Causality and Network Graph in General Bilinear State-Space Representations

Monika Jozsa*, Mihaly Petreczky, M. Kanat Camlibel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This article proposes an extension of the well-known concept of Granger causality, called GB-Granger causality. GB-Granger causality is designed to relate the internal structure of bilinear state-space systems and statistical properties of their output processes. That is, if such a system generates two processes, where one does not GB-Granger cause the other, then it can be interpreted as the interconnection of two subsystems: one that sends information to the other, and one which does not send information back.This result is an extension of earlier obtained results on the relationship between Granger causality and the internal structure of linear time-invariant state-space representations.

Original languageEnglish
Article number8894094
Pages (from-to)3623-3630
Number of pages8
JournalIEEE Transactions on Automatic Control
Volume65
Issue number8
DOIs
Publication statusPublished - Aug-2020

Keywords

  • Stochastic processes
  • Random variables
  • Algebra
  • Nonlinear systems
  • Linear systems
  • Biological system modeling
  • Interconnected systems
  • stochastic systems
  • system realization
  • BAYES NETS
  • REALIZATIONS
  • FEEDBACK

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