
Contents
Preface xiii
1 The Di screte Case: Multinomial Bayesian Networks 1
1.1 Introductory Example: Train Use Survey . . . . . . . . . . . 1
1.2 Graphical Representation . . . . . . . . . . . . . . . . . . . . 2
1.3 Probabilistic Representation . . . . . . . . . . . . . . . . . . 7
1.4 Estimating the Parameters: Conditional Probability Tables . 11
1.5 Learning the DAG Structure: Tests and Scores . . . . . . . . 14
1.5.1 Conditional Independence Tests . . . . . . . . . . . . . 15
1.5.2 Network Scores . . . . . . . . . . . . . . . . . . . . . . 17
1.6 Using Discrete BNs . . . . . . . . . . . . . . . . . . . . . . . 20
1.6.1 Using the DAG Structure .