Bayesian Networks and Decision Graphs

Chapters

1Foundations of Probability and Graphical Models

2Bayesian Networks: Concepts and Construction

3Conditional Independence and d-Separation

4Exact Inference in Bayesian Networks

5Approximate Inference Techniques

6Parameter Learning for Bayesian Networks

7Structure Learning and Causal Discovery

8Continuous and Hybrid Bayesian Networks

9Dynamic Bayesian Networks

10Decision Theory Foundations

11Decision Graphs and Influence Diagrams

Chance, decision, and utility nodes Arc semantics and information flow Decision timing and information sets Policies and strategies Influence diagram syntax Asymmetric decision problems Value of information concepts Additive and multiplicative utilities Deterministic policy nodes Arc reversal operations Node reduction operations Strong vs weak influence diagrams Strategy graphs and policies Example influence diagram modeling Extensions to dynamic IDs

12Algorithms for Decision Evaluation

13Causality and Interventions with Bayesian Networks

14Model Validation, Diagnostics, and Explainability

15Tools, Implementation, and Applications

Courses/Bayesian Networks and Decision Graphs /Decision Graphs and Influence Diagrams

Decision Graphs and Influence Diagrams

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Representation of decisions, information, and utilities within graphical models.

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Influence diagram syntax

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