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| 时间序列贝叶斯推断× | 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 | 1989 | 1989 |
| 提出者≠ | Mike West and Jeff Harrison | Thomas Dean & Keiji Kanazawa |
| 类型≠ | Bayesian probabilistic model | probabilistic graphical model for sequences |
| 开创性文献≠ | West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 | Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. DOI ↗ |
| 别名 | Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS | DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network |
| 相关≠ | 6 | 5 |
| 摘要≠ | Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks. | 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. |
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