mlsanityData to 7 Oct 2026

Variational Marginal Particle Filters

arXiv 2109.15134 ↗First posted 30 Sep 2021stat.ML cs.LGcited by 6

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Abstract (arXiv)

Variational inference for state space models (SSMs) is known to be hard in general. Recent works focus on deriving variational objectives for SSMs from unbiased sequential Monte Carlo estimators. We reveal that the marginal particle filter is obtained from sequential Monte Carlo by applying Rao-Blackwellization operations, which sacrifices the trajectory information for reduced variance and differentiability. We propose the variational marginal particle filter (VMPF), which is a differentiable and reparameterizable variational filtering objective for SSMs based on an unbiased estimator. We find that VMPF with biased gradients gives tighter bounds than previous objectives, and the unbiased reparameterization gradients are sometimes beneficial.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 36 of its 56 references that are in the map. First posted on arXiv 30 Sep 2021.

Builds on

36 of 56 references.

References · 36

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  4. 2015Variational Inference with Normalizing Flowscited by 2,161
  5. 2016Variational Inference: A Review for Statisticianscited by 1,466
  6. 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
  7. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
  8. 2015Importance Weighted Autoencoderscited by 454
  9. 2015A Recurrent Latent Variable Model for Sequential Datacited by 362
  10. 2016Structured Inference Networks for Nonlinear State Space Modelscited by 182
  11. 2017Variational Sequential Monte Carlocited by 93
  12. 2017Filtering Variational Objectivescited by 91
24 more
  1. 2015Hierarchical Variational Modelscited by 89
  2. 2018Implicit Reparameterization Gradientscited by 84
  3. 2018Tighter Variational Bounds are Not Necessarily Bettercited by 76
  4. 2017Auto-Encoding Sequential Monte Carlocited by 64
  5. 2018Particle Filter Networks with Application to Visual Localizationcited by 64
  6. 2018An Introduction to Probabilistic Programmingcited by 62
  7. 2019Elements of Sequential Monte Carlocited by 60
  8. 2021Differentiable Particle Filtering via Entropy-Regularized Optimal Transportcited by 59
  9. 2018Differentiable Particle Filters: End-to-End Learning with Algorithmic Priorscited by 58
  10. 2018Importance Weighting and Variational Inferencecited by 55
  11. 2018Hamiltonian Variational Auto-Encodercited by 41
  12. 2016Variational Boosting: Iteratively Refining Posterior Approximationscited by 36
  13. 2019Particle Filter Recurrent Neural Networkscited by 36
  14. 2015The Variational Gaussian Processcited by 29
  15. 2020Discriminative Particle Filter Reinforcement Learning for Complex Partial Observationscited by 27
  16. 2015Nested Sequential Monte Carlo Methodscited by 19
  17. 2020Towards Differentiable Resamplingcited by 19
  18. 2016Stochastic Backpropagation through Mixture Density Distributionscited by 14
  19. 2019Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximationcited by 14
  20. 2015Variational Gaussian Copula Inferencecited by 12
  21. 2018Tensor Monte Carlo: particle methods for the GPU eracited by 8
  22. 2021Learning Proposals for Probabilistic Programs with Inference Combinatorscited by 6
  23. 2019Copula-like Variational Inferencecited by 4
  24. 2019Particle Smoothing Variational Objectivescited by 4

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 1.

6 papers.

  1. 2023Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Frameworkcited by 6
  2. 2022Particle-Based Score Estimation for State Space Model Learning in Autonomous Drivingcited by 3
  3. 2024Differentiable and Stable Long-Range Tracking of Multiple Posterior Modescited by 2
  4. 2025Diffusion differentiable resamplingcited by 2
  5. 2023Massively Parallel Reweighted Wake-Sleepcited by 1
  6. 2024Nonparametric Filtering, Estimation and Classification using Neural Jump ODEscited by 1