Bayesian learning for recurrent neural networks
We extend the Bayesian learning framework to the modelling of multivariate time series with recurrent neural networks. The proposed extensions concern both regression and classification problems. We briefly discuss the choice of appropriate priors for building such sequential models and present a general method for translating prior knowledge into prior distributions.
bayesian learning priors recurrent networks sequence processing