Bayesian Networks: With Examples in R

1. Auflage 2014
ISBN: 978-1-4822-2558-7
Verlag: CRC PR INC

Buch, Englisch, 241 Seiten, Format (B × H): 159 mm x 241 mm, Gewicht: 557 g

Reihe: Chapman & Hall/CRC Texts in Statistical Science

Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained
Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets.

The first three chapters explain the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. These chapters cover discrete Bayesian, Gaussian Bayesian, and hybrid networks, including arbitrary random variables.

The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R and other software packages appropriate for Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein signaling network paper and graphical modeling approaches for predicting the composition of different body parts.

Suitable for graduate students and non-statisticians, this text provides an introductory overview of Bayesian networks. It gives readers a clear, practical understanding of the general approach and steps involved.

Zielgruppe

Graduate students in statistics, computer science, mathematics, and bioinformatics.

Autoren/Hrsg.

Scutari, Marco

Denis, Jean-Baptiste

Inhaltsverzeichnis

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.

Über Autor(innen)

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.