Salīdzināt metodes

	Hierarhiskais daļiņu filtrs	Particle Filter (Sequential Monte Carlo)
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	2000s–2010s	1993
Autors	Briers, Doucet, and colleagues	Gordon, Salmond & Smith
Tips	Sequential Monte Carlo / hierarchical state-space inference	Sequential Monte Carlo estimator
Pirmavots	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.
Citi nosaukumi	nested particle filter, multilevel particle filter, hierarchical SMC, HPF	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Saistītās	5	4
Kopsavilkums	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.