Korb K.B., Nicholson A.E. Bayesian Artificial Intelligence

Boca Raton: Chapman & Hall/CRC Press, 2004. — 365 p. — (Computer science and data analysis). — ISBN: 1-58488-387-1.Bayesian Artificial Intelligence, in our understanding, is the incorporation of Bayesian inferentialmethods in the development of a software architecture for an Artificial Intelligence (AI). We believe that important ingredients of such an architecture will be Bayesian networks and the Bayesian learning of Bayesian networks (Bayesian causal discovery) from observation and experiment. In this book we present the elements of Bayesian network technology, automated causal discovery, learning probabilities from data, and examples and ideas about how to employ these technologies in developing probabilistic expert systems, which we call Knowledge Engineering with Bayesian Networks.
This is a very practical project, because data mining with Bayesian networks (applied causal discovery) and the deployment of Bayesian networks in industry and government are two of the most promising areas in applied AI today. But it is also a very theoretical project, because the achievement of a Bayesian AI would be a major theoretical achievement.
With our title there are a number of subjects we could naturally include, but have not. Thus, another necessary aspect of an effective Bayesian AI will be the learning of concepts, and hierarchies of concepts. Bayesian methods for concept formation exist (e.g., Chris Wallace’s Snob [290]), but we do not treat them here. We could also have discussed Bayesian methods of classification, polynomial curve fitting, time series modeling, etc. We have chosen to hew close to the theme of using and discovering Bayesian networks both because this is our own main research area and because, important as the other Bayesian learning methods are, we believe the Bayesian network technology is central to the overall project.
Our text differs from others available on Bayesian networks in a number of ways. We aim at a practical and accessible introduction to the main concepts in the technology, while paying attention to foundational issues. Most texts in this area require somewhat more mathematical sophistication than ours; we presuppose only a basic understanding of algebra and calculus. Also, we give roughly equal weight to the causal discovery of networks and to the Bayesian inference procedures using a network once found. Most texts either ignore causal discovery or treat it lightly. Richard Neapolitan’s recent book, Learning Bayesian Networks [200], is an exception, but it is more technically demanding than ours. Another distinguishing feature of our text is that we advocate a causal interpretation of Bayesian networks, and we discuss the use of Bayesian networks for causal modeling. We also illustrate various applications of the technology at length, drawing upon our own applied research. We hope that these illustrations will be of some interest and indicate some of the possibilities for the technology. Our text is aimed at advanced undergraduates in computer science who have some background in artificial intelligence and at those who wish to engage in applied or pure research in Bayesian network technology.Part I Probabilistic Reasoning
Bayesian Reasoning
Introducing Bayesian Networks
Inference in Bayesian Networks
Decision Networks
Applications of Bayesian Networks
Part II Learning Causal Models
Learning Linear Causal Models
Learning Probabilities
Learning Discrete Causal Structure
Part III Knowledge Engineering
Knowledge Engineering with Bayesian Networks
Evaluation
KEBN Case Studies
A Notation
B Software Packages