Bayesian Networks and Decision Graphs

Chapters

1Foundations of Probability and Graphical Models

2Bayesian Networks: Concepts and Construction

Definition of Bayesian networks Structure and parameters Conditional probability tables (CPTs)Canonical CPDs: Noisy-OR Canonical CPDs: Noisy-AND Discrete vs continuous nodes Factor graphs vs Bayesian networks Moralization and triangulation basics BN semantics and assumptions Faithfulness and causal sufficiency Query types and evidence BN construction workflow Data and sample size needs Priors and smoothing choices Scalability considerations

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

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 /Bayesian Networks: Concepts and Construction

Bayesian Networks: Concepts and Construction

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Definition, semantics, and basic components of Bayesian networks with practical modeling steps.

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Conditional probability tables (CPTs)

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