Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic - Brossura

Suzuki, Joe

Brossura

ISBN 10: 9819553075 ISBN 13: 9789819553075

Casa editrice: Springer, 2026

Riassunto

Beginning with a gentle introduction to causal discovery and the foundations of probability and statistics, this textbook is written in a highly pedagogical way. By uniting probability theory, statistical inference, and graph theory, the book offers a systematic pathway from foundational principles to cutting-edge algorithms, including independence tests, the PC algorithm, LiNGAM, information criteria, and Bayesian methods. Far more than a theoretical treatment, this volume emphasizes hands-on learning through Python implementations, carefully designed exercises with solutions, and intuitive graphical illustrations. Readers will gain the ability to see, run, and understand causal discovery methods in practice. 

Key features of this book include:

• A clear and self-contained introduction, bridging probability, statistics, and modern causal discovery techniques
• 100 exercises with solutions, supporting self-study and classroom use
• Reproducible Python code, allowing readers to implement and extend the methods themselves
• Intuitive figures and visual explanations that clarify abstract concepts
• Broad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference

Su questo libro

Informazioni sull'autore

Joe Suzuki is a professor of statistics at Osaka University, Japan. 

 

Dalla quarta di copertina

Beginning with a gentle introduction to causal discovery and the foundations of probability and statistics, this textbook is written in a highly pedagogical way. By uniting probability theory, statistical inference, and graph theory, the book offers a systematic pathway from foundational principles to cutting-edge algorithms, including independence tests, the PC algorithm, LiNGAM, information criteria, and Bayesian methods. Far more than a theoretical treatment, this volume emphasizes hands-on learning through Python implementations, carefully designed exercises with solutions, and intuitive graphical illustrations. Readers will gain the ability to see, run, and understand causal discovery methods in practice. 

Key features of this book include:

• A clear and self-contained introduction, bridging probability, statistics, and modern causal discovery techniques
• 100 exercises with solutions, supporting self-study and classroom use
• Reproducible Python code, allowing readers to implement and extend the methods themselves
• Intuitive figures and visual explanations that clarify abstract concepts
• Broad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference

Dettagli dell'edizione

• Editore: Springer
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 10: 9819553075
• ISBN 13: 9789819553075
• Rilegatura: Copertina flessibile
• Numero di pagine: 208
• Contatto del produttore: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3,69115 Heidelberg, Germany; Heidelberg; 69115; Germania