An introduction to the use of probabilistic techniques in the design of expert systems, the book provides a background in probability and graph theory. It shows how to use the probability calculus in modelling reasoning under uncertainty, discusses subjective Bayesian analysis and the odds method and covers the theoretical foundations of causal networks, including Pearl's method of belief propagation and fusion in causal networks. Each chapter is split into two sections - the first section presents the theory and the second explains the technique's use and offers examples.
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This text is a reprint of the seminal 1989 book Probabilistic Reasoning in Expert systems: Theory and Algorithms, which helped serve to create the field we now call Bayesian networks. It introduces the properties of Bayesian networks (called causal networks in the text), discusses algorithms for doing inference in Bayesian networks, covers abductive inference, and provides an introduction to decision analysis. Furthermore, it compares rule-base experts systems to ones based on Bayesian networks, and it introduces the frequentist and Bayesian approaches to probability. Finally, it provides a critique of the maximum entropy formalism. Probabilistic Reasoning in Expert Systems was written from the perspective of a mathematician with the emphasis being on the development of theorems and algorithms. Every effort was made to make the material accessible. There are ample examples throughout the text. This text is important reading for anyone interested in both the fundamentals of Bayesian networks and in the history of how they came to be. It also provides an insightful comparison of the two most prominent approaches to probability.
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