Graphical Models and Causal Discovery with Python

100 Exercises for Building Logic

Joe Suzuki (Autor)

Buch | Softcover

2026
Springer Verlag, Singapore
978-981-95-5307-5 (ISBN)

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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

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

A Gentle Introduction to Causal Discovery.- Foundations of Probability and Statistics.- Graphical Models.- Testing Independence and Conditional Independence with Kernels.- The PC Algorithm.- LiNGAM.- Information Criteria and Marginal Likelihood.- Score-Based Structure Learning.

Erscheint lt. Verlag	12.4.2026
Zusatzinfo	Approx. 250 p.
Verlagsort	Singapore
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
Schlagworte	Causal Discovery • Data Science • Graphical model • Information Criteria • lingam • machine learning • PGM • probabilistic graphical model • Python
ISBN-10	981-95-5307-5 / 9819553075
ISBN-13	978-981-95-5307-5 / 9789819553075
Zustand	Neuware