Сравнение методов

	Последовательный Монте-Карло с ошибкой измерения	Последовательный Монте-Карло
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1993–2001	1993 (particle filter); 2006 (SMC samplers)
Автор метода	Gordon, Salmond & Smith (1993); extended by Doucet, de Freitas & Gordon (2001)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Тип	Sequential Bayesian filtering	Sequential Bayesian computation
Основополагающий источник	Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer New York. ISBN: 978-0-387-95146-1	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 with measurement error, particle filter with noisy observations, SMC state-space measurement error, sequential particle filtering with observation noise	SMC, particle filter, sequential importance resampling, SMC sampler
Связанные	6	6
Сводка	Sequential Monte Carlo (SMC) with measurement error is a particle-based Bayesian filtering method for tracking hidden states in dynamical systems when observations are corrupted by noise. It propagates a weighted cloud of particles through time, updating weights at each step to reflect how well each particle explains the noisy measurement, and produces a full posterior distribution over the latent state at every time point.	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.