A Transfer Entropy Method to Quantify Causality in Stochastic Nonlinear Systems*

In modern chemical processes, identification of the process variable connectivity and their topology is vital for maintaining the operational safety. As a general information theoretic method, transfer entropy can analyze the causality between two variables based on estimation of conditional probability density functions. Transfer entropy estimation is typically a data driven method, however, the associated high computational complexity and poor accuracy are not acceptable in real applications. Using a nonlinear stochastic state-space model in conjunction with particle filters, a novel transfer entropy estimation method is proposed. The proposed approach requires less data, is fast and accurate.