
C. Andrieu, A. Doucet, and R. Holenstein · 2010
Cited alongside, same era.
Coupled MCMC with a randomized acceptance probability
G. K. Nicholls, C. Fox, and A. M. Watt · 2012
Cited alongside, same era.
Exact sampling for the Ising model at all temperatures
M. Ullrich · 2013
Cited alongside, same era.
A lognormal central limit theorem for particle approximations of normalizing constants
J. Bérard, P. Del Moral, and A. Doucet · 2014
Cited alongside, same era.
Markov Chains
R. Douc, E. Moulines, P. Priouret, and P. Soulier · 2018
Closest in time.
British Election Study Internet Panel Waves 1-13, 2018
E. Fieldhouse, J. Green, G. Evans, H. Schmitt, C. V. D. Eijk, J. Mellon, and C. Prosser · 2018
Closest in time.
Bayesian inference in the presence of intractable normalizing functions
J. Park and M. Haran · 2018
Closest in time.
Estimating a separably Markov random field from binary observations
Y. Zhang, N. Malem-Shinitski, S. A. Allsop, K. M. Tye, and D. Ba · 2018
Closest in time.
Unbiased Hamiltonian Monte Carlo with couplings
J. Heng and P. E. Jacob · 2019
Closest in time.
Smoothing with couplings of conditional particle filters
P. E. Jacob, F. Lindsten, and T. B. Schön · 2019
Closest in time.
Unbiased smoothing using particle independent Metropolis–Hastings
L. Middleton, G. Deligiannidis, A. Doucet, and P. E. Jacob · 2019
Closest in time.
Controlled sequential Monte Carloo
J. Heng, A. Bishop, G. Deligiannidis, and A. Doucet · 2020
Closest in time.
Unbiased Markov chain Monte Carlo with couplings
P. E. Jacob, J. O’Leary, and Y. F. Atchadé · 2020
Closest in time.
Coupled conditional backward sampling particle filter
A. Lee, S. S. Singh, and M. Vihola · 2020
Closest in time.
Large sample asymptotics of the pseudo-marginal method
S. M. Schmon, G. Deligiannidis, A. Doucet, and M. K. Pitt · 2020
Closest in time.