Bayesian Networks

with Examples in R


M. Scutari and J.-B. Denis (2021).
Texts in Statistical Science, Chapman & Hall/CRC (US), 2nd edition.


Coming soon!

Table of Contents

  1. The Discrete Case: Multinomial Bayesian Networks

    1. Introductory Example: Train Use Survey
    2. Graphical Representation
    3. Probabilistic Representation
    4. Estimating the Parameters: Conditional Probability Tables
    5. Learning the DAG Structure: Tests and Scores
      1. Conditional Independence Tests
      2. Network Scores
    6. Using Discrete BNs
      1. Using the DAG Structure
      2. Using the Conditional Probability Tables
        1. Exact Inference
        2. Approximate Inference
    7. Plotting BNs
      1. Plotting DAGs
      2. Conditional Probability Distributions
    8. Further Reading
       Exercises
  2. The Continuous Case: Gaussian Bayesian Networks


    1. Introductory Example: Crop Analysis
    2. Graphical Representation
    3. Probabilistic Representation
    4. Estimating the Parameters: Correlation Coefficients
    5. Learning the DAG Structure
      1. Conditional Independence Tests
      2. Network Scores
    6. Using Gaussian Bayesian Networks
      1. Exact Inference
      2. Approximate Inference
    7. Plotting Gaussian Bayesian Networks
      1. Plotting DAGs
      2. Plotting Conditional Probability Distributions
    8. More Properties
    9. Further Reading
       Exercises
  3. The Mixed Case: Conditional Gaussian Bayesian Networks

    (NEW)
    1. Introductory Example: Healthcare Costs (NEW)
    2. Graphical and Probabilistic Representation (NEW)
    3. Estimating the Parameters: Mixtures of Regressions (NEW)
    4. Learning the DAG Structure: Tests and Scores (NEW)
    5. Using Conditional Gaussian Bayesian Networks (NEW)
    6. Further Reading (NEW)
       Exercises
  4. Time Series: Dynamic Bayesian Networks

    (NEW)
    1. Introductory Example: Domotics (NEW)
    2. Graphical Representation (NEW)
    3. Probabilistic Representation (NEW)
    4. Learning a Dynamic Bayesian Network (NEW)
    5. Using Dynamic Bayesian Networks (NEW)
    6. Plotting Dynamic Bayesian Networks (NEW)
    7. Further Reading (NEW)
       Exercises
  5. More Complex Cases: Hybrid Bayesian Networks

    (NEW)
    1. Introductory Example: A&E Waiting Times (NEW)
    2. Graphical and Probabilistic Representation (NEW)
    3. Building the Model in Stan (NEW)
      1. Generating Data (NEW)
      2. Exploring the Variables (NEW)
    4. Estimating the Parameters in Stan (NEW)
    5. Further Reading (NEW)
       Exercises
  6. Theory and Algorithms for Bayesian Networks

    1. Conditional Independence and Graphical Separation
    2. Bayesian Networks
    3. Markov Blankets
    4. Moral Graphs
    5. Bayesian Network Learning
      1. Structure Learning
        1. Constraint-based Algorithms
        2. Score-based Algorithms
        3. Hybrid Algorithms
      2. Parameter Learning
    6. Bayesian Network Inference
      1. Probabilistic Reasoning and Evidence
      2. Algorithms for Belief Updating
        1. Exact Inference Algorithms (NEW)
        2. Approximate Inference Algorithms (NEW)
    7. Causal Bayesian Networks
    8. Evaluating a Bayesian Network (NEW)
    9. Further Reading
       Exercises
  7. Software for Bayesian Networks

    1. An Overview of R Packages
      1. The deal package
      2. The catnet package
      3. The pcalg package
      4. The abn package (NEW)
    2. Stan and BUGS Software Packages (NEW)
      1. Stan: a Feature Overview (NEW)
      2. Inference Based on MCMC Sampling (NEW)
    3. Other Software Packages
      1. BayesiaLab
      2. Hugin
      3. GeNIe (NEW)
       Exercises
  8. Real-World Applications of Bayesian Networks

    1. Learning Protein-Signalling Networks
      1. A Gaussian Bayesian Network
      2. Discretising Gene Expressions
      3. Model Averaging
      4. Choosing the Significance Threshold
      5. Handling Interventional Data
      6. Querying the Network
    2. Predicting the Boby Composition
      1. Aim of the Study
      2. Designing the Predictive Approach
        1. Assessing the Quality of a Predictor
        2. The Saturated BN
        3. Convenient BNs
      3. Looking for Candidate BNs
    3. Further Reading
  1. Graph Theory

    1. Graphs, Nodes and Arcs
    2. The Structure of a Graph
    3. Further Reading
  2. Probability Distributions

    1. General Features
    2. Marginal and Conditional Distributions
    3. Discrete Distributions
    4. Continuous Distributions
    5. Conjugate Distributions
    6. Further Reading
  3. A Note About Bayesian Networks

   Glossary

   Solutions

   Bibliography

   Index