Сравнение методов

	Последовательный Монте-Карло	Фильтр частиц (последовательное Монте-Карло)
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1993 (particle filter); 2006 (SMC samplers)	1993
Автор метода	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)	Gordon, Salmond & Smith
Тип	Sequential Bayesian computation	Sequential Monte Carlo estimator
Основополагающий источник	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.	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	SMC, particle filter, sequential importance resampling, SMC sampler	SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm
Связанные	6	4
Сводка	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.	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.