SKU/Artículo: AMZ-9819553075
Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.50 kg
0.50 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- 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
AR$330.191
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AR$132.077
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AR$330.191
60% OFF
AR$132.077
10% OFF con cupon ANIVERSARIO10
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