mlsanityData to 7 Oct 2026

Nested Sequential Monte Carlo Methods

arXiv 1502.02536 ↗First posted 9 Feb 2015stat.CO stat.ME stat.MLcited by 19

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Abstract (arXiv)

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Feb 2015.

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Citing papers per year: 2021: 6; 2022: 3; 2023: 2; 2024: 3; 2025: 2; 2026: 3.

19 papers.

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  1. 2024Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decodingcited by 127
  2. 2021A Survey of Monte Carlo Methods for Parameter Estimationcited by 23
  3. 2024Doing Experiments and Revising Rules with Natural Language and Probabilistic Reasoningcited by 23
  4. 2025Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Samplingcited by 20
  5. 2021Differentiable Particle Filtering without Modifying the Forward Passcited by 18
  6. 2021Nested Variational Inferencecited by 14
  7. 2022Illusory Attacks: Information-Theoretic Detectability Matters in Adversarial Attackscited by 12
  8. 2021Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inferencecited by 10
  9. 2022Recursive Monte Carlo and Variational Inference with Auxiliary Variablescited by 8
  10. 2021Learning Proposals for Probabilistic Programs with Inference Combinatorscited by 6
  11. 2021Variational Marginal Particle Filterscited by 6
  12. 2023NAS-X: Neural Adaptive Smoothing via Twistingcited by 4
7 more
  1. 2026Localizing Credit at the Divergence: Path-Conditioned Self-Distillation for LLM Reasoningcited by 4
  2. 2024Divide-and-Conquer Predictive Coding: a structured Bayesian inference algorithmcited by 3
  3. 2025Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Spacecited by 2
  4. 2022Optimization of Annealed Importance Sampling Hyperparameterscited by 1
  5. 2026Discrete Diffusion Inference-Time Control with Nested Sequential Monte Carlocited by 1
  6. 2023String Diagrams with Factorized Densitiescited by 0
  7. 2026Nonlinear Bayesian Estimator for Parameter Learning: A Fixed-Point Characterizationcited by 0