With Bayesian network technology very much on the up-swing in industry and government, there is an increasing need for an introductory book that jointly emphasizes the understanding of its underlying priniciples and their application in practice. Bayesian Artificial Intelligence presents elements of Bayesian network technology, automated causal discovery, and learning probabilities from data along with extensive motivational examples of using these technologies to develop probabilistic expert systems. This practical, very accessible introduction balances the causal discovery of networks with the Bayesian inference procedures that use a network once it is found. The authors emphasize understanding and intuition, so they keep the mathematical details to a minimum, but also provide the algorithms and technical background needed for applications. They illustrate at length a number of applications and discuss application software in detail. A broad range of topics, a practical perspective, and a thoughtful discussion of philosophical underpinnings make Bayesian Artificial Intelligence an ideal introduction for students and for professionals who want to broaden their expertise. It provides the knowledge you need to put Bayesian network tools into practice, while also forming the basis for a more detailed investigation of the technology and original research.

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