方法对比

	时间序列序列蒙特卡洛方法	动态贝叶斯网络
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	1993	1989
提出者	Gordon, Salmond & Smith	Thomas Dean & Keiji Kanazawa
类型	Sequential Bayesian filtering algorithm	probabilistic graphical model for sequences
开创性文献	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.	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.
别名	particle filter, time series SMC, sequential particle filtering, bootstrap particle filter	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network
相关	5	5
摘要	Time series sequential Monte Carlo (SMC), commonly called the particle filter, is a Bayesian simulation method that tracks the hidden state of a dynamical system as observations arrive one at a time. A cloud of weighted random samples — particles — is propagated forward through the system dynamics, reweighted by how well each particle explains the new observation, and periodically resampled to keep the representation concentrated on plausible states.	A Dynamic Bayesian Network (DBN) extends a standard Bayesian network over time by representing how a set of random variables evolve across discrete time steps. It captures both the conditional independence structure among variables at each instant and the probabilistic dependencies between consecutive time slices, enabling principled reasoning about temporal processes under uncertainty.