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Bayesian Networks: With Examples in R de Marco Scutari

Bayesian Networks: With Examples in R: Chapman & Hall/CRC Texts in Statistical Science

Autor Marco Scutari, Jean-Baptiste Denis
en Limba Engleză Hardback – 29 iul 2021
The book introduces Bayesian networks using simple yet meaningful examples. Discrete Bayesian networks are described first followed by Gaussian Bayesian networks and mixed networks. All steps in learning are illustrated with R code.
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Specificații

ISBN-13: 9780367366513
ISBN-10: 0367366517
Pagini: 274
Dimensiuni: 156 x 234 x 20 mm
Greutate: 0.62 kg
Ediția:2 ed
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Texts in Statistical Science

Cuprins

1. 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.

Notă biografică

Marco Scutari is a Senior Lecturer at Istituto Dalle Molle di Studisull'Intelligenza Artificiale (IDSIA), Switzerland. He has held positions in Statistics, Statistical Genetics and Machine Learning in the UK and Switzerland since completing his Ph.D. in Statistics in 2011. His research focuses on the theory of Bayesian networks and their applications to biological and clinical data, as well as statistical computing and software engineering.
Jean-Baptiste Denis was formerly appointed as a statistician and modeller at the "Mathematics and Applied Informatics from Genome to Environment" unit of the French National Research Institute for Agriculture, Food and Environment. His main research interests were the modelling of two-way tables and Bayesian approaches, especially applied to genotype-by-environment interactions and microbiological food safety.

Recenzii

"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

Descriere

The book introduces Bayesian networks using simple yet meaningful examples. Discrete Bayesian networks are described first followed by Gaussian Bayesian networks and  mixed networks. All steps in learning are illustrated with R code.