Bayesian networks and decision graphs are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. Their strengths are two-sided. It is easy for humans to construct and understand them, and when communicated to a computer, they can easily be compiled. The book emphasizes both the human and the computer side. It gives a thorough introduction to Bayesian networks, decision trees and influence diagrams as well as algorithms and complexity issues.
Not bad. Writing style can be somewhat terse. Sometimes he meanders off into to many examples without really explaining things first. The first couple chapters however are some of the better ones I've read as far as an introduction to Bayesian thinking and models.