Introduction. The Discrete Case: Multinomial Bayesian Networks. The Continuous Case: Gaussian Bayesian Networks. More Complex Cases. Theory and Algorithms for Bayesian Networks. Real-World Applications of Bayesian Networks. Appendices. Bibliography.
Marco Scutari is a research associate in statistical genetics at the Genetics Institute, University College London (UCL). He studied statistics and computer science at the University of Padova. He is the author and maintainer of the bnlearn R package. His research focuses on the theory of Bayesian networks and their applications to biological data. Jean-Baptiste Denis is a senior scientist in the Applied Mathematics and Computer Science Department at the French National Institute for Agricultural Research. His main research interests are Bayesian approaches to statistics and networks, especially applications to microbiological food safety.
"... 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
-Australian & New Zealand Journal of Statistics, 2017