Causality
Models, Reasoning and Inference. Ausgezeichnet: ACM Turing Award for Transforming Artificial Intelligence 2011
Kiadás sorszáma: 2
Kiadó: Cambridge University Press
Megjelenés dátuma: 2009. szeptember 14.
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Kiadói listaár:
GBP 54.99
GBP 54.99
Az Ön ára:
23 904 (22 766 Ft + 5% áfa )
Kedvezmény(ek): 10% (kb. 2 656 Ft)
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A termék adatai:
| ISBN13: | 9780521895606 |
| ISBN10: | 052189560X |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 484 oldal |
| Méret: | 260x185x30 mm |
| Súly: | 1070 g |
| Nyelv: | angol |
| Illusztrációk: | 124 b/w illus. 7 tables |
| 0 |
Témakör:
Rövid leírás:
Written by one of the preeminent researchers in the field, this provides a comprehensive exposition of modern analysis of causation.
Hosszú leírás:
Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social sciences. Judea Pearl presents and unifies the probabilistic, manipulative, counterfactual, and structural approaches to causation and devises simple mathematical tools for studying the relationships between causal connections and statistical associations. Cited in more than 2,100 scientific publications, it continues to liberate scientists from the traditional molds of statistical thinking. In this revised edition, Judea Pearl elucidates thorny issues, answers readers' questions, and offers a panoramic view of recent advances in this field of research. Causality will be of interest to students and professionals in a wide variety of fields. Dr Judea Pearl has received the 2011 Rumelhart Prize for his leading research in Artificial Intelligence (AI) and systems from The Cognitive Science Society.
'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
'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
Tartalomjegyzék:
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.
