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| 动态贝叶斯网络× | 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 | 1988 |
| 提出者≠ | Thomas Dean & Keiji Kanazawa | Judea Pearl |
| 类型≠ | probabilistic graphical model for sequences | Probabilistic graphical model |
| 开创性文献≠ | Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. DOI ↗ | Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797 |
| 别名≠ | DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network | Bayes network, belief network, probabilistic graphical model, directed graphical model |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. | A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others. |
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