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

Search spaces and DAG equivalence CPDAGs and Markov equivalence Score functions: BIC and AIC Bayesian scores: BDeu and BGe Hill-climbing and tabu search Order-based search Constraint-based methods (PC)Hybrid methods (MMHC)Local score caching Statistical CI tests Dealing with latent variables Controlling false discoveries Sample complexity considerations Parallel and distributed search Human-in-the-loop refinement

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 /Structure Learning and Causal Discovery

Structure Learning and Causal Discovery

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Learning the network graph from data via scores, constraints, and hybrids.

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Sample complexity considerations

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