Causality.
Subjects, Judea Pearl
Judea Pearl Causality. pdf download - Le téléchargement de ce bel Causality. livre et le lire plus tard. Êtes-vous curieux, qui a écrit ce grand livre? Oui, Judea Pearl est l'auteur pour Causality.. Ce livre se composent de plusieurs pages 484. Cambridge University Press est la société qui libère Causality. au public. 2009-09-14 est la date de lancement pour la première fois. Lire l'Causality. maintenant, il est le sujet plus intéressant. Toutefois, si vous ne disposez pas de beaucoup de temps à lire, vous pouvez télécharger Causality. à votre appareil et vérifier plus tard.. Si vous avez décidé de trouver ou lire ce livre, ci-dessous sont des informations sur le détail de Causality. pour votre référence.
de Judea Pearl
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Nom de fichier : causality.pdf
La taille du fichier : 21.28 MB
Book by Pearl JudeaRang parmi les ventes Amazon: #98339 dans LivresMarque: Brand: Cambridge University PressPublié le: 2009-09-14Langue d'origine: AnglaisNombre d'articles: 1Dimensions: 9.96" h x 1.18" l x 8.46" L, 2.26 livres Reliure: Relié484 pagesRevue de presse'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.comPrésentation de l'éditeurWritten 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.Biographie de l'auteurJudea 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.
Si vous avez un intérêt pour Causality., vous pouvez également lire un livre similaire tel que cc Deep Learning, Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction, Counterfactuals and Causal Inference: Methods and Principles for Social Research, The Elements of Statistical Learning: Data Mining, Inference, and Prediction
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