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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

12Algorithms for Decision Evaluation

Maximum expected utility (MEU) queries Variable elimination with decisions Backward induction for IDs Strong junction tree evaluation Decision circuit compilation Policy construction algorithms Value of perfect information Value of partial information Dominance testing and pruning Robust decision analysis Sensitivity analysis for utilities Interval probabilities and utilities Computational complexity results Heuristics for large IDs Case studies of ID evaluation

13Causality and Interventions with Bayesian Networks

14Model Validation, Diagnostics, and Explainability

15Tools, Implementation, and Applications

Courses/Bayesian Networks and Decision Graphs /Algorithms for Decision Evaluation

Algorithms for Decision Evaluation

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Computational methods to compute optimal policies and VOI within IDs and BNs.

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7 of 15

Value of perfect information

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