An Information Criterion for Inferring Coupling of Distributed Dynamical Systems

Frontiers in Robotics and AI

Oliver Michael Cliff¹  Robert Charles Fitch²  Mikhail Prokopenko³ 

[1] Australian Centre for Field Robotics, The University of Sydney;Centre for Autonomous Systems;Complex Systems Research Group;

关键词: Information Theory;    dynamical systems;    complex networks;    Dynamic Bayesian Networks;    structure learning;    State space reconstruction;   

DOI  :  10.3389/frobt.2016.00071

来源: DOAJ

【 摘 要 】

The behaviour of many real-world phenomena can be modelled by nonlinear dynamical systems whereby a latent system state is observed through a filter. We are interested in interacting subsystems of this form, which we model by a set of coupled maps as a synchronous update graph dynamical systems. Specifically, we study the structure learning problem for spatially distributed dynamical systems coupled via a directed acyclic graph. Unlike established structure learning procedures that find locally maximum posterior probabilities of a network structure containing latent variables, our work exploits the properties of dynamical systems to compute globally optimal approximations of these distributions. We arrive at this result by the use of time delay embedding theorems. Taking an information-theoretic perspective, we show that the log-likelihood has an intuitive interpretation in terms of information transfer.

【 授权许可】

Unknown