"... 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

" . . . the book by Scutari and Denis provides a generous coverage of Bayesian networks, well beyond a simple introduction, with excursions into advanced Bayesian computations, e.g. the use of BUGS, and the investigation of causality to give only two examples. The audience that can benefit from this book is large. Lecturers in advanced Artificial Intelligence, Machine Learning, or Statistics courses could use it as a textbook for theoretical foundations and/or as a source of inspiration for practical tutorials. The book also offers solid answers to questions that might be posed by researchers (with prior exposure to standard Statistics) who are in need of quantitative approaches to the retrieval of relationships from complex multivariate data sets."
―Australian & New Zealand Journal of Statistics, 2017

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