Directed Information Graphs: A generalization of Linear Dynamical Graphs
Directed Information Graphs: A generalization of Linear Dynamical Graphs
复制标题
有向信息图：线性动态图的推广
DOI：
10.1109/acc.2014.6859362
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
N. Kiyavash
中科院分区：
文献类型：
--
作者：
Jalal Etesami;N. Kiyavash
我们研究有向信息图（DIG）和线性动态图（LDG）之间的关系，这两者都是图模型，其中节点代表标量随机过程。 DIG 基于定向信息，表示随机系统中进程之间的因果动态。 LDG 捕获因果动态，但仅限于线性动力系统，并且在 LDG 的子集中有维纳过滤来实现此目的。这项研究表明，DIG 是 LDG 的广义版本，任何严格因果的 LDG 都可以通过学习相应的 DIG 来重建。
We study the relationship between Directed Information Graphs (DIG) and Linear Dynamical Graphs (LDG), both of which are graphical models where nodes represent scalar random processes. DIGs are based on directed information and represent the causal dynamics between processes in a stochastic system. LDGs capture causal dynamics but only in linear dynamical systems and there are Wiener filtering to do so in a subset of LDGs. This study shows that the DIGs are generalized version of the LDGs and any strictly causal LDGs can be reconstructed through learning the corresponding DIGs.
DOI：
10.1073/pnas.95.25.14863
发表时间：
1998-12-08
影响因子：
11.1
作者：
Eisen, MB;Spellman, PT;Botstein, D
通讯作者：
Botstein, D