Description
Book SynopsisBayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side by side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine-learning practice: dynamic networks, networks with heterogeneous variables, and model validation.
The first three chapters explain the whole process of Bayesian network modelling, from structure learning to parameter learning to inference. These chapters cover discrete, Gaussian, and conditional Gaussian Bayesian networks. The following two chapters delve into dynamic networks (to model temporal data) and into networks including arbitrary random variables (using Stan). The book then gives a concise but rigorous tre
Trade Review
"The book has a practice-oriented, hands-on approach with R codes and outputs, clear examples, relevant exercises to elucidate the main concepts (with solutions included at the end). [...] Statisticians, data scientists and other researchers new to Bayesian networks might also find it valuable and interesting."
-Anikó Lovik in ISCB News, June 2022
Praise for the first edition:
"… an excellent introduction to Bayesian networks with detailed user-friendly examples and computer-aided illustrations. I enjoyed reading Bayesian Networks: With Examples in R and think that the book will serve very well as an introductory textbook for graduate students, non-statisticians, and practitioners in Bayesian networks and the related areas."
—Biometrics, September 2015
"Several excellent books about learning and reasoning with Bayesian networks are available and Bayesian Networks: With Examples in R provides a useful addition to this list. The book is usually easy to read, rich in examples that are described in great detail, and also provides several exercises with solutions that can be valuable to students. The book also provides an introduction to topics that are not covered in detail in existing books … . It also provides a good list of search algorithms for learning Bayesian network structures. But the major strength of the book is the simplicity that makes it particularly suitable to students with sufficient background in probability and statistical theory, particularly Bayesian statistics."
—Journal of the American Statistical Association, June 2015
Table of Contents1. The Discrete Case: Multinomial Bayesian Networks. 2. The Discrete Case: Multinomial Bayesian Networks. 3. The Mixed Case: Conditional Gaussian Bayesian Networks. 4. Time Series: Dynamic Bayesian Networks. 5. More Complex Cases: General Bayesian Networks. 6. Theory and Algorithms for Bayesian Networks. 7. Software for Bayesian Networks. 8. Real-World Applications of Bayesian Networks.
