Bayesian Networks: With Examples in R

by Marco Scutari, Jean-Baptiste Denis

Understand the Foundations of Bayesian Networks―Core Properties and Definitions Explained Bayesian 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 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.

241 pages, Hardcover

First published July 15, 2014

Reviews

[Kaniballos · Rating 4 out of 5]

Excellent introduction with many quite clear examples and well defined theory in a very (at least for me) complex tool.

[Mike Peleah · Rating 4 out of 5]

Good introduction into Bayesian Networks, with practical examples in R, downloadable from book website http://www.bnlearn.com/book-crc/