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On Particle Methods for Parameter Estimation in State-Space Models

arXiv 1412.8695 ↗First posted 30 Dec 2014stat.CO stat.MEcited by 47

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Abstract (arXiv)

Nonlinear non-Gaussian state-space models are ubiquitous in statistics, econometrics, information engineering and signal processing. Particle methods, also known as Sequential Monte Carlo (SMC) methods, provide reliable numerical approximations to the associated state inference problems. However, in most applications, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard particle methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive review of particle methods that have been proposed to perform static parameter estimation in state-space models. We discuss the advantages and limitations of these methods and illustrate their performance on simple models.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Dec 2014.

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Citing papers per year: 2021: 6; 2022: 9; 2023: 10; 2024: 6; 2025: 8; 2026: 8.

47 papers.

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  1. 2021Differentiable Particle Filtering via Entropy-Regularized Optimal Transportcited by 59
  2. 2023An overview of differentiable particle filters for data-adaptive sequential Bayesian inferencecited by 23
  3. 2021Online Variational Filtering and Parameter Learningcited by 19
  4. 2021Differentiable Particle Filters through Conditional Normalizing Flowcited by 15
  5. 2021Nested Variational Inferencecited by 14
  6. 2024Nesting Particle Filters for Experimental Design in Dynamical Systemscited by 12
  7. 2025Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolationcited by 11
  8. 2024Normalizing Flow-based Differentiable Particle Filterscited by 10
  9. 2022Conditional Measurement Density Estimation in Sequential Monte Carlo via Normalizing Flowcited by 6
  10. 2022Phi-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilationcited by 4
  11. 2023Sparse Graphical Linear Dynamical Systemscited by 4
  12. 2023On Feynman--Kac training of partial Bayesian neural networkscited by 4
35 more
  1. 2023Online Variational Sequential Monte Carlocited by 4
  2. 2025Learning to be Smooth: An End-to-End Differentiable Particle Smoothercited by 4
  3. 2022Online Probabilistic Model Identification using Adaptive Recursive MCMCcited by 3
  4. 2022Particle-Based Score Estimation for State Space Model Learning in Autonomous Drivingcited by 3
  5. 2023Real-Time Variational Method for Learning Neural Trajectory and its Dynamicscited by 3
  6. 2025Stochastic dynamics learning with state-space systemscited by 3
  7. 2022Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity Demandcited by 2
  8. 2023Differentiable Bootstrap Particle Filters for Regime-Switching Modelscited by 2
  9. 2023A State-Space Perspective on Modelling and Inference for Online Skill Ratingcited by 2
  10. 2023Learning Differentiable Particle Filter on the Flycited by 2
  11. 2023Risk-Sensitive Stochastic Optimal Control as Rao-Blackwellized Markovian Score Climbingcited by 2
  12. 2024Differentiable and Stable Long-Range Tracking of Multiple Posterior Modescited by 2
  13. 2024Revisiting semi-supervised training objectives for differentiable particle filterscited by 2
  14. 2025Sequential Monte Carlo for Policy Optimization in Continuous POMDPscited by 2
  15. 2025PyDPF: A Python Package for Differentiable Particle Filteringcited by 2
  16. 2022DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical modelscited by 1
  17. 2023Inferring Inferencecited by 1
  18. 2025Canonical Bayesian Linear System Identificationcited by 1
  19. 2025Active Measuring in Reinforcement Learning With Delayed Negative Effectscited by 1
  20. 2026Pathwise Learning of Stochastic Dynamical Systems with Partial Observationscited by 1
  21. 2026Efficient Stochastic Optimisation via Sequential Monte Carlocited by 1
  22. 2021Unsupervised Neural Hidden Markov Models with a Continuous latent state spacecited by 0
  23. 2021Warped Dynamic Linear Models for Time Series of Countscited by 0
  24. 2022Inference of Regulatory Networks Through Temporally Sparse Datacited by 0
  25. 2022Can a latent Hawkes process be used for epidemiological modelling?cited by 0
  26. 2022Expressivity of Hidden Markov Chains vs. Recurrent Neural Networks from a system theoretic viewpointcited by 0
  27. 2024Online Identification of Stochastic Continuous-Time Wiener Models Using Sampled Datacited by 0
  28. 2024Spacecraft inertial parameters estimation using time series clustering and reinforcement learningcited by 0
  29. 2025A State-Space Approach to Nonstationary Discriminant Analysiscited by 0
  30. 2026Scalable Model-Based Clustering with Sequential Monte Carlocited by 0
  31. 2026Online Generalised Predictive Codingcited by 0
  32. 2026Constrained latent state modeling: A unifying perspective on representation learning under competing constraintscited by 0
  33. 2026Efficient Learning of Deep State Space Models via Importance Smoothingcited by 0
  34. 2026Truncated Neural Likelihood Estimation for Simulation-Based Inference in State-Space Modelscited by 0
  35. 2026Structured Noise Adaptation for Sequential Bayesian Filtering with Embedded Latent Transfer Operatorscited by 0