Learning in Graphical Models (NATO Science Series D: (closed)) Review

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(More customer reviews)The title of the book is somewhat misleading, in that most of the research papers involve advanced issues concerning one particular graphical model, namely the Bayesian network. For this reason I highly recommend, as a prerequisite to this book, Finn Jensen's "Bayesian Networks and Decision Graphs". Jensen's book is adequate in giving a good introduction and overview of the subject, but not sufficient for calling oneself an "expert" upon successfully digesting it.
To its credit, "Learning in Graphical Models" has several well-written and interesting papers, but the tutorial papers just did not seem enough of an introduction for me to feel comfortable using it as a first source of introduction.
What I find most compelling about Bayesian networks is the fact that they seem both highly modular (which facilitates reusability and network interconnectivity) and can be designed in a semi-rational manner (contrast this with neural-network architectures for which few good algorithms exist for determining size and number of layers). For this reason I imagine they will be important players in future engineering projects that require learning and adaptation.

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In the past decade, a number of different research communitieswithin the computational sciences have studied learning in networks,starting from a number of different points of view. There has beensubstantial progress in these different communities and surprisingconvergence has developed between the formalisms. The awareness ofthis convergence and the growing interest of researchers inunderstanding the essential unity of the subject underlies the currentvolume. Two research communities which have used graphical or networkformalisms to particular advantage are the belief networkcommunity and the neural network community. Belief networksarose within computer science and statistics and were developed withan emphasis on prior knowledge and exact probabilistic calculations.Neural networks arose within electrical engineering, physics andneuroscience and have emphasised pattern recognition and systemsmodelling problems. This volume draws together researchers from thesetwo communities and presents both kinds of networks as instances of ageneral unified graphical formalism. The book focuses on probabilisticmethods for learning and inference in graphical models, algorithmanalysis and design, theory and applications. Exact methods, samplingmethods and variational methods are discussed in detail. Audience: A wide cross-section of computationally orientedresearchers, including computer scientists, statisticians, electricalengineers, physicists and neuroscientists.

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