Vertaile menetelmiä

	Hierarkkinen partikkelisuodin	Partikkelisuodin (sekventiaalinen Monte Carlo)
Tieteenala	Bayesilainen tilastotiede	Bayesilainen tilastotiede
Menetelmäperhe	Bayesian methods	Bayesian methods
Syntyvuosi	2000s–2010s	1993
Kehittäjä	Briers, Doucet, and colleagues	Gordon, Salmond & Smith
Tyyppi	Sequential Monte Carlo / hierarchical state-space inference	Sequential Monte Carlo estimator
Alkuperäislähde	Briers, M., Doucet, A. & Maskell, S. (2010). Smoothing algorithms for state-space models. Annals of the Institute of Statistical Mathematics, 62(1), 61-89.	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.
Rinnakkaisnimet	nested particle filter, multilevel particle filter, hierarchical SMC, HPF	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Liittyvät	5	4
Tiivistelmä	A hierarchical particle filter extends Sequential Monte Carlo to state-space models with multiple levels of latent variables. Particles are propagated at each level of the hierarchy, allowing the method to track both fine-grained state dynamics and slower-varying hyperparameters simultaneously, yielding calibrated posterior distributions across all levels of the model.	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.