3 papers
cs.IT2020
A Partial Information Decomposition Based on Causal Tensors
David Sigtermans
We propose a partial information decomposition based on the newly introduced framework of causal tensors, i.e., multilinear stochastic maps that transform source data into destinat…
cs.IT2019
Towards a Framework for Observational Causality From Time Series: When Shannon Meets Turing
David Sigtermans
We propose a novel tensor-based formalism for inferring causal structures from time series. An information theoretical analysis of transfer entropy, shows that transfer entropy res…
cs.IT2019
Transfer Entropy: where Shannon meets Turing
David Sigtermans
Transfer entropy is capable of capturing nonlinear source-destination relations between multi-variate time series. It is a measure of association between source data that are trans…