scholarly journals Parsing with Lexicalized Probabilistic Recursive Transition Networks

2006 ◽  
pp. 156-166 ◽  
Author(s):  
Alexis Nasr ◽  
Owen Rambow
Keyword(s):  
Transition Networks

scholarly journals Interruptable transition networks

1984 ◽  
Author(s):  
Sergei Nirenburg
Keyword(s):  
Transition Networks

scholarly journals Contrasting chaotic with stochastic dynamics via ordinal transition networks

2020 ◽  
Vol 30 (6) ◽  
pp. 063101 ◽  
Author(s):  
F. Olivares ◽  
M. Zanin ◽  
L. Zunino ◽  
D. G. Pérez
Keyword(s):  
Stochastic Dynamics ◽  
Transition Networks

scholarly journals Constructing ordinal partition transition networks from multivariate time series

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Jiayang Zhang ◽  
Jie Zhou ◽  
Ming Tang ◽  
Heng Guo ◽  
Michael Small ◽  
...  
Keyword(s):  
Time Series ◽  
Multivariate Time Series ◽  
Transition Networks

scholarly journals Causal coupling inference from multivariate time series based on ordinal partition transition networks

2021 ◽  
Author(s):  
Narayan Puthanmadam Subramaniyam ◽  
Reik V. Donner ◽  
Davide Caron ◽  
Gabriella Panuccio ◽  
Jari Hyttinen
Keyword(s):  
Dynamical Systems ◽  
Observational Data ◽  
Real World ◽  
Microelectrode Array ◽  
Nonlinear Dynamical Systems ◽  
Causal Effect ◽  
Epileptiform Activity ◽  
Brain Slices ◽  
Real World Data ◽  
Transition Networks

AbstractIdentifying causal relationships is a challenging yet crucial problem in many fields of science like epidemiology, climatology, ecology, genomics, economics and neuroscience, to mention only a few. Recent studies have demonstrated that ordinal partition transition networks (OPTNs) allow inferring the coupling direction between two dynamical systems. In this work, we generalize this concept to the study of the interactions among multiple dynamical systems and we propose a new method to detect causality in multivariate observational data. By applying this method to numerical simulations of coupled linear stochastic processes as well as two examples of interacting nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays. Finally, we study real-world observational microelectrode array electrophysiology data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity. Our results, both from simulations and real-world data, suggest that OPTNs can provide a complementary and robust approach to infer causal effect networks from multivariate observational data.

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