So sánh phương pháp

	Monte Carlo tuần tự với sai số đo lường	Monte Carlo Tuần tự
Lĩnh vực	Bayes	Bayes
Họ	Bayesian methods	Bayesian methods
Năm ra đời	1993–2001	1993 (particle filter); 2006 (SMC samplers)
Người khởi xướng	Gordon, Salmond & Smith (1993); extended by Doucet, de Freitas & Gordon (2001)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Loại	Sequential Bayesian filtering	Sequential Bayesian computation
Công trình gốc	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.
Tên gọi khác	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
Liên quan	6	6
Tóm tắt	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.