Causal Discovery: Foundations, Algorithms and Applications - Couverture rigide

Sucar, Luis Enrique

9783031983443: Causal Discovery: Foundations, Algorithms and Applications

Couverture rigide

ISBN 10 : 3031983440 ISBN 13 : 9783031983443

Editeur : Birkhauser Verlag AG, 2025

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This book presents an overview of causal discovery, an emergent field with important developments in the last few years, and multiple applications in several fields.

The book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, (c) causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.

Topics and features:

Covers the main types of causal discovery: learning from observational data, learning from interventional data, and learning from temporal data

This book can be used as a textbook for an advanced undergraduate or a graduate course on causal discovery for students of computer science, engineering, social sciences, etc. It can also be used as a complement to a course on causality, together with another text on causal reasoning. It could also serve as a reference book for professionals that want to apply causal models in different areas, or anyone who is interested in knowing the basis of these techniques.

The intended audience are students and professionals in computer science, statistics and

engineering who want to know the principles of causal discovery and / or applied them in different

domains. It could also be of interest to students and professionals in other areas who want to apply

causal discovery, for instance in medicine and economics.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

� propos de l?auteur

L. Enrique Sucar is Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics, Puebla, Mexico. He has published more than 400 papers in refereed journals and conferences, and is author of the Springer book, Probabilistic Graphical Models (2021, 2^nd ed.).

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Birkhauser Verlag AG
Date d'�dition: 2025
Langue: anglais
ISBN 10: 3031983440
ISBN 13: 9783031983443
Reliure: Reli�
Nombre de pages: 250
Coordonn�es du fabricant: ProductSafety@springernature.com
ProductSafety@springernature.com

Poststr. 9
Darmstadt
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Allemagne

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Causal Discovery

Luis Enrique Sucar

Edit� par Springer, Berlin, Birkh�user, 2025

ISBN 10 : 3031983440 ISBN 13 : 9783031983443

Neuf Couverture rigide

impression � la demande

Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents an overview of causal discovery, an emergent field with important developments in the last few years, and multiple applications in several fields.The book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, (c) causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.Topics and features:Includes the necessary background material: a review of probability and graph theory, Bayesian networks, causal graphical models and causal reasoningCovers the main types of causal discovery: learning from observational data, learning from interventional data, and learning from temporal dataIllustrates the application of causal discovery in practical problemsIncludes some of the latest developments in the field, such as continuous optimization, causal event networks, causal discovery under subsampling, subject specific causal models, and causal reinforcement learningProvides chapter exercises, including suggestions for research and programming projectsThis book can be used as a textbook for an advanced undergraduate or a graduate course on causal discovery for students of computer science, engineering, social sciences, etc. It can also be used as a complement to a course on causality, together with another text on causal reasoning. It could also serve as a reference book for professionals that want to apply causal models in different areas, or anyone who is interested in knowing the basis of these techniques.The intended audience are students and professionals in computer science, statistics andengineering who want to know the principles of causal discovery and / or applied them in differentdomains. It could also be of interest to students and professionals in other areas who want to applycausal discovery, for instance in medicine and economics. 229 pp. Englisch. N� de r�f. du vendeur 9783031983443

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Causal Discovery

Sucar, Luis Enrique

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Causal Discovery | Foundations, Algorithms and Applications

Luis Enrique Sucar

Edit� par Birkh�user, 2025

ISBN 10 : 3031983440 ISBN 13 : 9783031983443

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Causal Discovery

Luis Enrique Sucar

Edit� par Birkh�user, Palgrave Macmillan Okt 2025, 2025

ISBN 10 : 3031983440 ISBN 13 : 9783031983443

Neuf Couverture rigide

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents an overview of causal discovery, an emergent field with important developments in the last few years, and multiple applications in several fields.The book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, (c) causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.Topics and features:Includes the necessary background material: a review of probability and graph theory, Bayesian networks, causal graphical models and causal reasoningCovers the main types of causal discovery: learning from observational data, learning from interventional data, and learning from temporal dataIllustrates the application of causal discovery in practical problemsIncludes some of the latest developments in the field, such as continuous optimization, causal event networks, causal discovery under subsampling, subject specific causal models, and causal reinforcement learningProvides chapter exercises, including suggestions for research and programming projectsThis book can be used as a textbook for an advanced undergraduate or a graduate course on causal discovery for students of computer science, engineering, social sciences, etc. It can also be used as a complement to a course on causality, together with another text on causal reasoning. It could also serve as a reference book for professionals that want to apply causal models in different areas, or anyone who is interested in knowing the basis of these techniques.The intended audience are students and professionals in computer science, statistics andengineering who want to know the principles of causal discovery and / or applied them in differentdomains. It could also be of interest to students and professionals in other areas who want to applycausal discovery, for instance in medicine and economics.Springer Nature c/o IBS, Benzstrasse 21, 48619 Heek 252 pp. Englisch. N� de r�f. du vendeur 9783031983443

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Causal Discovery : Foundations, Algorithms and Applications

Luis Enrique Sucar

Edit� par Birkh�user, Springer International Publishing, 2025

ISBN 10 : 3031983440 ISBN 13 : 9783031983443

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents an overview of causal discovery, an emergent field with important developments in the last few years, and multiple applications in several fields.The book is divided into three parts. The first part provides the necessary background on causal graphical models and causal reasoning. The second describes the main algorithms and techniques for causal discovery: (a) causal discovery from observational data, (b) causal discovery from interventional data, (c) causal discovery from temporal data, and (d) causal reinforcement learning. The third part provides several examples of causal discovery in practice, including applications in biomedicine, social sciences, artificial intelligence and robotics.Topics and features:Includes the necessary background material: a review of probability and graph theory, Bayesian networks, causal graphical models and causal reasoningCovers the main types of causal discovery: learning from observational data, learning from interventional data, and learning from temporal dataIllustrates the application of causal discovery in practical problemsIncludes some of the latest developments in the field, such as continuous optimization, causal event networks, causal discovery under subsampling, subject specific causal models, and causal reinforcement learningProvides chapter exercises, including suggestions for research and programming projectsThis book can be used as a textbook for an advanced undergraduate or a graduate course on causal discovery for students of computer science, engineering, social sciences, etc. It can also be used as a complement to a course on causality, together with another text on causal reasoning. It could also serve as a reference book for professionals that want to apply causal models in different areas, or anyone who is interested in knowing the basis of these techniques.The intended audience are students and professionals in computer science, statistics andengineering who want to know the principles of causal discovery and / or applied them in differentdomains. It could also be of interest to students and professionals in other areas who want to applycausal discovery, for instance in medicine and economics. N� de r�f. du vendeur 9783031983443

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