เปรียบเทียบวิธี

	ตัวกรองอนุภาคแบบลำดับชั้น (Hierarchical Particle Filter)	Particle Filter (Sequential Monte Carlo)
สาขาวิชา	เบย์	เบย์
ตระกูล	Bayesian methods	Bayesian methods
ปีกำเนิด	2000s–2010s	1993
ผู้ริเริ่ม	Briers, Doucet, and colleagues	Gordon, Salmond & Smith
ประเภท	Sequential Monte Carlo / hierarchical state-space inference	Sequential Monte Carlo estimator
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	nested particle filter, multilevel particle filter, hierarchical SMC, HPF	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
ที่เกี่ยวข้อง	5	4
สรุป	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.