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

Variable elimination Elimination order heuristics Complexity and treewidth Belief propagation on trees Junction tree construction Triangulation strategies Building cliques and separators Clique potential initialization Message passing and calibration Computing marginals MAP and MPE queries Evidence incorporation Arithmetic circuits Knowledge compilation (d-DNNF)Caching and memoization

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 /Exact Inference in Bayesian Networks

Exact Inference in Bayesian Networks

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Algorithms that compute exact probabilities and MAP/MPE solutions when feasible.

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Building cliques and separators

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