The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the nonconvex optimization problem and the choice of the number of hidden units. In support vector machines (SVMs) for classification, as introduced by Vapnik, a nonlinear decision boundary is obtained ...

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We also discuss its Bayesian interpretation and justiication. We rst review previous results for the approximation of a function from discrete data (Girosi, 1998) in the co...

Motivated by the need of estimating the main spawning period of North Sea cod, we develop a common transfer function model with a panel of contemporaneously correlated times series data. This model incorporates (i) the smoothness on the parameters by assuming that the second differences are small and (ii) the contemporaneous correlation by assuming that the errors have a general variance-covari...

Motivated by the growing importance of social media, this paper examines the relationship between new media, old media, and sales, in the context of the music industry. In particular, we study the interplay between blog buzz, radio play and music sales, at both the album and song levels of analysis. We employ the Panel Vector Auto-regression (PVAR) methodology, an extension of vector auto-regre...