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We develop low-complexity algorithms to robustly identify the best directed tree approximation for a network of stochastic processes in the finite-sample regime. Directed information is used to quantify influence between stochastic processes and identify the best directed tree approximation in terms of Kullback-Leibler (KL) divergence. We provide finite-sample complexity bounds for confidence intervals...
In his 1969 paper, Granger proposed a statistical definition of causality between stochastic processes. It is based on whether causal side information helps in a sequential prediction task. However, his formulation was limited to linear predictors. We describe a generalized framework, where predictions are beliefs and compare the best predictor with side information to the best predictor without side...
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