Сравнение методов

	Динамическая байесовская иерархическая модель	Последовательный Монте-Карло
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1990s	1993 (particle filter); 2006 (SMC samplers)
Автор метода	West, Harrison, and colleagues	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Тип	Bayesian hierarchical state-space model	Sequential Bayesian computation
Основополагающий источник	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259	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.
Другие названия	DBHM, dynamic hierarchical Bayes, Bayesian dynamic multilevel model, state-space hierarchical Bayesian model	SMC, particle filter, sequential importance resampling, SMC sampler
Связанные	4	6
Сводка	A Dynamic Bayesian Hierarchical Model combines the multilevel structure of Bayesian hierarchical models with an explicit time-evolution equation for the latent states. Observations at each time point are linked to unobserved dynamic states, which evolve according to a probabilistic transition law, while a shared hyperprior pools information across units or levels, enabling coherent inference over time and across groups simultaneously.	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.