Hybrid Random Fields: A Scalable Approach to Structure and Parameter Learning in Probabilistic Graphical Models

This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book—-rigorous in the treatment of the mathematical background, but also enlivened by interesting and original hist...