- Inbunden (Hardback)
- Antal sidor
- ACM Turing Award for Transforming Artificial Intelligence
- Cambridge University Press
- Judea, Pearl
- 124 b, w illus 7 tables
- 66 line figures 58 halftones 7 tables
- 279 x 190 x 38 mm
- Antal komponenter
- 1043 g
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Models, Reasoning and Inference
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Fler böcker av Judea Pearl
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'Make no mistake about it: this is an important book ... The field has no shortage of lively controversy and divergent opinion, but be that as it may, this is certainly one of the contributions that will bring this material further out of the closet and into the face of the broader statistical community, a move that we should welcome both as consumers and as testers of its utility.' Journal of the American Statistical Association
'Pearl's career has been motivated by problems of artificial intelligence, but the implications of this book are much broader. The distinctions he raises and the mathematical foundation he assembles are critical for every field of scientific endeavor. This updated edition of a modern classic deserves a broad and attentive audience.' H. Van Dyke Parunak, reviews.com
Bloggat om Causality
Judea Pearl is professor of computer science and statistics at the University of California, Los Angeles, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human reasoning, and philosophy of science. The author of Heuristics and Probabilistic Reasoning, he is a member of the National Academy of Engineering and a Founding Fellow of the American Association for Artificial Intelligence. Dr Pearl is the recipient of the IJCAI Research Excellence Award for 1999, the London School of Economics Lakatos Award for 2001, and the ACM Alan Newell Award for 2004. In 2008, he received the Franklin Medal for computer and cognitive science from the Franklin Institute.
1. Introduction to probabilities, graphs, and causal models; 2. A theory of inferred causation; 3. Causal diagrams and the identification of causal effects; 4. Actions, plans, and direct effects; 5. Causality and structural models in social science and economics; 6. Simpson's paradox, confounding, and collapsibility; 7. The logic of structure-based counterfactuals; 8. Imperfect experiments: bounding effects and counterfactuals; 9. Probability of causation: interpretation and identification; 10. The actual cause.