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Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference.
1901
Earlier work this paper cites.
1905
Earlier work this paper cites.
Robbins, H. and Monro, S. (1951). A stochastic approximation method. The annals of mathematical statistics
1951
Earlier work this paper cites.
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. and Teller, E. (1953). Equation of state calculations by fast computing machines. The journal of chemical physics
1953
Earlier work this paper cites.
Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika
1970
Earlier work this paper cites.
Ermak, D. L. (1975). A computer simulation of charged particles in solution. I. Technique and equilibrium properties. The Journal of Chemical Physics
1975
Earlier work this paper cites.
Parisi, G. (1981). Correlation functions and computer simulations. Nuclear Physics B
1981
Earlier work this paper cites.
Ripley, B. D. (1987). Stochastic simulation
1987
Earlier work this paper cites.
Besag, J. (1994). Comments on “representations of knowledge in complex systems” by u. grenander and mi miller. J. Roy. Statist. Soc. Ser. B
1994
Earlier work this paper cites.
Meyn, S. P., Tweedie, R. L. et al
1994
Earlier work this paper cites.
Roberts, G. O. and Tweedie, R. L. (1996). Exponential convergence of Langevin distributions and their discrete approximations. Bernoulli
1996
Earlier work this paper cites.
Roberts, G., Rosenthal, J. et al
1997
Earlier work this paper cites.
Brooks, S. P. and Gelman, A. (1998). General methods for monitoring convergence of iterative simulations. Journal of computational and graphical statistics
1998
Earlier work this paper cites.
Roberts, G. O. and Rosenthal, J. S. (1998). Optimal scaling of discrete approximations to Langevin diffusions. Journal of the Royal Statistical Society: Series B (Statistical Methodology)
1998
Earlier work this paper cites.
Lunn, D. J., Thomas, A., Best, N. and Spiegelhalter, D. (2000). Winbugs-a bayesian modelling framework: concepts, structure, and extensibility. Statistics and computing
2000
Earlier work this paper cites.
Minka, T. P. (2001). Expectation propagation for approximate bayesian inference. In: Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
2001
Earlier work this paper cites.
Roberts, G. O. and Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statistical science
2001
Earlier work this paper cites.
Gibbs, A. L. and Su, F. E. (2002). On choosing and bounding probability metrics. International statistical review
2002
Earlier work this paper cites.
Beck, A. and Teboulle, M. (2003). Mirror descent and nonlinear projected subgradient methods for convex optimization. Operations Research Letters
2003
Earlier work this paper cites.
Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research
2003
Earlier work this paper cites.
Plummer, M. et al
2003
Earlier work this paper cites.
Roberts, G. O., Rosenthal, J. S. et al
2004
Earlier work this paper cites.
Bishop, C. M. (2006). Pattern recognition and machine learning
2006
Earlier work this paper cites.
Fearnhead, P., Papaspiliopoulos, O. and Roberts, G. O. (2008). Particle filters for partially observed diffusions. Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2008
Earlier work this paper cites.
Salakhutdinov, R. and Mnih, A. (2008). Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th international conference on Machine learning
2008
Earlier work this paper cites.
LeCun, Y., Cortes, C. and Burges, C. (2010). Mnist handwritten digit database. AT&T Labs [Online]. Available: http://yann. lecun. com/exdb/mnist
2010
Earlier work this paper cites.
Brooks, S., Gelman, A., Jones, G. and Meng, X.-L. (2011). Handbook of markov chain monte carlo
2011
Earlier work this paper cites.
Girolami, M. and Calderhead, B. (2011). Riemann manifold langevin and hamiltonian monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2011
Earlier work this paper cites.
Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In: Handbook of Markov chain Monte Carlo
2011
Cited alongside, same era.
Welling, M. and Teh, Y. W. (2011). Bayesian learning via stochastic gradient Langevin dynamics. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11)
2011
Cited alongside, same era.
2012
Cited alongside, same era.
Neal, R. M. (2012). Bayesian learning for neural networks
2012
Cited alongside, same era.
Pillai, N. S., Stuart, A. M., Thiéry, A. H. et al
2012
Cited alongside, same era.
2016
Later among the works it cites.
Vollmer, S. J., Zygalakis, K. C. and Teh, Y. W. (2016). Exploration of the (non-) asymptotic bias and variance of stochastic gradient Langevin dynamics. The Journal of Machine Learning Research
2016
Later among the works it cites.
Blei, D. M., Kucukelbir, A. and McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association
2017
Later among the works it cites.
2017
Later among the works it cites.
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2013
Cited alongside, same era.
Patterson, S. and Teh, Y. W. (2013). Stochastic gradient riemannian langevin dynamics on the probability simplex. In: Advances in Neural Information Processing Systems
2013
Cited alongside, same era.
Bardenet, R., Doucet, A. and Holmes, C. (2014). Towards scaling up markov chain monte carlo: an adaptive subsampling approach. In: International Conference on Machine Learning (ICML)
2014
Cited alongside, same era.
Chen, T., Fox, E. and Guestrin, C. (2014). Stochastic gradient Hamiltonian Monte Carlo. In: International Conference on Machine Learning
2014
Cited alongside, same era.
Ding, N., Fang, Y., Babbush, R., Chen, C., Skeel, R. D. and Neven, H. (2014). Bayesian sampling using stochastic gradient thermostats. In: Advances in neural information processing systems
2014
Cited alongside, same era.
Hoffman, M. D. and Gelman, A. (2014). The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo. Journal of Machine Learning Research
2014
Cited alongside, same era.
Korattikara, A., Chen, Y. and Welling, M. (2014). Austerity in mcmc land: Cutting the metropolis-hastings budget. In: International Conference on Machine Learning
2014
Cited alongside, same era.
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P. and Riddell, A. (2017). Stan: A probabilistic programming language. Journal of Statistical Software
2017
Later among the works it cites.
2017
Later among the works it cites.
Dalalyan, A. S. (2017). Theoretical guarantees for approximate sampling from smooth and log-concave densities. Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2017
Later among the works it cites.
2017
Later among the works it cites.
Durmus, A. and Moulines, E. (2017). Nonasymptotic convergence analysis for the unadjusted Langevin algorithm. The Annals of Applied Probability
2017
Later among the works it cites.
Gorham, J. and Mackey, L. (2017). Measuring sample quality with kernels. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
de Valpine, P., Turek, D., Paciorek, C. J., Anderson-Bergman, C., Lang, D. T. and Bodik, R. (2017). Programming with models: writing statistical algorithms for general model structures with nimble. Journal of Computational and Graphical Statistics
2017
Later among the works it cites.
2018
Later among the works it cites.
Andersen, M., Winther, O., Hansen, L. K., Poldrack, R. and Koyejo, O. (2018). Bayesian structure learning for dynamic brain connectivity. In: International Conference on Artificial Intelligence and Statistics
2018
Later among the works it cites.
Baker, J., Fearnhead, P., Fox, E. and Nemeth, C. (2018). Large-scale stochastic sampling from the probability simplex. In: Advances in Neural Information Processing Systems
2018
Later among the works it cites.
Bouchard-Côté, A., Vollmer, S. J. and Doucet, A. (2018). The bouncy particle sampler: A nonreversible rejection-free markov chain monte carlo method. Journal of the American Statistical Association
2018
Later among the works it cites.
Brosse, N., Durmus, A. and Moulines, É. (2018). The promises and pitfalls of Stochastic Gradient Langevin Dynamics. In: Advances in Neural Information Processing Systems
2018
Later among the works it cites.
Bubeck, S., Eldan, R. and Lehec, J. (2018). Sampling from a log-concave distribution with projected langevin monte carlo. Discrete & Computational Geometry
2018
Later among the works it cites.
2018
Later among the works it cites.
Fearnhead, P., Bierkens, J., Pollock, M., Roberts, G. O. et al
2018
Later among the works it cites.
Hsieh, Y.-P., Kavis, A., Rolland, P. and Cevher, V. (2018). Mirrored langevin dynamics. In: Advances in Neural Information Processing Systems
2018
Later among the works it cites.
2018
Later among the works it cites.
Nemeth, C., Sherlock, C. et al
2018
Later among the works it cites.
Quiroz, M., Kohn, R., Villani, M. and Tran, M.-N. (2018). Speeding up mcmc by efficient data subsampling. Journal of the American Statistical Association
2018
Later among the works it cites.
Srivastava, S., Li, C. and Dunson, D. B. (2018). Scalable bayes via barycenter in wasserstein space. The Journal of Machine Learning Research
2018
Later among the works it cites.
Bierkens, J., Fearnhead, P. and Roberts, G. (2019). The zig-zag process and super-efficient sampling for Bayesian analysis of big data. Annals of Statistics, to appear
2019
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