Bayesian Networks and Decision Graphs

Chapters

1Foundations of Probability and Graphical Models

2Bayesian Networks: Concepts and Construction

3Conditional Independence and d-Separation

Paths, colliders, and non-colliders Blocking and active trails Explaining away Local, pairwise, and global Markov properties Markov blanket derivations Minimal I-maps Conditional independence lattices d-separation algorithms Separation in moralized graphs Context-specific independence CSI via decision trees CSI via algebraic constraints Faithfulness violations Testing conditional independence CI in finite samples

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

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 /Conditional Independence and d-Separation

Conditional Independence and d-Separation

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How graph structure encodes independence and how to reason about information flow.

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Separation in moralized graphs

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