เปรียบเทียบวิธี

	ตัวกรองอนุภาคแบบลำดับชั้น (Hierarchical Particle Filter)	Kalman Filter
สาขาวิชา	เบย์	เบย์
ตระกูล	Bayesian methods	Bayesian methods
ปีกำเนิด	2000s–2010s	1960
ผู้ริเริ่ม	Briers, Doucet, and colleagues	Rudolf E. Kalman
ประเภท	Sequential Monte Carlo / hierarchical state-space inference	recursive Bayesian filter
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.	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
ชื่อเรียกอื่น	nested particle filter, multilevel particle filter, hierarchical SMC, HPF	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
ที่เกี่ยวข้อง	5	5
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 Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.