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Time series particle filter

The time series particle filter is a Sequential Monte Carlo method that tracks the hidden state of a nonlinear, non-Gaussian state-space model as new observations arrive one at a time. It represents the evolving posterior distribution over the latent state as a weighted cloud of random samples (particles), updating them at each time step through propagation, likelihood weighting, and resampling.

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Time Series Particle Filter (Sequential Monte Carlo for State-Space Models)

Taksonomisk metoderegister · bayesian / bayesian

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., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer. · ISBN 978-0387951461

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