So sánh phương pháp

	Dynamic Sequential Monte Carlo	Monte Carlo Tuần tự
Lĩnh vực	Bayes	Bayes
Họ	Bayesian methods	Bayesian methods
Năm ra đời	2006	1993 (particle filter); 2006 (SMC samplers)
Người khởi xướng	Del Moral, Doucet, Jasra	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Loại	Sequential Monte Carlo sampler for dynamic settings	Sequential Bayesian computation
Công trình gốc	Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436.	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	Dynamic SMC, SMC for dynamic models, sequential particle filter, dynamic particle sampler	SMC, particle filter, sequential importance resampling, SMC sampler
Liên quan	6	6
Tóm tắt	Dynamic Sequential Monte Carlo (Dynamic SMC) is a Bayesian computational method that maintains and updates a population of weighted samples — particles — as new observations arrive over time. It propagates particles through a dynamic system model, reweights them by how well they match the observed data, and periodically resamples to concentrate effort on high-probability regions, yielding online posterior inference for state-space and time-evolving models.	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.