Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic

Graphical Models and Causal Discovery with Python: 100 Exercises for Building Logic

Suzuki, Joe

Paperback

General ComputersProbability & Statistics

Currently unavailable to order

ISBN10: 9819553075
ISBN13: 9789819553075
Publisher: Springer
Published: Jun 2 2026
Pages: 195
Weight: 0.69
Height: 0.43 Width: 6.19 Depth: 9.24
Language: English
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.

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