mlsanityData to 7 Oct 2026

An invitation to sequential Monte Carlo samplers

arXiv 2007.11936 ↗First posted 23 Jul 2020stat.CO stat.MEcited by 33

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Abstract (arXiv)

Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approximate distributions of interest and their normalizing constants. These samplers originate from particle filtering for state space models and have become general and scalable sampling techniques. This article describes sequential Monte Carlo samplers and their possible implementations, arguing that they remain under-used in statistics, despite their ability to perform sequential inference and to leverage parallel processing resources among other potential benefits.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jul 2020.

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Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 11; 2025: 8; 2026: 6.

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  1. 2021Annealed Flow Transport Monte Carlocited by 90
  2. 2022Continual Repeated Annealed Flow Transport Monte Carlocited by 66
  3. 2023Monte Carlo guided Diffusion for Bayesian linear inverse problemscited by 65
  4. 2024Particle Denoising Diffusion Samplercited by 63
  5. 2022Score-Based Diffusion meets Annealed Importance Samplingcited by 51
  6. 2024Sequential Controlled Langevin Diffusionscited by 47
  7. 2022Probability flow solution of the Fokker-Planck equationcited by 46
  8. 2025From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingcited by 17
  9. 2023A connection between Tempering and Entropic Mirror Descentcited by 16
  10. 2025Test-time scaling of diffusions with flow mapscited by 15
  11. 2023Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyondcited by 13
  12. 2024Nesting Particle Filters for Experimental Design in Dynamical Systemscited by 12
21 more
  1. 2024Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamicscited by 10
  2. 2024Scaling of Stochastic Normalizing Flows in SU(3) lattice gauge theorycited by 10
  3. 2025Scaling flow-based approaches for topology sampling in SU(3) gauge theorycited by 6
  4. 2024PASOA- PArticle baSed Bayesian Optimal Adaptive designcited by 5
  5. 2024Stable Training of Normalizing Flows for High-dimensional Variational Inferencecited by 5
  6. 2024A Practical Diffusion Path for Samplingcited by 5
  7. 2025Sequential Monte Carlo approximations of Wasserstein--Fisher--Rao gradient flowscited by 5
  8. 2022Mixtures of Gaussian Process Experts with SMC2cited by 3
  9. 2024Comparison of parallel SMC and MCMC for Bayesian deep learningcited by 1
  10. 2026Efficient Stochastic Optimisation via Sequential Monte Carlocited by 1
  11. 2026Counterdiabatic Hamiltonian Monte Carlocited by 1
  12. 2026SMCEvolve: Principled Scientific Discovery via Sequential Monte Carlo Evolutioncited by 1
  13. 2024Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlocited by 0
  14. 2024Incremental Structure Discovery of Classification via Sequential Monte Carlocited by 0
  15. 2025Utilising Gradient-Based Proposals Within Sequential Monte Carlo Samplers for Training of Partial Bayesian Neural Networkscited by 0
  16. 2025Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensemblescited by 0
  17. 2025Briding Diffusion Posterior Sampling and Monte Carlo methods: a surveycited by 0
  18. 2025BOCO: Bayesian Online Contextual Optimization for Decision-Focused Online Learningcited by 0
  19. 2026Properties and limitations of geometric tempering for gradient flow dynamicscited by 0
  20. 2026Tempered Sequential Monte Carlo for Trajectory and Policy Optimization with Differentiable Dynamicscited by 0
  21. 2026Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applicationscited by 0