Graphical Models and Causal Discovery with Python
100 Exercises for Building Logic
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Beschreibung
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
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
Presents 100 carefully selected exercises accompanied by solutions in the main text Provides an in-depth understanding of Python source programs, rather than explaining how to use ready-made packages Written in an easy-to-follow and self-contained style
Autor*in
Joe Suzuki
Themen in »Graphical Models and Causal Discovery with Python«
Graphical Model Python PGM Probabilistic Graphical Model LiNGAM Causal Discovery Information Criteria