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Idioma: Inglés
Publicado por The MIT Press Bookstore, 2017
ISBN 10: 0262037319 ISBN 13: 9780262037310
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Añadir al carritoHardcover. Condición: 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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ISBN 10: 0262037319 ISBN 13: 9780262037310
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Añadir al carritoCondición: 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.
Idioma: Inglés
Publicado por MIT Press Ltd Apr 2018, 2018
ISBN 10: 0262037319 ISBN 13: 9780262037310
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Añadir al carritoBuch. Condición: 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.
Idioma: Inglés
Publicado por MIT Press Ltd, Cambridge, Mass., 2017
ISBN 10: 0262037319 ISBN 13: 9780262037310
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Añadir al carritoHardcover. Condición: 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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Añadir al carritoBuch. Condición: 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.