Σύγκριση μεθόδων

	Προσομοίωση Ιεραρχικής Επαναδειγματοληψίας Bootstrap	Sequential Monte Carlo
Πεδίο	Μπεϋζιανή Στατιστική	Μπεϋζιανή Στατιστική
Οικογένεια	Bayesian methods	Bayesian methods
Έτος προέλευσης	1997-2008	1993 (particle filter); 2006 (SMC samplers)
Δημιουργός	Davison & Hinkley; Cameron, Gelbach & Miller	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Τύπος	resampling simulation	Sequential Bayesian computation
Θεμελιώδης πηγή	Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press. ISBN: 978-0521574716	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.
Εναλλακτικές ονομασίες	cluster bootstrap, multilevel bootstrap, nested bootstrap resampling, hierarchical resampling	SMC, particle filter, sequential importance resampling, SMC sampler
Συναφείς	5	6
Σύνοψη	Hierarchical bootstrap simulation is a resampling technique designed for data with nested or clustered structure — students within schools, patients within hospitals, repeated measures within subjects. It preserves the natural grouping of the data by resampling at each level of the hierarchy in sequence, producing a sampling distribution that correctly reflects both between-group and within-group variability.	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.