Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of thes

This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and M

In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of di