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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

13Causality and Interventions with Bayesian Networks

14Model Validation, Diagnostics, and Explainability

Train, validation, and test splits Predictive log-likelihood and perplexity Cross-validation strategies Posterior predictive checks Residual analysis for BNs Parameter identifiability checks Structure stability via bootstrapping Sensitivity to priors and hyperparameters Marginal and local sensitivity analysis Conflict and surprise measures Debugging inconsistent evidence Explaining inferences to stakeholders Fairness and bias assessment Uncertainty communication Model risk management

15Tools, Implementation, and Applications

Courses/Bayesian Networks and Decision Graphs /Model Validation, Diagnostics, and Explainability

Model Validation, Diagnostics, and Explainability

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Assessing predictive performance, stability, and transparency of models.

Content

8 of 15

Sensitivity to priors and hyperparameters

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