Bayesian methodsBayesian / computational
动态贝叶斯网络
动态贝叶斯网络（DBN）通过表示一组随机变量在离散时间步长上的演变，扩展了标准贝叶斯网络在时间上的应用。它同时捕捉了每个瞬时变量之间的条件独立结构以及连续时间切片之间的概率依赖性，从而能够在不确定性下对时间过程进行原理性的推理。
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来源
- Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. DOI: 10.1111/j.1467-8640.1989.tb00324.x ↗
- Murphy, K. P. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, University of California, Berkeley. link ↗
如何引用本页
ScholarGate. (2026, June 3). Dynamic Bayesian Network. ScholarGate. https://scholargate.app/zh/bayesian/dynamic-bayesian-network
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