Granger Causality of Gaussian Signals from Quantized Measurements
S Ahmadi, GN Nair, E Weyer
Proceedings of the 2019 IEEE 58th Conference on Decision and Control (CDC) | IEEE | Published : 2020
An approach is proposed for inferring Granger causality between jointly stationary, Gaussian signals from quantized data. First, a necessary and sufficient rank criterion for the equality of two conditional Gaussian distributions is proved. Assuming a partial finite-order Markov property, sufficient conditions are then derived under which Granger causality between them can be reliably inferred from the second order moments of the quantized processes. This approach does not require the statistics of the underlying Gaussian signals to be estimated, or a system model to be identified.
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Awarded by Australian Research Council
This work was partially supported by the Australian Research Council via Future Fellowship grant FT140100527.