Graphical Models and Causal Discovery with Python

100 Exercises for Building Logic

von Joe Suzuki

195 Seiten, Taschenbuch

Kurzbeschreibung des Verlags

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

• Themen: Informatik und Informationstechnologie Informatik Theoretische Informatik Mathematik für Informatiker
• ISBN: 9789819553075
• Sprache: Englisch
• Erscheinungsdatum: 30.05.2026
• Größe: 23.5 x 15.5 cm
• Verlag: Springer Singapore