Abstract
This introductory chapter starts by describing the effects of uncertainty in intelligent systems and presents a brief history of the development of uncertain reasoning in artificial intelligence. Then it presents the basic approach for probabilistic reasoning, motivating the development of probabilistic graphical models. It gives an overview of probabilistic graphical models, the types of models, and how these can be classified. It concludes with a description of the rest of the book.
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Notes
- 1.
This and the following sections assume that the reader is familiar with some basic concepts of probability theory; a review of these and other concepts is given in Chap. 2.
- 2.
Random variables are formally defined later on.
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Sucar, L.E. (2015). Introduction. In: Probabilistic Graphical Models. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6699-3_1
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DOI: https://doi.org/10.1007/978-1-4471-6699-3_1
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