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9780262037310 - Elements of Causal Inference: Foundations and Learning Algorithms Adaptive Computation and Machine Learning Series by Peters, Jonas; Janzing, Dominik; Scholkopf, Be... (27 results)
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Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
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Elements of Causal Inference
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Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Language: English
Published by The MIT Press Bookstore, 2017
ISBN 10: 0262037319 ISBN 13: 9780262037310
Condition: New.
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Elements of Causal Inference (Hardcover)
Language: English
Published by MIT Press Ltd, Cambridge, Mass., 2017
ISBN 10: 0262037319 ISBN 13: 9780262037310
Hardcover. Condition: new. Hardcover. A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models- how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts. A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Language: English
Published by The MIT Press Bookstore, 2017
ISBN 10: 0262037319 ISBN 13: 9780262037310
Condition: New.
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Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Language: English
Published by The MIT Press Bookstore, 2017
ISBN 10: 0262037319 ISBN 13: 9780262037310
Condition: New.
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Elements of Causal Inference : Foundations and Learning Algorithms
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Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning Series)
Language: English
Published by MIT Press 2018-01-16, 2018
ISBN 10: 0262037319 ISBN 13: 9780262037310
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Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
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Add to basketHardcover. Condition: Brand New. 288 pages. 9.00x7.00x1.00 inches. In Stock.
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Elements of Causal Inference: Foundations and Learning Algorithms
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Elements of Causal Inference : Foundations and Learning Algorithms
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Elements of Causal Inference: Foundations and Learning Algorithms
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Add to basketHardcover. Condition: Brand New. 288 pages. 9.00x7.00x1.00 inches. In Stock.
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Condition: New. Jonas Peters is Associate Professor of Statistics at the University of Copenhagen.Dominik Janzing is a Senior Research Scientist at the Max Planck Institute for Intelligent Systems in Tübingen, Germany.Bernhard Schölkopf is Directo.
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Elements of Causal Inference : Foundations and Learning Algorithms
Language: English
Published by MIT Press Ltd Apr 2018, 2018
ISBN 10: 0262037319 ISBN 13: 9780262037310
Buch. Condition: Neu. Neuware - A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
-
Elements of Causal Inference (Hardcover)
Language: English
Published by MIT Press Ltd, Cambridge, Mass., 2017
ISBN 10: 0262037319 ISBN 13: 9780262037310
Hardcover. Condition: new. Hardcover. A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models- how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts. A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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US$ 60.51
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Add to basketHardback. Condition: New.
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Buch. Condition: Neu. Elements of Causal Inference | Foundations and Learning Algorithms | Jonas Peters (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2018 | MIT Press | EAN 9780262037310 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
