2nd Edition

Bayesian Networks
With Examples in R



  • Available for pre-order. Item will ship after July 9, 2021
ISBN 9780367366513
July 9, 2021 Forthcoming by Chapman and Hall/CRC
272 Pages

USD $99.95

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Book Description

Bayesian Networks: With Examples in R, Second Edition introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples illustrate each step of the modelling process and discuss side-by-side the underlying theory and its application using R code. The examples start from the simplest notions and gradually increase in complexity. In particular, this new edition contains significant new material on topics from modern machine learning practice: dynamic networks, networks with heterogeneous variables, and model validation.

The first three chapters explain the whole process of Bayesian network modelling, from structure learning to parameter learning to inference. These chapters cover discrete, Gaussian, and conditional Gaussian Bayesian networks. The following two chapters delve into dynamic networks (to model temporal data) and into networks including arbitrary random variables (using Stan). 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 packages and other software implementing Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein-signaling network published in Science and a probabilistic graphical model for predicting the composition of different body parts.

Covering theoretical and practical aspects of Bayesian networks, this book provides you with an introductory overview of the field. It gives you a clear, practical understanding of the key points behind this modelling approach and, at the same time, it makes you familiar with the most relevant packages used to implement real-world analyses in R. The examples covered in the book span several application fields, data-driven models and expert systems, probabilistic and causal perspectives, thus giving you a starting point to work in a variety of scenarios.

Online supplementary materials include the data sets and the code used in the book, which will all be made available from https://www.bnlearn.com/book-crc-2ed/

Table of Contents

Preface to the Second Edition
Preface to the First Edition

1. The Discrete Case: Multinomial Bayesian Networks
 Introductory Example: Train Use Survey           
 Graphical Representation                    
 Probabilistic Representation                  
 Estimating the Parameters: Conditional Probability Tables 
 Learning the DAG Structure: Tests and Scores        
 Conditional Independence Tests             
 Network Scores                      
 Using Discrete Bayesian Networks               
 Using the DAG Structure                
 Using the Conditional Probability Tables       
 Exact Inference                
 Approximate Inference            
 Plotting Discrete Bayesian Networks              
 Plotting DAGs                      
 Plotting Conditional Probability Distributions    
 Further Reading                         

 2. The Continuous Case: Gaussian Bayesian Networks
 Introductory Example: Crop Analysis             
 Graphical Representation                    
 Probabilistic Representation                  
 Estimating the Parameters: Correlation Coefficients     
 Learning the DAG Structure: Tests and Scores        
 Conditional Independence Tests             
 Network Scores                      
 Using Gaussian Bayesian Networks              
 Exact Inference                      
 Approximate Inference                  
 Plotting Gaussian Bayesian Networks             
 Plotting DAGs                      
 Plotting Conditional Probability Distributions    
 More Properties                         
 Further Reading                         

 3. The Mixed Case: Conditional Gaussian Bayesian Networks
 Introductory Example: Healthcare Costs           
 Graphical and Probabilistic Representation          
 Estimating the Parameters: Mixtures of Regressions     
 Learning the DAG Structure: Tests and Scores        
 Using Conditional Gaussian Bayesian Networks       
 Further Reading                         

 4. Time Series: Dynamic Bayesian Networks
 Introductory Example: Domotics                
 Graphical Representation                    
 Probabilistic Representation                  
 Learning a Dynamic Bayesian Network            
 Using Dynamic Bayesian Networks               
 Plotting Dynamic Bayesian Networks             
 Further Reading                         

 5. More Complex Cases: General Bayesian Networks
 Introductory Example: A&E Waiting Times         
 Graphical and Probabilistic Representation          
 Building the Model in Stan                   
 Generating Data                     
 Exploring the Variables                 
 Estimating the Parameters in Stan               
 Further Reading                         

 6. Theory and Algorithms for Bayesian Networks
 Conditional Independence and Graphical Separation     
 Bayesian Networks                        
 Markov Blankets                         
 Moral Graphs                          
 Bayesian Network Learning                   
 Structure Learning                    
 Constraint-based Algorithms         
 Score-based Algorithms            
 Hybrid Algorithms               
 Parameter Learning                   
 Bayesian Network Inference                   
 Probabilistic Reasoning and Evidence         
 Algorithms for Belief Updating             
 Exact Inference Algorithms          
 Approximate Inference Algorithms      
 Causal Bayesian Networks                   
 Evaluating a Bayesian Network                 
 Further Reading                         

 7. Software for Bayesian Networks
 An Overview of R Packages                   
 The deal Package                    
 The catnet Package                   
 The pcalg Package                   
 The abn Package                     
 Stan and BUGS Software Packages              
 Stan: a Feature Overview                
 Inference Based on MCMC Sampling          
 Other Software Packages                    
 BayesiaLab                        
 Hugin                           
 GeNIe                           

 8. Real-World Applications of Bayesian Networks
 Learning Protein-Signalling Networks             
 A Gaussian Bayesian Network             
 Discretising Gene Expressions              
 Model Averaging                     
 Choosing the Significance Threshold          
 Handling Interventional Data              
 Querying the Network                  
 Predicting the Body Composition               
 Aim of the Study                     
 Designing the Predictive Approach           
 Assessing the Quality of a Predictor     
 The Saturated BN               
 Convenient BNs                
 Looking for Candidate BNs               
 Further Reading                    

A Graph Theory
A Graphs, Nodes and Arcs                    
A The Structure of a Graph                    
A Further Reading                         

B Probability Distributions
B General Features                         
B Marginal and Conditional Distributions            
B Discrete Distributions                      
B Binomial Distribution                  
B Multinomial Distribution                
B Other Common Distributions              
B Bernoulli Distribution             
B Poisson Distribution              
B Continuous Distributions                    
B Normal Distribution                   
B Multivariate Normal Distribution            
B Other Common Distributions              
B Chi-square Distribution            
B Student’s t Distribution            
B Beta Distribution               
B Dirichlet Distribution             
B Conjugate Distributions                     
B Further Reading                         

C A Note about Bayesian Networks
C Bayesian Networks and Bayesian Statistics          

 

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Author(s)

Biography

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.