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 that is rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. The book not only marks an effective direction of investigation with significant experimental advances, but it is also, and perhaps primarily, a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, full of insights.