Bayesian Networks, 2nd Edition

Bayesian Networks, 2nd Edition

by Marco Scutari, Jean-Baptiste Denis
Released July 2021
Publisher(s): Chapman and Hall/CRC
ISBN: 9781000410396

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Book description

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.

Table of contents

  1. Cover
  2. Half Title
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Dedication
  7. Contents
  8. Preface to the Second Edition
  9. Preface to the First Edition
  10. 1 The Discrete Case: Multinomial Bayesian Networks
    1. 1.1 Introductory Example: Train-Use Survey
    2. 1.2 Graphical Representation
    3. 1.3 Probabilistic Representation
    4. 1.4 Estimating the Parameters: Conditional Probability Tables
    5. 1.5 Learning the DAG Structure: Tests and Scores
      1. 1.5.1 Conditional Independence Tests
      2. 1.5.2 Network Scores
    6. 1.6 Using Discrete Bayesian Networks
      1. 1.6.1 Using the DAG Structure
      2. 1.6.2 Using the Conditional Probability Tables
        1. 1.6.2.1 Exact Inference
        2. 1.6.2.2 Approximate Inference
    7. 1.7 Plotting Discrete Bayesian Networks
      1. 1.7.1 Plotting DAGs
      2. 1.7.2 Plotting Conditional Probability Distributions
    8. 1.8 Further Reading
  11. 2 The Continuous Case: Gaussian Bayesian Networks
    1. 2.1 Introductory Example: Crop Analysis
    2. 2.2 Graphical Representation
    3. 2.3 Probabilistic Representation
    4. 2.4 Estimating the Parameters: Correlation Coefficients
    5. 2.5 Learning the DAG Structure: Tests and Scores
      1. 2.5.1 Conditional Independence Tests
      2. 2.5.2 Network Scores
    6. 2.6 Using Gaussian Bayesian Networks
      1. 2.6.1 Exact Inference
      2. 2.6.2 Approximate Inference
    7. 2.7 Plotting Gaussian Bayesian Networks
      1. 2.7.1 Plotting DAGs
      2. 2.7.2 Plotting Conditional Probability Distributions
    8. 2.8 More Properties
    9. 2.9 Further Reading
  12. 3 The Mixed Case: Conditional Gaussian Bayesian Networks
    1. 3.1 Introductory Example: Healthcare Costs
    2. 3.2 Graphical and Probabilistic Representation
    3. 3.3 Estimating the Parameters: Mixtures of Regressions
    4. 3.4 Learning the DAG Structure: Tests and Scores
    5. 3.5 Using Conditional Gaussian Bayesian Networks
    6. 3.6 Further Reading
  13. 4 Time Series: Dynamic Bayesian Networks
    1. 4.1 Introductory Example: Domotics
    2. 4.2 Graphical Representation
    3. 4.3 Probabilistic Representation
    4. 4.4 Learning a Dynamic Bayesian Network
    5. 4.5 Using Dynamic Bayesian Networks
    6. 4.6 Plotting Dynamic Bayesian Networks
    7. 4.7 Further Reading
  14. 5 More Complex Cases: General Bayesian Networks
    1. 5.1 Introductory Example: A&E Waiting Times
    2. 5.2 Graphical and Probabilistic Representation
    3. 5.3 Building the Model in Stan
      1. 5.3.1 Generating Data
      2. 5.3.2 Exploring the Variables
    4. 5.4 Estimating the Parameters in Stan
    5. 5.5 Further Reading
  15. 6 Theory and Algorithms for Bayesian Networks
    1. 6.1 Conditional Independence and Graphical Separation
    2. 6.2 Bayesian Networks
    3. 6.3 Markov Blankets
    4. 6.4 Moral Graphs
    5. 6.5 Bayesian Network Learning
      1. 6.5.1 Structure Learning
        1. 6.5.1.1 Constraint-Based Algorithms
        2. 6.5.1.2 Score-Based Algorithms
        3. 6.5.1.3 Hybrid Algorithms
      2. 6.5.2 Parameter Learning
    6. 6.6 Bayesian Network Inference
      1. 6.6.1 Probabilistic Reasoning and Evidence
      2. 6.6.2 Algorithms for Belief Updating
        1. 6.6.2.1 Exact Inference Algorithms
        2. 6.6.2.2 Approximate Inference Algorithms
    7. 6.7 Causal Bayesian Networks
    8. 6.8 Evaluating a Bayesian Network
    9. 6.9 Further Reading
  16. 7 Software for Bayesian Networks
    1. 7.1 An Overview of R Packages
      1. 7.1.1 The deal Package
      2. 7.1.2 The catnet Package
      3. 7.1.3 The pcalg Package
      4. 7.1.4 The abn Package
    2. 7.2 Stan and BUGS Software Packages
      1. 7.2.1 Stan: A Feature Overview
      2. 7.2.2 Inference Based on MCMC Sampling
    3. 7.3 Other Software Packages
      1. 7.3.1 BayesiaLab
      2. 7.3.2 Hugin
      3. 7.3.3 GeNIe
  17. 8 Real-World Applications of Bayesian Networks
    1. 8.1 Learning Protein-Signalling Networks
      1. 8.1.1 A Gaussian Bayesian Network
      2. 8.1.2 Discretising Gene Expressions
      3. 8.1.3 Model Averaging
      4. 8.1.4 Choosing the Significance Threshold
      5. 8.1.5 Handling Interventional Data
      6. 8.1.6 Querying the Network
    2. 8.2 Predicting the Body Composition
      1. 8.2.1 Aim of the Study
      2. 8.2.2 Designing the Predictive Approach
        1. 8.2.2.1 Assessing the Quality of a Predictor
        2. 8.2.2.2 The Saturated BN
        3. 8.2.2.3 Convenient BNs
      3. 8.2.3 Looking for Candidate BNs
    3. 8.3 Further Reading
  18. A Graph Theory
    1. A.1 Graphs, Nodes and Arcs
    2. A.2 The Structure of a Graph
    3. A.3 Further Reading
  19. B Probability Distributions
    1. B.1 General Features
    2. B.2 Marginal and Conditional Distributions
    3. B.3 Discrete Distributions
      1. B.3.1 Binomial Distribution
      2. B.3.2 Multinomial Distribution
      3. B.3.3 Other Common Distributions
        1. B.3.3.1 Bernoulli Distribution
        2. B.3.3.2 Poisson Distribution
    4. B.4 Continuous Distributions
      1. B.4.1 Normal Distribution
      2. B.4.2 Multivariate Normal Distribution
      3. B.4.3 Other Common Distributions
        1. B.4.3.1 Chi-Square Distribution
        2. B.4.3.2 Student's t Distribution
        3. B.4.3.3 Beta Distribution
        4. B.4.3.4 Dirichlet Distribution
    5. B.5 Conjugate Distributions
    6. B.6 Further Reading
  20. C A Note about Bayesian Networks
    1. C.1 Bayesian Networks and Bayesian Statistics
  21. Glossary
  22. Solutions
  23. Bibliography
  24. Index

Product information

  • Title: Bayesian Networks, 2nd Edition
  • Author(s): Marco Scutari, Jean-Baptiste Denis
  • Release date: July 2021
  • Publisher(s): Chapman and Hall/CRC
  • ISBN: 9781000410396