Fetching the paper…
Reading the bibliography…
Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling.
1912
Earlier work this paper cites.
Kirkpatrick, S., Jr., C. D. G. & Vecchi, M. P. (1983), ‘Optimization by simulated annealing’, Science
1983
Earlier work this paper cites.
Duane, S., Kennedy, A. D., Pendleton, B. J. & Roweth, D. (1987), ‘Hybrid Monte Carlo’, Physics Letters B
1987
Earlier work this paper cites.
Geyer, C. J. (1991), ‘Markov chain Monte Carlo maximum likelihood’
1991
Earlier work this paper cites.
Horowitz, A. M. (1991), ‘A generalized guided Monte Carlo algorithm’, Physics Letters B
1991
Earlier work this paper cites.
Gordon, N. J., Salmond, D. J. & Smith, A. F. M. (1993), Novel approach to nonlinear/non-Gaussian Bayesian state estimation, in
1993
Earlier work this paper cites.
Grenander, U. & Miller, M. I. (1994), ‘Representations of knowledge in complex systems’, Journal of the Royal Statistical Society: Series B (Methodological)
1994
Earlier work this paper cites.
Kong, A., Liu, J. S. & Wong, W. H. (1994), ‘Sequential imputations and Bayesian missing data problems’, Journal of the American Statistical Association
1994
Earlier work this paper cites.
Neal, R. M. (1994), ‘An improved acceptance procedure for the hybrid Monte Carlo algorithm’, Journal of Computational Physics
1994
Earlier work this paper cites.
Rubin, D. B. (1996), ‘Multiple imputation after 18+ years’, Journal of the American Statistical Association
1996
Earlier work this paper cites.
Chen, M.-H. & Shao, Q.-M. (1997), ‘On Monte Carlo methods for estimating ratios of normalizing constants’, The Annals of Statistics
1997
Earlier work this paper cites.
Jarzynski, C. (1997), ‘Nonequilibrium equality for free energy differences’, Physical Review Letters
1997
Earlier work this paper cites.
Gelman, A. & Meng, X.-L. (1998), ‘Simulating normalizing constants: From importance sampling to bridge sampling to path sampling’, Statistical Science
1998
Earlier work this paper cites.
Blackard, J. A. (2000), Comparison of neural networks and discriminant analysis in predicting forest cover types, PhD thesis, Department of Forest Sciences, Colorado State University
2000
Earlier work this paper cites.
Jarzynski, C. (2000), ‘Hamiltonian derivation of a detailed fluctuation theorem’, Journal of Statistical Physics
2000
Earlier work this paper cites.
Gilks, W. R. & Berzuini, C. (2001), ‘Following a moving target — Monte Carlo inference for dynamic Bayesian models’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2001
Earlier work this paper cites.
Neal, R. M. (2001), ‘Annealed importance sampling’, Statistics and Computing
2001
Earlier work this paper cites.
Chopin, N. (2002), ‘A sequential particle filter method for static models’, Biometrika
2002
Earlier work this paper cites.
Collobert, R., Bengio, S. & Bengio, Y. (2002), A parallel mixture of SVMs for very large scale problems, in
2002
Earlier work this paper cites.
Murphy, K. M. & Topel, R. H. (2002), ‘Estimation and inference in two-step econometric models’, Journal of Business & Economic Statistics
2002
Earlier work this paper cites.
Nummelin, E. (2002), ‘MC’s for MCMC’ists’, International Statistical Review
2002
Earlier work this paper cites.
Whitfield, T., Bu, L. & Straub, J. (2002), ‘Generalized parallel sampling’, Physica A: Statistical Mechanics and its Applications
2002
Earlier work this paper cites.
2004
Earlier work this paper cites.
Del Moral, P. (2004), Feynman–Kac formulae
2004
Earlier work this paper cites.
Neal, R. M. (2005), Hamiltonian importance sampling, in
2005
Earlier work this paper cites.
Del Moral, P., Doucet, A. & Jasra, A. (2006), ‘Sequential Monte Carlo samplers’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2006
Earlier work this paper cites.
Kostov, S. (2006), Hamiltonian sequential Monte Carlo and normalizing constants, PhD thesis, University of Bristol
2006
Earlier work this paper cites.
Schöll-Paschinger, E. & Dellago, C. (2006), ‘A proof of Jarzynski’s nonequilibrium work theorem for dynamical systems that conserve the canonical distribution’, The Journal of Chemical Physics
2006
Earlier work this paper cites.
Skilling, J. (2006), ‘Nested sampling for general Bayesian computation’, Bayesian Analysis
2006
Earlier work this paper cites.
Cappé, O., Godsill, S. J. & Moulines, E. (2007), ‘An overview of existing methods and recent advances in sequential Monte Carlo’, Proceedings of the IEEE
2007
Earlier work this paper cites.
Del Moral, P., Doucet, A. & Jasra, A. (2007), Sequential Monte Carlo for Bayesian computation, in
2007
Earlier work this paper cites.
2007
Earlier work this paper cites.
Ng, P. & Maechler, M. (2007), ‘A fast and efficient implementation of qualitatively constrained quantile smoothing splines’, Statistical Modelling
2007
Earlier work this paper cites.
Sisson, S. A., Fan, Y. & Tanaka, M. M. (2007), ‘Sequential Monte Carlo without likelihoods’, Proceedings of the National Academy of Sciences
2007
Earlier work this paper cites.
Cornebise, J., Moulines, É. & Olsson, J. (2008), ‘Adaptive methods for sequential importance sampling with application to state space models’, Statistics and Computing
2008
Earlier work this paper cites.
Vaikuntanathan, S. & Jarzynski, C. (2008), ‘Escorted free energy simulations: Improving convergence by reducing dissipation’, Physical Review Letters
2008
Earlier work this paper cites.
Bernardo, J. M. & Smith, A. F. (2009), Bayesian theory
2009
Earlier work this paper cites.
Diaconis, P. (2009), ‘The Markov chain Monte Carlo revolution’, Bulletin of the American Mathematical Society
2009
Earlier work this paper cites.
Andrieu, C., Doucet, A. & Holenstein, R. (2010), ‘Particle Markov chain Monte Carlo methods’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2010
Earlier work this paper cites.
Brockwell, A., Del Moral, P. & Doucet, A. (2010), ‘Sequentially interacting Markov chain Monte Carlo methods’, The Annals of Statistics
2010
Earlier work this paper cites.
Brooks, S., Gelman, A., Jones, G. & Meng, X.-L. (2011), Handbook of Markov chain Monte Carlo
2011
Earlier work this paper cites.
Cérou, F., Del Moral, P. & Guyader, A. (2011), A nonasymptotic theorem for unnormalized Feynman–Kac particle models, in
2011
Earlier work this paper cites.
Jasra, A., Stephens, D. A., Doucet, A. & Tsagaris, T. (2011), ‘Inference for Lévy-driven stochastic volatility models via adaptive sequential Monte Carlo’, Scandinavian Journal of Statistics
2011
Earlier work this paper cites.
Nilmeier, J. P., Crooks, G. E., Minh, D. D. & Chodera, J. D. (2011), ‘Nonequilibrium candidate Monte Carlo is an efficient tool for equilibrium simulation’, Proceedings of the National Academy of Sciences
2011
Cited alongside, same era.
Robert, C. & Casella, G. (2011), ‘A short history of Markov chain Monte Carlo: Subjective recollections from incomplete data’, Statistical Science
2011
Cited alongside, same era.
Bacaër, N. (2012), ‘The model of Kermack and McKendrick for the plague epidemic in Bombay and the type reproduction number with seasonality’, Journal of Mathematical Biology
2012
Cited alongside, same era.
Cérou, F., Del Moral, P., Furon, T. & Guyader, A. (2012), ‘Sequential Monte Carlo for rare event estimation’, Statistics and Computing
2012
Cited alongside, same era.
Cisewski, J. & Hannig, J. (2012), ‘Generalized fiducial inference for normal linear mixed models’, The Annals of Statistics
Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P. & Riddell, A. (2017), ‘Stan: A probabilistic programming language’, Journal of Statistical Software
2017
Later among the works it cites.
Chopin, N. & Ridgway, J. (2017), ‘Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation’, Statistical Science
2017
Later among the works it cites.
Griffin, J. E. (2017), ‘Sequential Monte Carlo methods for mixtures with normalized random measures with independent increments priors’, Statistics and Computing
2017
Later among the works it cites.
Holmes, C. C. & Walker, S. G. (2017), ‘Assigning a value to a power likelihood in a general Bayesian model’, Biometrika
2017
Later among the works it cites.
Kempinska, K. & Shawe-Taylor, J. (2017), Adversarial sequential Monte Carlo, in
2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2012
Cited alongside, same era.
Del Moral, P., Doucet, A. & Jasra, A. (2012), ‘An adaptive sequential Monte Carlo method for approximate Bayesian computation’, Statistics and Computing
2012
Cited alongside, same era.
Dellaportas, P. & Kontoyiannis, I. (2012), ‘Control variates for estimation based on reversible Markov chain Monte Carlo samplers’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2012
Cited alongside, same era.
Jun, S.-H., Wang, L. & Bouchard-Côté, A. (2012), Entangled Monte Carlo, in
2012
Cited alongside, same era.
2012
Cited alongside, same era.
2012
Cited alongside, same era.
Chan, H. P. & Lai, T. L. (2013), ‘A general theory of particle filters in hidden Markov models and some applications’, The Annals of Statistics
2013
Cited alongside, same era.
Chopin, N., Jacob, P. E. & Papaspiliopoulos, O. (2013), ‘SMC 2 : an efficient algorithm for sequential analysis of state space models’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2013
Cited alongside, same era.
Later among the works it cites.
Lindsten, F., Johansen, A. M., Naesseth, C. A., Kirkpatrick, B., Schön, T. B., Aston, J. A. D. & Bouchard-Côté, A. (2017), ‘Divide-and-Conquer with Sequential Monte Carlo’, Journal of Computational and Graphical Statistics
2017
Later among the works it cites.
Brosse, N., Durmus, A. & Moulines, É. (2018), ‘Normalizing constants of log-concave densities’, Electronic Journal of Statistics
2018
Later among the works it cites.
Chatterjee, S. & Diaconis, P. (2018), ‘The sample size required in importance sampling’, The Annals of Applied Probability
2018
Later among the works it cites.
Lee, A. & Whiteley, N. (2018), ‘Variance estimation in the particle filter’, Biometrika
2018
Later among the works it cites.
Murray, L. M. & Schön, T. B. (2018), ‘Automated learning with a probabilistic programming language: Birch’, Annual Reviews in Control
2018
Later among the works it cites.
Naesseth, C., Linderman, S., Ranganath, R. & Blei, D. (2018), Variational Sequential Monte Carlo, in
2018
Later among the works it cites.
Nishimura, A. & Dunson, D. (2018), ‘Recycling intermediate steps to improve Hamiltonian Monte Carlo’, Bayesian Analysis
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
Gerber, M., Chopin, N. & Whiteley, N. (2019), ‘Negative association, ordering and convergence of resampling methods’, The Annals of Statistics
2019
Later among the works it cites.
Hooten, M. B., Johnson, D. S. & Brost, B. M. (2019), ‘Making recursive Bayesian inference accessible’, The American Statistician
2019
Later among the works it cites.
Huggins, J. H. & Roy, D. M. (2019), ‘Sequential Monte Carlo as approximate sampling: bounds, adaptive resampling via ∞ \infty -ESS, and an application to particle Gibbs’, Bernoulli
2019
Later among the works it cites.
Middleton, L., Deligiannidis, G., Doucet, A. & Jacob, P. E. (2019), Unbiased smoothing using particle independent Metropolis-Hastings, in
2019
Later among the works it cites.
Paulin, D., Jasra, A. & Thiery, A. (2019), ‘Error bounds for sequential Monte Carlo samplers for multimodal distributions’, Bernoulli
2019
Later among the works it cites.
Shao, S., Jacob, P. E., Ding, J. & Tarokh, V. (2019), ‘Bayesian model comparison with the Hyvärinen score: computation and consistency’, Journal of the American Statistical Association
2019
Later among the works it cites.
South, L. F., Pettitt, A. N. & Drovandi, C. C. (2019), ‘Sequential Monte Carlo samplers with independent Markov chain Monte Carlo proposals’, Bayesian Analysis
2019
Later among the works it cites.
Vempala, S. S. & Wibisono, A. (2019), Rapid convergence of the unadjusted Langevin algorithm: log-Sobolev suffices, in
2019
Later among the works it cites.
Buchholz, A., Chopin, N. & Jacob, P. E. (2020), ‘Adaptive tuning of Hamiltonian Monte Carlo within sequential Monte Carlo’, Bayesian Analysis (to appear)
2020
Closest in time.
Chopin, N. & Papaspiliopoulos, O. (2020), An introduction to sequential Monte Carlo
2020
Closest in time.
Dunson, D. B. & Johndrow, J. (2020), ‘The Hastings algorithm at fifty’, Biometrika
2020
Closest in time.
Everitt, R. G., Culliford, R., Medina-Aguayo, F. & Wilson, D. J. (2020), ‘Sequential Monte Carlo with transformations’, Statistics and Computing
2020
Closest in time.
Finke, A., Doucet, A. & Johansen, A. M. (2020), ‘Limit theorems for sequential MCMC methods’, Advances in Applied Probability
2020
Closest in time.
Gunawan, D., Dang, K.-D., Quiroz, M., Kohn, R. & Tran, M.-N. (2020), ‘Subsampling sequential Monte Carlo for static Bayesian models’, Statistics and Computing
2020
Closest in time.
Heng, J., Bishop, A. N., Deligiannidis, G. & Doucet, A. (2020), ‘Controlled sequential Monte Carlo’, The Annals of Statistics
2020
Closest in time.
Jacob, P. E., O’Leary, J. & Atchadé, Y. F. (2020), ‘Unbiased Markov chain Monte Carlo methods with couplings’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2020
Closest in time.
Roy, V. (2020), ‘Convergence diagnostics for Markov chain Monte Carlo’, Annual Review of Statistics and Its Application
2020
Closest in time.
Arbel, M., Matthews, A. & Doucet, A. (2021), Annealed flow transport Monte Carlo, in
2021
Closest in time.
Corenflos, A., Thornton, J., Deligiannidis, G. & Doucet, A. (2021), Differentiable particle filtering via entropy-regularized optimal transport, in
2021
Closest in time.
Du, Q. & Guyader, A. (2021), ‘Variance estimation in adaptive sequential Monte Carlo’, The Annals of Applied Probability
2021
Closest in time.
Fulop, A., Heng, J., Li, J. & Liu, H. (2021), ‘Bayesian estimation of long-run risk models using sequential Monte Carlo’, Journal of Econometrics
2021
Closest in time.
Geffner, T. & Domke, J. (2021), ‘MCMC variational inference via uncorrected Hamiltonian annealing’, Advances in Neural Information Processing Systems
2021
Closest in time.
Grinsztajn, L., Semenova, E., Margossian, C. C. & Riou, J. (2021), ‘Bayesian workflow for disease transmission modeling in Stan’, Statistics in Medicine
2021
Closest in time.
Heng, J., Doucet, A. & Pokern, Y. (2021), ‘Gibbs flow for approximate transport with applications to Bayesian computation’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2021
Closest in time.
Syed, S., Romaniello, V., Campbell, T. & Bouchard-Côté, A. (2021), Parallel tempering on optimized paths, in
2021
Closest in time.
Zhang, G., Hsu, K., Li, J., Finn, C. & Grosse, R. B. (2021), ‘Differentiable annealed importance sampling and the perils of gradient noise’, Advances in Neural Information Processing Systems
2021
Closest in time.
Dau, H.-D. & Chopin, N. (2022), ‘Waste-free sequential Monte Carlo’, Journal of the Royal Statistical Society: Series B (Statistical Methodology)
2022
Closest in time.