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Fourier Bayesian Information Criterion for network structure and causality estimation

Lookup NU author(s): Dr Luis Peraza RodriguezORCiD


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We propose a variant of the Bayesian Information Criterion (BIC) for network structure learning that we have called Fourier BIC (FBIC). The new measure is based on spectral techniques and can be applied in a similar way to previous network fitting measures such as Akaike’s, Minimum description length or BIC. FBIC presents the advantage of causality estimation, which is of paramount importance in dynamic networks and complex systems analysis. We test the performance of FBIC by estimating the structure of a causal Gaussian network using the K2 algorithm.

Publication metadata

Author(s): Peraza LR, Halliday DM

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: IEEE International Conference on Signals and Electronic Systems (ICSES)

Year of Conference: 2010

Pages: 33-36

ISSN: 9781424453078

Publisher: IEEE