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Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention.
Spectral subsampling MCMC for stationary time series
Salomone, R., Quiroz, M., Kohn, R., Villani, M., and Tran, M.-N. (2020) · 1910
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Equation of state calculations by fast computing machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953) · 1953
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Monte Carlo sampling methods using Markov chains and their applications
Hastings, W. K. (1970) · 1970
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Unbiased multi-step estimators for the Monte Carlo evaluation of certain functional integrals
Wagner, W. (1988) · 1988
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Hand-book on statistical distributions for experimentalists
Walck, C. (1996) · 1996
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Weak convergence and optimal scaling of random walk Metropolis algorithms
Roberts, G. O., Gelman, A., and Gilks, W. R. (1997) · 1997
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Asymptotic Statistics
Van der Vaart, A. W. (1998) · 1998
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A parallel mixture of SVMs for very large scale problems
Collobert, R., Bengio, S., and Bengio, Y. (2002) · 2002
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Li, R., Wang, X., Zha, H., and Tao, M. (2020) · 2002
Earlier work this paper cites.
General state space Markov chains and MCMC algorithms
Roberts, G. O. and Rosenthal, J. S. (2004) · 2004
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CODA: Convergence diagnosis and output analysis for MCMC
Plummer, M., Best, N., Cowles, K., and Vines, K. (2006) · 2006
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An Introduction to Probability Theory and its Applications
Feller, W. (2008) · 2008
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The pseudo-marginal approach for efficient Monte Carlo computations
Andrieu, C. and Roberts, G. O. (2009) · 2009
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A methodological framework for Monte Carlo probabilistic inference for diffusion processes
Papaspiliopoulos, O. (2009) · 2009
Cited alongside, same era.
MCMC using Hamiltonian dynamics
Neal, R. M. (2011) · 2011
Cited alongside, same era.
Markov Chains and Stochastic Stability
Meyn, S. P. and Tweedie, R. L. (2012) · 2012
Cited alongside, same era.
On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
Pitt, M. K., Silva, R. S., Giordani, P., and Kohn, R. (2012) · 2012
Cited alongside, same era.
Searching for exotic particles in high-energy physics with deep learning
Baldi, P., Sadowski, P., and Whiteson, D. (2014) · 2014
Cited alongside, same era.
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach
Bardenet, R., Doucet, A., and Holmes, C. (2014) · 2014
Unbiased estimation with square root convergence for SDE models
Rhee, C. and Glynn, P. W. (2015) · 2015
Later among the works it cites.
On the efficiency of pseudo-marginal random walk Metropolis algorithms
Sherlock, C., Thiery, A. H., Roberts, G. O., and Rosenthal, J. S. (2015) · 2015
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On Markov chain Monte Carlo methods for tall data
Bardenet, R., Doucet, A., and Holmes, C. (2017) · 2017
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Discontinuous Hamiltonian Monte Carlo for discrete parameters and discontinuous likelihoods
Nishimura, A., Dunson, D., and Lu, J. (2017) · 2017
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The block pseudo-marginal sampler
Tran, M.-N., Kohn, R., Quiroz, M., and Villani, M. (2017) · 2017
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Taking the twists into account: Predicting firm bankruptcy risk with splines of financial ratios
Giordani, P., Jacobson, T., Von Schedvin, E., and Villani, M. (2014) · 2014
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Austerity in MCMC land: Cutting the Metropolis-Hastings budget
Korattikara, A., Chen, Y., and Welling, M. (2014) · 2014
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Firefly Monte Carlo: Exact MCMC with subsets of data
Maclaurin, D. and Adams, R. P. (2014) · 2014
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Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
Doucet, A., Pitt, M., Deligiannidis, G., and Kohn, R. (2015) · 2015
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On nonnegative unbiased estimators
Jacob, P. E. and Thiery, A. H. (2015) · 2015
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On Russian roulette estimates for Bayesian inference with doubly-intractable likelihoods
Lyne, A.-M., Girolami, M., Atchade, Y., Strathmann, H., and Simpson, D. (2015) · 2015
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The bouncy particle sampler: A nonreversible rejection-free Markov chain Monte Carlo method
Bouchard-Côté, A., Vollmer, S. J., and Doucet, A. (2018) · 2018
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The correlated pseudomarginal method
Deligiannidis, G., Doucet, A., and Pitt, M. K. (2018) · 2018
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Two-stage Metropolis-Hastings for tall data
Payne, R. D. and Mallick, B. K. (2018) · 2018
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Accelerating Metropolis-Hastings algorithms by delayed acceptance
Banterle, M., Grazian, Clara, L. A., and Robert, C. P. (2019) · 2019
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The zig-zag process and super-efficient sampling for Bayesian analysis of big data
Bierkens, J., Fearnhead, P., and Roberts, G. (2019) · 2019
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Hamiltonian Monte Carlo with energy conserving subsampling
Dang, K.-D., Quiroz, M., Kohn, R., Tran, M.-N., and Villani, M. (2019) · 2019
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Speeding up MCMC by efficient data subsampling
Quiroz, M., Kohn, R., Villani, M., and Tran, M.-N. (2019) · 2019
Closest in time.