D. J. C. MacKay. Introduction to Gaussian processes. In C. M. Bishop, editor, Neural Networks and Machine Learning, volume 168 of NATO ASI Series, pages 133-165. Springer, Berlin, 1998.
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed syste
The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed
The normal inverse Gaussian process has been used to model both stock returns and interest rate processes. Although several numerical methods are available to compute, for instance, VaR and derivatives values, these are in a relatively undeveloped s
