mlsanityData to 7 Oct 2026

Nesting Particle Filters for Experimental Design in Dynamical Systems

arXiv 2402.07868 ↗First posted 12 Feb 2024stat.ML cs.LG stat.MEcited by 12

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Abstract (arXiv)

In this paper, we propose a novel approach to Bayesian experimental design for non-exchangeable data that formulates it as risk-sensitive policy optimization. We develop the Inside-Out SMC2 algorithm, a nested sequential Monte Carlo technique to infer optimal designs, and embed it into a particle Markov chain Monte Carlo framework to perform gradient-based policy amortization. Our approach is distinct from other amortized experimental design techniques, as it does not rely on contrastive estimators. Numerical validation on a set of dynamical systems showcases the efficacy of our method in comparison to other state-of-the-art strategies.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 20 of its 61 references that are in the map. First posted on arXiv 12 Feb 2024.

Builds on

20 of 61 references.

References · 20

  1. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  3. 2019On Variational Bounds of Mutual Informationcited by 550
  4. 2023Modern Bayesian Experimental Designcited by 121
  5. 2021Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Designcited by 103
  6. 2017On Nesting Monte Carlo Estimatorscited by 82
  7. 2013Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizationscited by 68
  8. 2021Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoodscited by 61
  9. 2023Prediction-Oriented Bayesian Active Learningcited by 60
  10. 2020A Unifying View of Optimism in Episodic Reinforcement Learningcited by 50
  11. 2014On Particle Methods for Parameter Estimation in State-Space Modelscited by 47
  12. 2020Markovian Score Climbing: Variational Inference with KL(p||q)cited by 46
8 more
  1. 2022Optimizing Sequential Experimental Design with Deep Reinforcement Learningcited by 45
  2. 2020An invitation to sequential Monte Carlo samplerscited by 33
  3. 2016Sequential Bayesian optimal experimental design via approximate dynamic programmingcited by 27
  4. 2019Receding Horizon Curiositycited by 10
  5. 2019Parameter elimination in particle Gibbs samplingcited by 5
  6. 2023State and parameter learning with PaRIS particle Gibbscited by 4
  7. 2023Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbingcited by 2
  8. 2022Consistent and fast inference in compartmental models of epidemics using Poisson Approximate Likelihoodscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 6.

12 papers.

  1. 2025Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Designcited by 12
  2. 2024Bayesian Experimental Design via Contrastive Diffusionscited by 9
  3. 2024Recursive Nested Filtering for Efficient Amortized Bayesian Experimental Designcited by 3
  4. 2026Constrained Bayesian Experimental Design via Online Planningcited by 2
  5. 2026Efficient Adaptive Data Acquisition via Pretrained Belief Representationscited by 2
  6. 2025BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fieldscited by 1
  7. 2025Online Bayesian Experimental Design for Partially Observed Dynamical Systemscited by 1
  8. 2025Designing an Optimal Sensor Network via Minimizing Information Losscited by 0
  9. 2026VariBASed: Variational Bayes-Adaptive Sequential Monte-Carlo Planning for Deep Reinforcement Learningcited by 0
  10. 2026Maximin Robust Bayesian Experimental Designcited by 0
  11. 2026FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Datacited by 0
  12. 2026Bayesian Experimental Design via Score Matchingcited by 0