Verfügbarkeit: Causality | Technische Universität München

Causality: models, reasoning, and inference

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 intellig...

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Bibliographische Detailangaben
Beteilige Person:	Pearl, Judea 1936- (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	Englisch
Veröffentlicht:	Cambridge Cambridge University Press 2021
Ausgabe:	Second edition, reprinted with corrections
Schlagwörter:	Causation Probabilities Vernunft Statistische Schlussweise Unsicheres Schließen Statistische Analyse Kausalität Kausalitätswahrnehmung Wahrscheinlichkeit Methode Wahrscheinlichkeitsrechnung Wissenschaft Kausalmodell
Links:	https://doi.org/10.1017/CBO9780511803161 https://doi.org/10.1017/CBO9780511803161 https://doi.org/10.1017/CBO9780511803161
Zusammenfassung:	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
Umfang:	1 Online-Ressource (xix, 465 Seiten)
ISBN:	9780511803161
DOI:	10.1017/CBO9780511803161

Internformat

MARC


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dewey-tens	120 - Epistemology, causation, humankind
discipline	Allgemeines Informatik Soziologie Mathematik Philosophie Wirtschaftswissenschaften
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illustrated	Not Illustrated
indexdate	2024-12-20T19:54:48Z
institution	BVB
isbn	9780511803161
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-034152102
oclc_num	1376412251
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owner	DE-521 DE-19 DE-BY-UBM
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physical	1 Online-Ressource (xix, 465 Seiten)
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publishDate	2021
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publisher	Cambridge University Press
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spelling	Pearl, Judea 1936- Verfasser (DE-588)139283331 aut Causality models, reasoning, and inference Judea Pearl, University of California, Los Angeles Second edition, reprinted with corrections Cambridge Cambridge University Press 2021 1 Online-Ressource (xix, 465 Seiten) txt rdacontent c rdamedia cr rdacarrier 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 Causation Probabilities Vernunft (DE-588)4063106-0 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 gnd rswk-swf Unsicheres Schließen (DE-588)4361044-4 gnd rswk-swf Statistische Analyse (DE-588)4116599-8 gnd rswk-swf Kausalität (DE-588)4030102-3 gnd rswk-swf Kausalitätswahrnehmung (DE-588)4290135-2 gnd rswk-swf Wahrscheinlichkeit (DE-588)4137007-7 gnd rswk-swf Methode (DE-588)4038971-6 gnd rswk-swf Wahrscheinlichkeitsrechnung (DE-588)4064324-4 gnd rswk-swf Wissenschaft (DE-588)4066562-8 gnd rswk-swf Kausalmodell (DE-588)4123496-0 gnd rswk-swf Wissenschaft (DE-588)4066562-8 s Methode (DE-588)4038971-6 s Kausalitätswahrnehmung (DE-588)4290135-2 s 1\p DE-604 Kausalität (DE-588)4030102-3 s Wahrscheinlichkeitsrechnung (DE-588)4064324-4 s 2\p DE-604 Wahrscheinlichkeit (DE-588)4137007-7 s 3\p DE-604 Vernunft (DE-588)4063106-0 s Kausalmodell (DE-588)4123496-0 s 4\p DE-604 Statistische Analyse (DE-588)4116599-8 s 5\p DE-604 Unsicheres Schließen (DE-588)4361044-4 s 6\p DE-604 Statistische Schlussweise (DE-588)4182963-3 s 7\p DE-604 Erscheint auch als Druck-Ausgabe, Hardcover 978-0-521-89560-6 https://doi.org/10.1017/CBO9780511803161 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 5\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 6\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 7\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk
spellingShingle	Pearl, Judea 1936- Causality models, reasoning, and inference Causation Probabilities Vernunft (DE-588)4063106-0 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Unsicheres Schließen (DE-588)4361044-4 gnd Statistische Analyse (DE-588)4116599-8 gnd Kausalität (DE-588)4030102-3 gnd Kausalitätswahrnehmung (DE-588)4290135-2 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd Methode (DE-588)4038971-6 gnd Wahrscheinlichkeitsrechnung (DE-588)4064324-4 gnd Wissenschaft (DE-588)4066562-8 gnd Kausalmodell (DE-588)4123496-0 gnd
subject_GND	(DE-588)4063106-0 (DE-588)4182963-3 (DE-588)4361044-4 (DE-588)4116599-8 (DE-588)4030102-3 (DE-588)4290135-2 (DE-588)4137007-7 (DE-588)4038971-6 (DE-588)4064324-4 (DE-588)4066562-8 (DE-588)4123496-0
title	Causality models, reasoning, and inference
title_auth	Causality models, reasoning, and inference
title_exact_search	Causality models, reasoning, and inference
title_full	Causality models, reasoning, and inference Judea Pearl, University of California, Los Angeles
title_fullStr	Causality models, reasoning, and inference Judea Pearl, University of California, Los Angeles
title_full_unstemmed	Causality models, reasoning, and inference Judea Pearl, University of California, Los Angeles
title_short	Causality
title_sort	causality models reasoning and inference
title_sub	models, reasoning, and inference
topic	Causation Probabilities Vernunft (DE-588)4063106-0 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Unsicheres Schließen (DE-588)4361044-4 gnd Statistische Analyse (DE-588)4116599-8 gnd Kausalität (DE-588)4030102-3 gnd Kausalitätswahrnehmung (DE-588)4290135-2 gnd Wahrscheinlichkeit (DE-588)4137007-7 gnd Methode (DE-588)4038971-6 gnd Wahrscheinlichkeitsrechnung (DE-588)4064324-4 gnd Wissenschaft (DE-588)4066562-8 gnd Kausalmodell (DE-588)4123496-0 gnd
topic_facet	Causation Probabilities Vernunft Statistische Schlussweise Unsicheres Schließen Statistische Analyse Kausalität Kausalitätswahrnehmung Wahrscheinlichkeit Methode Wahrscheinlichkeitsrechnung Wissenschaft Kausalmodell
url	https://doi.org/10.1017/CBO9780511803161
work_keys_str_mv	AT pearljudea causalitymodelsreasoningandinference

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