Indirect Causes in Dynamic Bayesian Networks Revisited
Indirect Causes in Dynamic Bayesian Networks Revisited
复制标题
DOI：
10.1613/jair.5361
复制
发表时间：
2015-07
期刊：
影响因子：
--
通讯作者：
A. Motzek;R. Möller
中科院分区：
文献类型：
--
作者：
A. Motzek;R. Möller
对因果依赖关系进行建模通常需要粗粒度时间尺度上的循环。如果要使用贝叶斯网络对不确定性进行建模，则使用动态贝叶斯网络消除循环，随着时间的推移传播间接依赖关系并强制执行无限小的时间分辨率。如果没有“因果设计”，即，如果不及时适当地预测间接影响，我们认为这样的网络返回虚假的结果。通过引入激活器随机变量，我们提出了一个因果使用时间的动态贝叶斯网络建模的模板片段，预测在坚实的数学基础上的间接影响，遵守贝叶斯网络的法律。
Modeling causal dependencies often demands cycles at a coarse-grained temporal scale. If Bayesian networks are to be used for modeling uncertainties, cycles are eliminated with dynamic Bayesian networks, spreading indirect dependencies over time and enforcing an infinitesimal resolution of time. Without a "causal design," i.e., without anticipating indirect influences appropriately in time, we argue that such networks return spurious results. By introducing activator random variables, we propose template fragments for modeling dynamic Bayesian networks under a causal use of time, anticipating indirect influences on a solid mathematical basis, obeying the laws of Bayesian networks.