So sánh phương pháp

	Suy luận Bayes động	Monte Carlo Tuần tự
Lĩnh vực	Bayes	Bayes
Họ	Bayesian methods	Bayesian methods
Năm ra đời	1989–1997	1993 (particle filter); 2006 (SMC samplers)
Người khởi xướng	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Loại	Bayesian sequential / online inference framework	Sequential Bayesian computation
Công trình gốc	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.
Tên gọi khác	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating	SMC, particle filter, sequential importance resampling, SMC sampler
Liên quan	6	6
Tóm tắt	Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.	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.