有向信息论与Granger因果图
On directed information theory and Granger causality graphs
摘要：
有向信息论研究具有反馈的通信渠道。当应用于网络时，需要基于因果条件的自然扩展。我们在这里表明，建立在网络中的有向信息论的措施，可以用来评估随机过程的格兰杰因果关系图。我们表明，有向信息论包括措施，如转移熵，它是足够的信息理论框架需要神经科学的应用，如连接推理问题。
英文摘要：
Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
