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

Maximum likelihood for complete data Dirichlet-multinomial conjugacy Bayesian parameter estimation MAP estimation and smoothing Credible intervals for CPTs EM algorithm fundamentals Soft and hard EM Missing data mechanisms Parameter tying and sharing Regularization strategies Hierarchical priors Continuous CPDs with Gaussians Learning Noisy-OR parameters Uncertainty quantification Model averaging over parameters

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 /Parameter Learning for Bayesian Networks

Parameter Learning for Bayesian Networks

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Estimating CPDs from data using frequentist and Bayesian approaches with missingness.

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Hierarchical priors

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