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Sinopsis
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
"Sinopsis" puede pertenecer a otra edición de este libro.
Acerca del autor
Joe Suzuki is a professor of statistics at Osaka University, Japan.
De la contraportada
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
"Sobre este título" puede pertenecer a otra edición de este libro.
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Graphical Models and Causal Discovery with Python (eng)
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Graphical Models and Causal Discovery with Python
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Graphical Models and Causal Discovery with Python
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Springer Verlag, Singapore, SG, 2026
ISBN 10: 9819553075
ISBN 13: 9789819553075
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Paperback. Condición: New. 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 techniques100 exercises with solutions, supporting self-study and classroom useReproducible Python code, allowing readers to implement and extend the methods themselvesIntuitive figures and visual explanations that clarify abstract conceptsBroad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference. Nº de ref. del artículo: LU-9789819553075
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Graphical Models and Causal Discovery with Python (Paperback)
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Springer Verlag, Singapore, Singapore, 2026
ISBN 10: 9819553075
ISBN 13: 9789819553075
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Paperback. Condición: new. Paperback. 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 techniques100 exercises with solutions, supporting self-study and classroom useReproducible Python code, allowing readers to implement and extend the methods themselvesIntuitive figures and visual explanations that clarify abstract conceptsBroad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9789819553075
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Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic
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Graphical Models and Causal Discovery with Python
Publicado por
Springer Verlag, Singapore, SG, 2026
ISBN 10: 9819553075
ISBN 13: 9789819553075
Nuevo
Paperback
Librería: Rarewaves USA, HEBRON, KY, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: New. 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 techniques100 exercises with solutions, supporting self-study and classroom useReproducible Python code, allowing readers to implement and extend the methods themselvesIntuitive figures and visual explanations that clarify abstract conceptsBroad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference. Nº de ref. del artículo: LU-9789819553075
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Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic
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Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic
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Graphical Models and Causal Discovery with Python
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Springer Verlag, Singapore Jul 2026, 2026
ISBN 10: 9819553075
ISBN 13: 9789819553075
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 techniques100 exercises with solutions, supporting self-study and classroom useReproducible Python code, allowing readers to implement and extend the methods themselvesIntuitive figures and visual explanations that clarify abstract conceptsBroad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference 195 pp. Englisch. Nº de ref. del artículo: 9789819553075
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Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic
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