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Figure 6 | BMC Bioinformatics

Figure 6

From: Granger causality vs. dynamic Bayesian network inference: a comparative study

Figure 6

Granger causality and Bayesian network inference applied on a stochastic coefficients non-linear model. The parameters in polynomial equation are randomly generated in the interval [-2,2]. (A) We applied both approaches on different sample size (from 300 to 900). For each sample size, we generated 100 different coefficient vectors, so the total number of directed interactions for each sample size is 500. (a) The percentage of detected true positive causalities for both approaches. (b) Time cost for both approaches. (B) For sample size 900, the derived causality (1 represents positive causality and 0 represents negative) is plotted with the absolute value of corresponding coefficients. For visualization purpose, the figure for Granger causality is shifted upward.

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