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 sets."
—Australian & New Zealand Journal of Statistics, 2017
|
Ask a Question About this Product More... |
|
