A hierarchical Bayesian learning scheme for autoregressive neural networks: application to the CATS benchmark

In this paper, a hierarchical Bayesian learning scheme for autoregressive neural network models is shown, which overcomes the problem of identifying the separate linear and nonlinear parts modeled by the network. We show how the identification can be carried out by defining suitable priors on the parameter space, which help the learning algorithms to avoid undesired parameter configurations. An application to synthetic data is shown and we apply the method to the CATS times series prediction benchmark.