Variational inference for Student-t MLP models

This paper presents a novel methodology to infer parameters of probabilistic models whose output noise is a Student-t distribution. The method is an extension of earlier work for models that are linear in parameters to non-linear multi-layer perceptrons (MLPs). We used an EM algorithm combined with variational approximation, an evidence procedure, and an optimisation algorithm. The technique was tested on two forecasting applications. The first one is a synthetic dataset and the second is gas forward contract prices data from the UK energy market. The results showed that forecasting accuracy is significantly improved by using Student-t noise models.