  In networks of dynamic systems, one challenge is to identify the
interconnection structure on the basis of measured signals. Inspired by a
Bayesian approach in [1], in this paper, we explore a Bayesian model selection
method for identifying the connectivity of networks of transfer functions,
without the need to estimate the dynamics. The algorithm employs a Bayesian
measure and a forward-backward search algorithm. To obtain the Bayesian
measure, the impulse responses of network modules are modeled as Gaussian
processes and the hyperparameters are estimated by marginal likelihood
maximization using the expectation-maximization algorithm. Numerical results
demonstrate the effectiveness of this method.
