Bayesian methods
Particle Filter (Sequential Monte Carlo)
The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive.
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Sources
- Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113. DOI: 10.1049/ip-f-2.1993.0015 ↗
- Doucet, A., Godsill, S. J., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197–208. DOI: 10.1023/A:1008935410038 ↗
- Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer-Verlag. DOI: 10.1007/978-1-4757-3437-9 ↗
Related methods
Referenced by
Approximate Bayesian Computation with Measurement ErrorApproximate Bayesian Computation with Missing DataDynamic Bayesian Hierarchical ModelDynamic Bayesian InferenceDynamic Bayesian NetworkDynamic Metropolis-Hastings AlgorithmDynamic Monte Carlo SimulationDynamic Particle FilterDynamic Sequential Monte CarloDynamic Variational InferenceEnsemble Kalman FilterHierarchical Kalman FilterHierarchical Particle FilterKalman FilterKalman Filter with Measurement ErrorMultilevel Monte Carlo SimulationParticle Filter with Missing DataRobust Approximate Bayesian ComputationRobust Kalman FilterRobust Particle FilterRobust Sequential Monte CarloSequential Monte CarloSequential Monte Carlo with Measurement ErrorSequential Monte Carlo with Missing DataSimultaneous Localization and MappingSpatial Kalman FilterTime series approximate Bayesian computationTime series Bayesian inferenceTime Series Kalman FilterTime series MCMCTime series particle filterTime series sequential Monte Carlo