Bayesian Networks: With Examples in R

Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using hands-on examples and R code, covering structure learning, parameter learning, inference, dynamic networks, heterogeneous variables, and model validation. It also provides an overview of R packages and other software implementing Bayesian networks and evaluates two real-world examples. Online supplementary materials include the data sets and code used in the book.

Format: Hardback
Length: 258 pages
Publication date: 29 July 2021
Publisher: Taylor & Francis Ltd

Bayesian Networks: With Examples in R, Second Edition, introduces Bayesian networks through a hands-on approach, featuring simple yet meaningful examples that illustrate each step of the modelling process and discuss the underlying theory and its application using R code. The book covers discrete, Gaussian, and conditional Gaussian Bayesian networks, as well as dynamic networks for temporal data and networks with arbitrary random variables using Stan. It also provides a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. Additionally, it presents an overview of R packages and other software implementing Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein-signalling network published in Science and a probabilistic graphical model for predicting the composition of different body parts. The book provides a comprehensive overview of the field, covering theoretical and practical aspects, and equips readers with a clear, practical understanding of the key points behind this modelling approach. It also familiarises readers with the most relevant packages used to implement real-world analyses in R. The examples covered in the book span several application fields, demonstrating the versatility of Bayesian networks.

Weight: 562g
Dimension: 162 x 244 x 24 (mm)
ISBN-13: 9780367366513
Edition number: 2 New edition