mlsanityData to 7 Oct 2026

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

arXiv 1301.3853 ↗First posted 16 Jan 2013cs.LG cs.AI stat.COcited by 46

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Abstract (arXiv)

Particle filters (PFs) are powerful sampling-based inference/learning algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a principled way, any type of probability distribution, nonlinearity and non-stationarity. They have appeared in several fields under such names as "condensation", "sequential Monte Carlo" and "survival of the fittest". In this paper, we show how we can exploit the structure of the DBN to increase the efficiency of particle filtering, using a technique known as Rao-Blackwellisation. Essentially, this samples some of the variables, and marginalizes out the rest exactly, using the Kalman filter, HMM filter, junction tree algorithm, or any other finite dimensional optimal filter. We show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate estimates than standard PFs. We demonstrate RBPFs on two problems, namely non-stationary online regression with radial basis function networks and robot localization and map building. We also discuss other potential application areas and provide references to some finite dimensional optimal filters.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jan 2013.

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Citing papers per year: 2021: 5; 2022: 7; 2023: 12; 2024: 9; 2025: 8; 2026: 5.

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  1. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  2. 2024Deep Learning-Based Object Pose Estimation: A Comprehensive Surveycited by 39
  3. 2024Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planningcited by 28
  4. 2023An overview of differentiable particle filters for data-adaptive sequential Bayesian inferencecited by 23
  5. 2021Deep Explicit Duration Switching Models for Time Seriescited by 17
  6. 2023Neural Relational Inference with Fast Modular Meta-learningcited by 16
  7. 2022A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learningcited by 12
  8. 2024Normalizing Flow-based Differentiable Particle Filterscited by 10
  9. 2021Partition Function Estimation: A Quantitative Studycited by 8
  10. 2025Inverse Problem Sampling in Latent Space Using Sequential Monte Carlocited by 7
  11. 2021Language Understanding for Field and Service Robots in a Priori Unknown Environmentscited by 6
  12. 2022Ambiguity-Aware Multi-Object Pose Optimization for Visually-Assisted Robot Manipulationcited by 6
34 more
  1. 2023Sequential Estimation of Gaussian Process-based Deep State-Space Modelscited by 5
  2. 2024A unifying framework for generalised Bayesian online learning in non-stationary environmentscited by 5
  3. 2023Automatically Marginalized MCMC in Probabilistic Programmingcited by 4
  4. 2023Switching Autoregressive Low-rank Tensor Modelscited by 3
  5. 2022Active Exploration based on Information Gain by Particle Filter for Efficient Spatial Concept Formationcited by 2
  6. 2023Differentiable Bootstrap Particle Filters for Regime-Switching Modelscited by 2
  7. 2023For A More Comprehensive Evaluation of 6DoF Object Pose Trackingcited by 2
  8. 2025Deep Variational Sequential Monte Carlo for High-Dimensional Observationscited by 2
  9. 2025DiffPF: Differentiable Particle Filtering with Generative Sampling via Conditional Diffusion Modelscited by 2
  10. 2026Teacher Forcing as Generalized Bayes: Optimization Geometry Mismatch in Switching Surrogates for Chaotic Dynamicscited by 2
  11. 2022Hierarchical Path-planning from Speech Instructions with Spatial Concept-based Topometric Semantic Mappingcited by 1
  12. 2022Indoor SLAM Using a Foot-mounted IMU and the local Magnetic Fieldcited by 1
  13. 2023Multiparticle Kalman filter for object localization in symmetric environmentscited by 1
  14. 2023Incorporating sufficient physical information into artificial neural networks: a guaranteed improvement via physics-based Rao-Blackwellizationcited by 1
  15. 2024A Family of Distributions of Random Subsets for Controlling Positive and Negative Dependencecited by 1
  16. 2024Rao-Blackwellized POMDP Planningcited by 1
  17. 2024Hamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Modelscited by 1
  18. 2025Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Modelcited by 1
  19. 2026A Predictive View on Streaming Hidden Markov Modelscited by 1
  20. 2026The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modelingcited by 1
  21. 2021DNN-assisted Particle-based Bayesian Joint Synchronization and Localizationcited by 0
  22. 2022Continuous-time Particle Filtering for Latent Stochastic Differential Equationscited by 0
  23. 2022DynaConF: Dynamic Forecasting of Non-Stationary Time Seriescited by 0
  24. 2023Tracking People in Highly Dynamic Industrial Environmentscited by 0
  25. 2023Auxiliary MCMC and particle Gibbs samplers for parallelisable inference in latent dynamical systemscited by 0
  26. 2023Probabilistic Modeling of Human Teams to Infer False Beliefscited by 0
  27. 2024Inverse Particle Filtercited by 0
  28. 2024Inference Plans for Hybrid Particle Filteringcited by 0
  29. 2025Belief Roadmaps with Uncertain Landmark Evanescencecited by 0
  30. 2025Adversarial Transform Particle Filterscited by 0
  31. 2025Neural Bayesian Filteringcited by 0
  32. 2025Goal inference with Rao-Blackwellized Particle Filterscited by 0
  33. 2026The Theorems of Dr. David Blackwell and Their Contributions to Artificial Intelligencecited by 0
  34. 2026Scalable Rao-Blackwellized Online Planning for High-Dimensional POMDPscited by 0