Neural Computation
An Introduction to Variational Methods for Graphical Models
Machine Learning
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks
Combining the Wavelet Transform and Forecasting Models to Predict Gas Forward Prices
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Robust Bayesian mixture modelling
Neurocomputing
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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.